The Effect of Raw Material on the Identification of Knapping Skill: a Case Study from Olduvai Gorge, Tanzania

The identification of Oldowan hominin knapping skill levels has been a focus of numerous studies, with apparent variation in technical abilities identified between a number of Early Stone Age archaeological sites. Raw material variability, however, can play a significant role in the outcomes of knapping events as well as in the accuracy of analysis. Implications of such variability are yet to be fully understood. Here we present an experimental study to assess the effects that varying raw materials have on the identification of technological attributes typically associated with varying skill levels and whether it is possible to identify knapper skill levels across multiple raw materials. Variation was tested between raw materials from Olduvai Gorge across and between skill levels. The results suggest that knapping skill levels manifest differently in the material record across raw materials. In addition, we suggest that raw material has a significant effect on identifying knapper skill variation. This has implications for future research concerned with identifying knapper skill within and between early assemblages of differing raw materials.

Uma perspetiva diacrónica da ocupação do concheiro do Cabeço da Amoreira (Muge, Portugal) a partir da tecnologia lítica

This study presents preliminary results from a technological analysis of lithic artefacts from the Mesolithic shellmidden of Cabeço da Amoreira (Muge, Portugal). The main goal was to understand the technological and raw material variability within the two main excavation areas of the site, in order to characterize the different occupation moments. A typological and attribute approach was used in the analysis. The results suggest a clear distinction of the lithic assemblages, associated with the sedimentary differences identified in the composition of the several layers. This separation can be found mostly in the frequencies of raw materials, cores and retouched tools.

Handling Constraints and Raw Material Variability in Rotomolding through Data-Driven Model Predictive Control

This work addresses the problems of uniquely specifying and robustly achieving user-specified product quality in a complex industrial batch process, which has been demonstrated using a lab-scale uni-axial rotational molding process. In particular, a data-driven modeling and control framework is developed that is able to reject raw material variation and achieve product quality which is specified through constraints on quality variables. To this end, a subspace state-space model of the rotational molding process is first identified from historical data generated in the lab. This dynamic model predicts the evolution of the internal mold temperature for a given set of input move trajectory (heater and compressed air profiles). Further, this dynamic model is augmented with a linear least-squares based quality model, which relates its terminal (states) prediction with key quality variables. For the lab-scale process, the chosen quality variables are sinkhole area, ultrasonic spectra amplitude, impact energy and shear viscosity. The complete model is then deployed within a model-based control scheme that facilitates specifying on-spec products via limits on the quality variables. Further, this framework is demonstrated to be capable of rejecting raw material variability to achieve the desired specifications. To replicate raw material variability observed in practice, in this work, the raw material is obtained by blending the matrix resin with a resin of slightly different viscosity at varying weight fractions. Results obtained from experimental studies demonstrate the capability of the proposed model predictive control (MPC) in meeting process specifications and rejecting raw material variability.