In 2015, a continuous 15.4 m snow/firn core was recovered from central South Georgia Island at ∼850 m a.s.l. All firn core samples were analyzed for major (Al, Ca, Mg, Na, K, Ti and Fe) and trace element concentrations (Sr, Cd, Cs, Ba, La, Ce, Pr, Pb, Bi, U, As, Li, S, V, Cr, Mn, Co, Cu and Zn) and stable water isotopes. The chemical and isotopic signal is well preserved in the top 6.2 m of the core. Below this depth, down to the bottom of the core, signal dampening is observed in the majority of the elemental species making it difficult to distinguish a seasonal signal. Thirteen elements (As, Bi, Ca, Cd, Cu, K, Li, Mg, Na, Pb, S, Sr and Zn) have crustal enrichment factor values higher than 10 suggesting sources in addition to those found naturally in the crust. While this study shows that 850 m a.s.l. is not high enough to preserve a record including recent years, higher-elevation (>1250 m a.s.l.) glaciers may be likely candidates for ice core drilling to recover better-preserved, continuous, recent to past glaciochemical records.

Perhaps the most common criterion for partitioning a data set is the minimization of the within-cluster sums of squared deviation from cluster centroids. Although optimal solution procedures for within-cluster sums of squares (WCSS) partitioning are computationally feasible for small data sets, heuristic procedures are required for most practical applications in the behavioral sciences. We compared the performances of nine prominent heuristic procedures for WCSS partitioning across 324 simulated data sets representative of a broad spectrum of test conditions. Performance comparisons focused on both percentage deviation from the “best-found” WCSS values, as well as recovery of true cluster structure. A real-coded genetic algorithm and variable neighborhood search heuristic were the most effective methods; however, a straightforward two-stage heuristic algorithm, HK-means, also yielded exceptional performance. A follow-up experiment using 13 empirical data sets from the clustering literature generally supported the results of the experiment using simulated data. Our findings have important implications for behavioral science researchers, whose theoretical conclusions could be adversely affected by poor algorithmic performances.

To date, most methods for direct blockmodeling of social network data have focused on the optimization of a single objective function. However, there are a variety of social network applications where it is advantageous to consider two or more objectives simultaneously. These applications can broadly be placed into two categories: (1) simultaneous optimization of multiple criteria for fitting a blockmodel based on a single network matrix and (2) simultaneous optimization of multiple criteria for fitting a blockmodel based on two or more network matrices, where the matrices being fit can take the form of multiple indicators for an underlying relationship, or multiple matrices for a set of objects measured at two or more different points in time. A multiobjective tabu search procedure is proposed for estimating the set of Pareto efficient blockmodels. This procedure is used in three examples that demonstrate possible applications of the multiobjective blockmodeling paradigm.

A wide variety of paired comparison, triple comparison, and ranking experiments may be viewed as generalized linear models. These include paired comparison models based on both the Bradley-Terry and Thurstone-Mosteller approaches, as well as extensions of these models that allow for ties, order of presentation effects, and the presence of covariates. Moreover, the triple comparison model of Pendergrass and Bradley, as well as models for complete rankings of more than three items, can also be represented as generalized linear models. All such models can be easily fit by maximum likelihood, using the widely available GLIM computer package. Additionally, GLIM enables the computation of likelihood ratio statistics for testing many hypotheses of interest. Examples are presented that cover a variety of cases, along with their implementation on GLIM.

The monotone homogeneity model (MHM—also known as the unidimensional monotone latent variable model) is a nonparametric IRT formulation that provides the underpinning for partitioning a collection of dichotomous items to form scales. Ellis (Psychometrika 79:303–316, 2014, doi:10.1007/s11336-013-9341-5) has recently derived inequalities that are implied by the MHM, yet require only the bivariate (inter-item) correlations. In this paper, we incorporate these inequalities within a mathematical programming formulation for partitioning a set of dichotomous scale items. The objective criterion of the partitioning model is to produce clusters of maximum cardinality. The formulation is a binary integer linear program that can be solved exactly using commercial mathematical programming software. However, we have also developed a standalone branch-and-bound algorithm that produces globally optimal solutions. Simulation results and a numerical example are provided to demonstrate the proposed method.

