The overriding goal of this paper is to present the most influential minimalist approaches1 to the resolution of the infinite regress in antecedent-contained deletion constructions. Therefore, the point of departure is the general description of the problem concerning the interpretation of sentences containing antecedent-contained deletion, which occurs at LF (Logical Form), when a null VP is filled with a copy of its antecedent creating at the same time an infinite regress. In the subsequent sections of this paper I strive to give an account of the attempts to resolve ACD by focusing on the theory of Vehicle Change developed by Fiengo and May (1993), the case-based approach proposed by Hornstein (1995) as well as the copy theory of movement by Fox (2002). In the last section of this paper I provide some Chomsky’s views (2004) on the direction in which the research on ACD constructions may proceed.

Sediments of two dam reservoirs in SE Poland, Zalew Zemborzycki (ZZ) and Brody Iłżeckie (BI) were studied. The sediments from both reservoirs were sampled in the transects perpendicular to the shoreline, at the river inflow and the frontal dam. The total concentration of Mn, Zn, Pb, Cd, Cu, Cr and Ni was determined by ICP-EAS method after the sample digestion in the mixture of concentrated HNO3 and HClO4 acids. The statistical analyses: value intervals, mean values, variation coefficient, the median and the skewed distribution were performed. To estimate differences between the means for transects, Tukey’s test was applied with least significant difference (LSD) determination. The maps of the metal spatial distribution were drawn and sediment quality according to the geochemical and ecotoxicological criteria evaluated. Differences between the reservoirs in terms of heavy metals concentration in bottom sediments, and regularities in their spatial distribution were found. In the ZZ sediments the concentration was at the level of geochemical background (Zn, Cr), slightly (Cd, Cu, Ni) or moderately (Pb) contaminated sediments. The metal concentration in the sediments of the BI was up to eight times higher as compared to the ZZ. Moreover, sediments from the BI reservoir showed a greater variability of metal concentration than those from ZZ, which resulted from the dredging operation performed in the part of the reservoir. Metal concentration in sediments of the dredged part was ca. 2–5 times lower than in the undredged one, which indicates that after the dredging operation, accumulation of these metals was slight. The concentrations of Zn, Pb and Cd from the undredged part of BI were at the level of contaminated sediments and exceeded the probable effects level (PEL). In the ZZ, the greatest accumulation of metals occurred in the upper part of the reservoir and at the frontal dam, and the lowest in the middle part of the reservoir. In BI, the lower outflow of water in this reservoir caused a lower metal concentration in the sediments at the frontal dam, as compared with the other sediments in the undredged part of the reservoir. The results indicate that in small and shallow reservoirs, areas of accumulation of heavy metals depend on such factors as a parent river current, reservoir depth, water waving, reservoir shape (narrowing, coves/bays), and type of water outflow.

A new method of creating constitutive model of masonry is reported in this work. The model is not an explicit orthotropic elastic-plastic one, but with an artificial neural network (ANN) giving an implicit constitutive function. It relates the new state of generalised stresses Σ ⁿ⁺¹ with the old state Σ ⁿ and with an increment of generalised strains ΔE (plane-stress conditions are assumed). The first step is to run a strain- controlled homogenisation, repeatedly, on a three-dimensional finite element model of a periodic cell, with elastic-plastic models (Drucker–Prager) of the components; thus a set of paths is created in (Σ, ΔE) space. From these paths, a set of patterns is formed to train the ANN. A description of how to prepare these data and a discussion on ANN training issues are presented. Finally, the procedure based on trained ANN is put into a finite-element code as a constitutive function. This enables the analysis of arbitrarily large masonry systems. The approach is verified by comparing the results of the developed model basing on ANN with a direct (single-scale) one, which showed acceptable accuracy.