Fifty-six sherds of fine ware from Locri Epizephiri were analysed by inductively coupled plasma optical emission spectroscopy (ICPOES) andjame emission spectroscopy (FES).The sherds had been excavated on the archaeological areas of Marasci Sud, Centocamere and San Cono, and represent local and imported products f r o m the seventh to the second century BC. The samples were dissolved by fusion with lithium metaborate and analysed f o r nine elements (Al, Fe, Ca, M g , Ti, Mn, Sr, Ba and Cr) by ICPOES, while N a and K were determined by FES. The analytical data were subjected to hierarchical agglomerative clustering and principal components analysis to classify the sherds into compositional groups. The results obtained indicated that Corinthian, Attic and Ionian products or imitations ma-v be classijied in a few separate groups, while most local products are found in a unique, large group.
One hundred and jifty-seven sherds of Campanian pottery were analysed by inductively coupled plasma optical emission spectroscopy (ICP-OES) and flame emission spectroscopy (FES). The sherds were found in the course of archaeological excavations carried out at the sites of Sibari, Cosenza, Crotone, Locri, Reggio and Oppido Mamertina in southern Italy (ancient Bruttiurn, today Calabria). Analyses were carried out on dissolved samples, determining 15 elements per sample (AI,
Although the C-based interpreter of Python is reasonably fast, implementations on the CLI or the JVM platforms offers some advantages in terms of robustness and interoperability. Unfortunately, because the CLI and JVM are primarily designed to execute statically typed, object-oriented languages, most dynamic language implementations cannot use the native bytecodes for common operations like method calls and exception handling; as a result, they are not able to take full advantage of the power offered by the CLI and JVM.We describe a different approach that attempts to preserve the flexibility of Python, while still allowing for efficient execution. This is achieved by limiting the use of the more dynamic features of Python to an initial, bootstrapping phase. This phase is used to construct a final RPython (Restricted Python) program that is actually executed. RPython is a proper subset of Python, is statically typed, and does not allow dynamic modification of class or method definitions; however, it can still take advantage of Python features such as mixins and first-class methods and classes. This paper presents an overview of RPython, including its design and its translation to both CLI and JVM bytecode. We show how the bootstrapping phase can be used to implement advanced features, like extensible classes and generative programming. We also discuss what work remains before RPython is truly ready for general use, and compare the performance of RPython with that of other approaches.
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