Plasticity performance of Al0.5CoCrCuFeNi high-entropy alloys under nanoindentation

The statistical and dynamic behaviors of the displacement-load curves of a high-entropy alloy Al0.3CoCrCuFeNi were analyzed for the nanoindentation performed at two temperatures. Critical behavior of serrations at room temperature and chaotic flows at 200 degrees C were detected. These results are attributed to the interaction among a large number of slip hands. For the nanoindentation at room temperature recurrent partial events between slip hands introduce a hierarchy of length scales leading to a critical state. For the nanoindentation at 200 degrees C there is no spatial interference between two slip hands which is corresponding to the evolution of separated trajectory of chaotic behavior

Plant-level dynamics and aggregate productivity growth in the Turkish meat-processing industry: Evidence from longitudinal data

The authors examine how plant-level dynamics contribute to aggregate productivity growth in the Turkish meat-processing industry. An aggregate productivity decomposition approach that utilizes plant-level longitudinal data is used to achieve this goal. Their results are consistent with the empirical literature in the sense that productivity enhancement within existing plants is the main source of aggregate productivity growth in this sector. However, their analysis generally suggests that plants that exit the meat-processing industry tend to be more productive than entering plants, especially in the posteconomic crisis period studied. Even though the latter insight is not consistent with the existing empirical literature, they show that these results tend to support R. Caballero and M. Hammour's (2000) contention that institutional factors such as industry structure (i.e., mature vs. infant industry) and economic crisis conditions (i.e., pre- vs. postcrisis periods) affect the nature of plant dynamics' contributions to aggregate productivity growth. Overall, the study's results reveal that industry-specific institutional factors must be taken into consideration when shaping policies aimed to improve and sustain aggregate productivity growth. [JEL Classifications: D24, L25, O12]. © 2006 Wiley Periodicals, Inc. Agribusiness 22: 91-107, 2006.

Generation of “Virtual” Control Groups for Single Arm Prostate Cancer Adjuvant Trials

<div><p>It is difficult to construct a control group for trials of adjuvant therapy (Rx) of prostate cancer after radical prostatectomy (RP) due to ethical issues and patient acceptance. We utilized 8 curve-fitting models to estimate the time to 60%, 65%, … 95% chance of progression free survival (PFS) based on the data derived from Kattan post-RP nomogram. The 8 models were systematically applied to a training set of 153 post-RP cases without adjuvant Rx to develop 8 subsets of cases (reference case sets) whose observed PFS times were most accurately predicted by each model. To prepare a virtual control group for a single-arm adjuvant Rx trial, we first select the optimal model for the trial cases based on the minimum weighted Euclidean distance between the trial case set and the reference case set in terms of clinical features, and then compare the virtual PFS times calculated by the optimum model with the observed PFSs of the trial cases by the logrank test. The method was validated using an independent dataset of 155 post-RP patients without adjuvant Rx. We then applied the method to patients on a Phase II trial of adjuvant chemo-hormonal Rx post RP, which indicated that the adjuvant Rx is highly effective in prolonging PFS after RP in patients at high risk for prostate cancer recurrence. The method can accurately generate control groups for single-arm, post-RP adjuvant Rx trials for prostate cancer, facilitating development of new therapeutic strategies.</p></div