Two-mode binary data matrices arise in a variety of social network contexts, such as the attendance or non-attendance of individuals at events, the participation or lack of participation of groups in projects, and the votes of judges on cases. A popular method for analyzing such data is two-mode blockmodeling based on structural equivalence, where the goal is to identify partitions for the row and column objects such that the clusters of the row and column objects form blocks that are either complete (all 1s) or null (all 0s) to the greatest extent possible. Multiple restarts of an object relocation heuristic that seeks to minimize the number of inconsistencies (i.e., 1s in null blocks and 0s in complete blocks) with ideal block structure is the predominant approach for tackling this problem. As an alternative, we propose a fast and effective implementation of tabu search. Computational comparisons across a set of 48 large network matrices revealed that the new tabu-search heuristic always provided objective function values that were better than those of the relocation heuristic when the two methods were constrained to the same amount of computation time.

In May 2018, the Hague Conference on Private International Law (‘HCCH’) produced a draft convention for the recognition and enforcement of foreign judgments. A Diplomatic Session of the HCCH is expected to take place in 2019 at which this draft ‘Judgments Convention’ will be presented. If a multilateral convention emerges from the Diplomatic Session, Australia is likely to be an early adopter: the Commonwealth Attorney-General’s Department conducted a public consultation on the draft Judgments Convention in 2018. Against that background, this article considers the impact of implementation of the Judgments Convention in Australia. It is argued that domestic legislation that emerges from the Judgments Convention will deliver an overdue refurbishment of the Australian law relating to the recognition and enforcement of foreign judgments. Australia’s adoption of the Judgments Convention ought to be welcomed.

The selection of a subset of variables from a pool of candidates is an important problem in several areas of multivariate statistics. Within the context of principal component analysis (PCA), a number of authors have argued that subset selection is crucial for identifying those variables that are required for correct interpretation of the components. In this paper, we adapt the variable neighborhood search (VNS) paradigm to develop two heuristics for variable selection in PCA. The performances of these heuristics were compared to those obtained by a branch-and-bound algorithm, as well as forward stepwise, backward stepwise, and tabu search heuristics. In the first experiment, which considered candidate pools of 18 to 30 variables, the VNS heuristics matched the optimal subset obtained by the branch-and-bound algorithm more frequently than their competitors. In the second experiment, which considered candidate pools of 54 to 90 variables, the VNS heuristics provided better solutions than their competitors for a large percentage of the test problems. An application to a real-world data set is provided to demonstrate the importance of variable selection in the context of PCA.

Eight different variable selection techniques for model-based and non-model-based clustering are evaluated across a wide range of cluster structures. It is shown that several methods have difficulties when non-informative variables (i.e., random noise) are included in the model. Furthermore, the distribution of the random noise greatly impacts the performance of nearly all of the variable selection procedures. Overall, a variable selection technique based on a variance-to-range weighting procedure coupled with the largest decreases in within-cluster sums of squares error performed the best. On the other hand, variable selection methods used in conjunction with finite mixture models performed the worst.

Common outputs of software programs for network estimation include association matrices containing the edge weights between pairs of symptoms and a plot of the symptom network. Although such outputs are useful, it is sometimes difficult to ascertain structural relationships among symptoms from these types of output alone. We propose that matrix permutation provides a simple, yet effective, approach for clarifying the order relationships among the symptoms based on the edge weights of the network. For directed symptom networks, we use a permutation criterion that has classic applications in electrical circuit theory and economics. This criterion can be used to place symptoms that strongly predict other symptoms at the beginning of the ordering, and symptoms that are strongly predicted by other symptoms at the end. For undirected symptom networks, we recommend a permutation criterion that is based on location theory in the field of operations research. When using this criterion, symptoms with many strong ties tend to be placed centrally in the ordering, whereas weakly-tied symptoms are placed at the ends. The permutation optimization problems are solved using dynamic programming. We also make use of branch-search algorithms for extracting maximum cardinality subsets of symptoms that have perfect structure with respect to a selected criterion. Software for implementing the dynamic programming algorithms is available in MATLAB and R. Two networks from the literature are used to demonstrate the matrix permutation algorithms.

The Foreign States Immunities Act 1985 (Cth) provides that foreign states are immune to the jurisdiction of Australian courts, and that their property is immune from execution. Those immunities are subject to important ‘commercial exceptions’. First, foreign states are not immune in Australian proceedings insofar as they concern a ‘commercial transaction’. Second, foreign states are not immune from execution in respect of ‘commercial property’. The distinction between the commercial and the non-commercial may be difficult to pin down. With reference to recent case law, including the High Court's decision in Firebird Global Master Fund II Ltd v Republic of Nauru (2015) 258 CLR 31, this article aims to articulate the scope of the commercial exceptions. It is argued that the scope of the commercial transaction exception is uncertain, and depends on courts’ approach to the task of characterisation. It is also argued that the commercial property exception is undesirably narrow, and will present a recurring impediment to the vindication of private rights.

