Correction to: Direct and Residual Effects of Water Deficit Stress,Different Sources of Silicon and Plant-Growth Promoting Bacteria on Silicon Fractions in the Soil

Silicon - A Correction to this paper has been published: https://doi.org/10.1007/s12633-021-01161-w     

A novel single-step method for fabrication of dense surface porous aluminum

A new manufacturing process was developed for functionally graded porous Al foams. Morphological variations were monitored by scanning electron microscopy. Mechanical properties were measured by compressive tests. Results indicated that the maximum temperature of 773 K and minimum interior porosity of 60% are the best conditions. Scanning electron microscope (SEM) images show a strong connection between Al particles with high necking radius. Micro-hot molding of Al particles in the space between salt particles is probably responsible for porosity formation.     

Intrinsic relationship between energy consumption,pressure, and leakage in water distribution systems

The basic implications of changes in delivery pressure on system energy use and cost, on leakage, excess pressure, and environmental impacts are explored. An analytical expression is first developed to characterize the primary relationships between energy use, leakage and pressure for a simple pipe segment. Then, two more realistic case studies, based on varying versions of the Anytown network, are considered. The results indicate that energy use responds more to changes in the delivery pressure in systems with higher leakage rates while reductions in pressures curtail energy use and leakage more dramatically in low resistance systems. Perhaps more surprisingly, systems with more effective water storage and thus uniform pressures tend to have higher leakage rates, greater energy usage, and higher GHG emissions relative to systems relying on direct pumping. The generalization that results from these studies is perhaps predictable but has profound implications: the higher the delivery pressure the greater will likely be the amount of water wasted and energy dissipated.     

Challenges in atherosclerotic plaque characterization with intravascular ultrasound (IVUS): from data collection to classification

In vivo plaque characterization is an important research field in interventional cardiology. We will study the realistic challenges to this goal by deploying 40 MHz single-element, mechanically rotating transducers. The intrinsic variability among the transducers' spectral parameters as well as tissue signals will be demonstrated. Subsequently, we will show that global data normalization is not suited for data calibration, due to the aforementioned variations as well as the stringent characteristics of spectral features. We will describe the sensitivity of an existing feature extraction algorithm based on eight spectral signatures (integrated backscatter coefficient, slope, midband-fit (MBF), intercept, and maximum and minimum powers and their relative frequencies) to a number of factors, such as the window size and order of the autoregressive (AR) model. It will be further demonstrated that the variations in the transducer's spectral parameters (i.e., center frequency and bandwidth) cause inconsistencies among extracted features. In this paper, two fundamental questions are addressed: 1) what is the best reliable way to extract the most informative features? and 2) which classification algorithm is the most appropriate for this problem? We will present a full-spectrum analysis as an alternative to the eight-feature approach. For the first time, different classification algorithms, such as k-nearest neighbors (k-NN) and linear Fisher, will be employed and their performances quantified. Finally, we will explore the reliability of the training dataset and the complexity of the recognition algorithm and illustrate that these two aspects can highly impact the accuracy of the end result, which has not been considered until now.     

Compressive properties of Cu with different grain sizes: sub-micron to nanometer realm

High-quality ultra-fine grained (ufg) and nanocrystalline (nc) bulk Cu samples of proper sizes reliable for mechanical testing, with grain sizes (d) ranging from 720 down to 22 nm were prepared by means of room temperature ball-milling and consolidation processes. The specimens were subjected to compressive loading at the quasi-static strain rate of 10⁻⁴ s⁻¹ to large strains (ε = 50%). The specimens prepared from the 10-h-milled powder (d = 32 nm) were tested at a wide range of strain rates (10⁻⁴ to 1,860 s⁻¹), and the strain rate sensitivity (SRS) of the material was determined as a function of strain. The strength and work-hardening behavior were dramatically influenced by change in the grain size; the strength approached ∼900 MPa for the 30-h-milled Cu (d = 22 nm) at the strain level of ∼50%. The SRS increased several fold as the grain size was reduced to 32 nm. Further, the results obtained in this study were compared with those of other investigators on ufg and nc Cu, to gain insights into the effect of different processing routes on the investigated material properties.     

Dynamic scheduling techniques for heterogeneous computing systems

There has been a recent increase of interest in heterogeneous computing systems, due partly to the fact that a single parallel architecture may not be adequate for exploiting all of a program's available parallelism. In some cases, heterogeneous systems have been shown to produce higher performance for lower cost than a single large machine. However, there has been only limited work on developing techniques and frameworks for partitioning and scheduling applications across the components of a heterogeneous system. In this paper we propose a general model for describing and evaluating heterogeneous systems that considers the degree of uniformity in the processing elements and the communication channels as a measure of the heterogeneity in the system. We also propose a class of dynamic scheduling algorithms for a heterogeneous computing system interconnected with an arbitrary communication network. These algorithms execute a novel optimization technique to dynamically compute schedules based on the potentially non-uniform computation and communication costs on the processors of a heterogeneous system. A unique aspect of these algorithms is that they easily adapt to different task granularities, to dynamically varying processor and system loads, and to systems with varying degrees of heterogeneity. Our simulations are designed to facilitate the evaluation of different scheduling algorithms under varying degrees of heterogeneity. The results show improved performance for our algorithms compared to the performance resulting from existing scheduling techniques.