(4-Hydr­oxy-3-nitro­benz­yl)methyl­ammonium chloride

The title compound, C8H11N2O3 +·Cl−, was synthesized as an inter­mediate in the development of a new sugar sensor. The structure displays N—H⋯Cl and O—H⋯O hydrogen bonding, as well as weak O—H⋯Cl inter­actions and π–π stacking (3.298 Å). There are two formula units in the asymmetric unit

Chlorido{N-[2-(diphenyl­phosphan­yl)benz­yl]-1-(pyridin-2-yl)methanamine-κP}gold(I)

In the title compound, [AuCl(C25H23N2P)], the AuI atom is in a typical almost linear coordination environment defined by phosphane P and Cl atoms [bond angle = 175.48 (4)°]. Helical supra­molecular chains along the b axis and mediated by N—H⋯Cl hydrogen bonds feature in the crystal packing

8-(Biphenyl-4-yl)-8-hydroxy­penta­cyclo­[5.4.0.02,6.03,10.05,9]undecan-11-one ethyl­ene ketal

The title compound, C25H24O3, synthesized as a potential chiral catalyst, exhibits a range of C—C bond lengths in the penta­cyclo­undecane cage between 1.5144 (18) and 1.5856 (16) Å. The two benzene rings are not planar with respect to each other, but rather are twisted at a torsion angle of 34.67 (17)°. The mol­ecule has an intra­molecular O—H⋯O inter­action and participates in two C—H⋯O inter­molecular inter­actions to form a one-dimensional chain

Maritime Navigation: Characterizing Collaboration in a High-Speed Craft Navigation Activity

acceptedVersio

Di-μ-chlorido-bis­{[2-({[2-(2-pyrid­yl)eth­yl](2-pyridylmeth­yl)amino}meth­yl)phenol]zinc(II)} bis­(perchlorate) dihydrate

The title compound, [Zn2Cl2(C20H21N3O)2](ClO4)2·2H2O, consists of a dinuclear ZnII cationic complex, two disordered perchlorate anions and two water mol­ecules as solvate. The [Zn2(μ-Cl)2(HL)2]2+ cation [HL is 2-({[2-(2-pyrid­yl)eth­yl](2-pyridylmeth­yl)amino}meth­yl)phenol] has a centrosymmetric structure with the ZnII ions in a distorted octa­hedral environment surrounded by an N3OCl2 donor set. HL acts as a tetra­dentate ligand through three N atoms from one amine group and two pyridyl arms and one O atom from the phenolic arm. The three N-donor sites of the HL ligand are arranged in meridional fashion, with the pyridine rings coordinated in trans positions with respect to each other. Consequently, the amine and phenol groups are trans to the asymmetric di-μ-chlorido exogenous bridges. A polymeric chain is formed along [010] by C(12)R 4 2(8) inter­molecular hydrogen bonding. The perchlorate anion is disordered and was modelled by two sites in a 0.345 (18):0.655 (18) ratio. Water–perchlorate O—H⋯O inter­actions form cyclic structures, while phenol–water O—H⋯O inter­actions generate an infinite chain. In addition, weak inter­molecular C—H⋯π(Ph) inter­actions between pyridine donor and phenol acceptor groups of neighboring cations build a two-dimensional polymeric structure parallel to (110)

A consideration of the challenges involved in supervising international masters students

This paper explores the challenges facing supervisors of international postgraduate students at the dissertation stage of the masters programme. The central problems of time pressure, language difficulties, a lack of critical analysis and a prevalence of personal problems among international students are discussed. This paper makes recommendations for the improvement of language and critical thinking skills, and questions the future policy of language requirements at HE for international Masters students

Crystal chemistry of type paulkerrite and establishment of the paulkerrite group nomenclature

A single-crystal structure determination and refinement has been conducted for the type specimen of paulkerrite. The structure analysis showed that the mineral has monoclinic symmetry, space group P21/c, not orthorhombic, Pbca, as originally reported. The unit-cell parameters are a=10.569(2), b=20.590(4), c=12.413(2) Å, and β=90.33(3)∘. The results from the structure refinement were combined with electron microprobe analyses to establish the empirical structural formula A1[(H2O)0.98K0.02]Σ1.00 A2K1.00 M1(Mg1.02Mn0.982+)Σ2.00 M2(Fe1.203+Ti0.544+Al0.24Mg0.02)Σ2.00 M3(Ti0.744+ Fe0.263+)Σ1.00 (PO4)4.02 X[O1.21F0.47(OH)0.32]Σ2.00(H2O)10 ⋅ 3.95H2O, which leads to the end-member formula (H2O)KMg2Fe2Ti(PO4)4(OF)(H2O)10 ⋅ 4H2O. A proposal for a paulkerrite group, comprising orthorhombic members benyacarite, mantiennéite, pleysteinite, and hochleitnerite and monoclinic members paulkerrite and rewitzerite, has been approved by the International Mineralogical Association's Commission for New Minerals, Nomenclature and Classification. The general formulae are A2M12M22M3(PO4)4X2(H2O)10 ⋅ 4H2O and A1A2M12M22M3(PO4)4X2(H2O)10 ⋅ 4H2O for orthorhombic and monoclinic species, respectively, where A= K, H2O, □ (= vacancy); M1 = Mn2+, Mg, Fe2+, Zn (rarely Fe3+); M2 and M3 = Fe3+, Al, Ti4+ (and very rarely Mg); X= O, OH, F. In monoclinic species, K and H2O show an ordering at the A1 and A2 sites, whereas O, (OH), and F show a disordering over the two non-equivalent X1 and X2 sites, which were hence merged as X2 in the general formula. In both monoclinic and orthorhombic species, a high degree of mixing of Fe3+, Al, and Ti occurs at the M2 and M3 sites of paulkerrite group members, making it difficult to get unambiguous end-member formulae from the structural determination of the constituents at individual sites. To deal with this problem an approach has been used that involves merging the compositions at the M2 and M3 sites and applying the site-total-charge method. The merged-site approach allows end-member formulae to be obtained directly from the chemical analysis without the need to conduct crystal-structure refinements to obtain the individual site species.</p

Regularizing Portfolio Optimization

The optimization of large portfolios displays an inherent instability to estimation error. This poses a fundamental problem, because solutions that are not stable under sample fluctuations may look optimal for a given sample, but are, in effect, very far from optimal with respect to the average risk. In this paper, we approach the problem from the point of view of statistical learning theory. The occurrence of the instability is intimately related to over-fitting which can be avoided using known regularization methods. We show how regularized portfolio optimization with the expected shortfall as a risk measure is related to support vector regression. The budget constraint dictates a modification. We present the resulting optimization problem and discuss the solution. The L2 norm of the weight vector is used as a regularizer, which corresponds to a diversification "pressure". This means that diversification, besides counteracting downward fluctuations in some assets by upward fluctuations in others, is also crucial because it improves the stability of the solution. The approach we provide here allows for the simultaneous treatment of optimization and diversification in one framework that enables the investor to trade-off between the two, depending on the size of the available data set