A version of the discrete proportional hazards model is developed for psychometrical applications. In such applications, a primary covariate that influences failure times is a latent variable representing a psychological construct. The Metropolis-Hastings algorithm is studied as a method for performing marginal likelihood inference on the item parameters. The model is illustrated with a real data example that relates the age at which teenagers first experience various substances to the latent ability to avoid the onset of such behaviors.

The gut microbiome is impacted by certain types of dietary fibre. However, the type, duration and dose needed to elicit gut microbial changes and whether these changes also influence microbial metabolites remain unclear. This study investigated the effects of supplementing healthy participants with two types of non-digestible carbohydrates (resistant starch (RS) and polydextrose (PD)) on the stool microbiota and microbial metabolite concentrations in plasma, stool and urine, as secondary outcomes in the Dietary Intervention Stem Cells and Colorectal Cancer (DISC) Study. The DISC study was a double-blind, randomised controlled trial that supplemented healthy participants with RS and/or PD or placebo for 50 d in a 2 × 2 factorial design. DNA was extracted from stool samples collected pre- and post-intervention, and V4 16S rRNA gene sequencing was used to profile the gut microbiota. Metabolite concentrations were measured in stool, plasma and urine by high-performance liquid chromatography. A total of fifty-eight participants with paired samples available were included. After 50 d, no effects of RS or PD were detected on composition of the gut microbiota diversity (alpha- and beta-diversity), on genus relative abundance or on metabolite concentrations. However, Drichlet’s multinomial mixture clustering-based approach suggests that some participants changed microbial enterotype post-intervention. The gut microbiota and fecal, plasma and urinary microbial metabolites were stable in response to a 50-d fibre intervention in middle-aged adults. Larger and longer studies, including those which explore the effects of specific fibre sub-types, may be required to determine the relationships between fibre intake, the gut microbiome and host health.

The authors report on ancient DNA data from two human skeletons buried within the chancel of the 1608–1616 church at the North American colonial settlement of Jamestown, Virginia. Available archaeological, osteological and documentary evidence suggest that these individuals are Sir Ferdinando Wenman and Captain William West, kinsmen of the colony's first Governor, Thomas West, Third Baron De La Warr. Genomic analyses of the skeletons identify unexpected maternal relatedness as both carried the mitochondrial haplogroup H10e. In this unusual case, aDNA prompted further historical research that led to the discovery of illegitimacy in the West family, an aspect of identity omitted, likely intentionally, from genealogical records.

The hypothesis that chemical remanent magnetization (CRM) in argillaceous rocks may be due to release of Fe during smectite illitization has been tested by study of spatial and temporal relationships of CRM acquisition, smectite illitization, and organic-matter maturation to deformation in the Montana Disturbed Belt. New K-Ar ages and stacking order and percentages of illite layers in illite-smectite (I-S) are consistent with conclusions from previous studies that smectite illitization of bentonites in Subbelts I and II of the Disturbed Belt was produced by thrust-sheet burial resulting from the Laramide Orogeny. Internally concordant, early Paleogene, K-Ar age values (55–57 Ma) were obtained from clay subfractions of thick bentonites which were significantly different in terms of their ages (i.e. Jurassic Ellis Formation and late Cretaceous Marias River Shale), further supporting a model of smectite illitization as a result of the Laramide Orogeny. Internally concordant K-Ar ages were found also for clay sub-fractions from a thick bentonite at Pishkun Canal (54 Ma) and from an undeformed bentonite near Vaughn on the Sweetgrass Arch (48 Ma). In Subbelts I and II, a greater degree of smectite illitization corresponds to increased thermal maturation, increased natural remanent magnetization intensity, and increased deformation (dip of beds). A dissolution-precipitation model over a short duration is proposed for the formation of illite layers in Subbelts I and II. A characteristic remanent magnetization was developed before or just after folding began in the early Paleogene. More smectite-rich I-S, low thermal maturity, and the absence of a CRM were noted in one outcrop of an undeformed rock on the Sweetgrass Arch. Strontium isotope data allow for the possibility that internal or externally derived fluids may have influenced illitization, but the K-Ar age values suggest that illitization was probably in response to conductive heating after the overthrusting had occurred. The differences in K-Ar dates among the bentonites studied herein may be due to differences in the timing of peak temperature related to differences in distance below the overthrust slab, in rates of burial and exhumation, and in initial temperature.