Structural characterization of the Fddd phase in a diblock copolymer thin film by electron microtomography

A 3-dimensional Fddd network structure of a polystyrene-block-polyisoprene (PS-b-PI) diblock copolymer (M(n) = 31 500, f(PI) = 0.645) was observed for the first time in real space by transmission electron microtomography (TEMT). In a 650 nm thick film of the PS-b-PI thin film on a silicon wafer, the Fddd phase was developed after annealing at 215 degrees C for 24 h. The single network structure consists of the connected tripodal units of minor PS block domains. The {111}(Fddd) plane, the densest plane of the minor PS phase, was found to orient parallel to the film plane. The transitional structure from the wetting layer at the free surface to the internal {111}(Fddd) plane via a perforated layer structure was also observed.X111313sciescopu

An Energy-Efficient Deep Neural Network Accelerator Design

This paper introduces an energy-efficient design method for Deep Neural Network (DNN) accelerator. Although GPU is widely used for DNN acceleration, its huge power consumption limits practical usage on mobile devices. Recent DNN accelerators are dedicated to high energy-efficiency to realize real-time DNN acceleration with low power consumption. But a hardware-oriented algorithm is essential for realistic implementation. Therefore, various techniques of network compression are applied with the DNN accelerators that utilize several schemes to reduce computational complexity in trade of accuracy loss. This work studies the optimization schemes and presents a DNN accelerator architecture by hardware-software co-optimization

Use of coal bottom ash for the production of sodium silicate solution in metakaolin-based geopolymers concerning environmental load reduction

This research proposes applying coal bottom ash (CBA) to metakaolin-based geopolymers. The conventional CBA mixing method in geopolymers degrades the compressive strength of the geopolymers mainly because of the low reactivity of CBA. To minimize the strength degradation and maximize the available CBA content in geo-polymers, the CBA was treated with an alkaline solution and then mixed with metakaolin. A sodium hydroxide solution with 10 and 12 M concentrations, a water-to-solid ratio of 0.50, and curing at ambient temperature were used for all geopolymer composites. If replaced with CBA up to 15% of the total weight of the geopolymer, the proposed method can not only yield a higher compressive strength than the conventional mixing method by providing external silicon from the CBA, but it can also show compressive strength similar to a typical geo-polymer without CBA. The properties observed by XRD, FT-IR, SEM/EDS, and MIP disclose the reasons for the proposed method accommodating a larger amount of CBA

A Ternary Neural Network Computing-in-Memory Processor With 16T1C Bitcell Architecture

A highly energy-efficient Computing-in-Memory (CIM) processor for Ternary Neural Network (TNN) acceleration is proposed in this brief. Previous CIM processors for multi-bit precision neural networks showed low energy efficiency and throughput. Lightweight binary neural networks were accelerated with CIM processors for high energy efficiency but showed poor inference accuracy. In addition, most previous works suffered from poor linearity of analog computing and energy-consuming analog-to-digital conversion. To resolve the issues, we propose a Ternary-CIM (T-CIM) processor with 16T1C ternary bitcell for good linearity with the compact area and a charge-based partial sum adder circuit to remove analog-to-digital conversion that consumes a large portion of the system energy. Furthermore, flexible data mapping enables execution of the whole convolution layers with smaller bitcell memory capacity. Designed with 65 nm CMOS technology, the proposed T-CIM achieves 1,316 GOPS of peak performance and 823 TOPS/W of energy efficiency

Removal of Phosphorus by Ferric Ion-Rich Solutions Prepared Using Various Fe(III)-Containing Minerals

Various biological, chemical, and physical technologies have been studied to effectively remove total phosphorus (T-P) from wastewater. Among them, some mineral suspensions and cations in the aqueous phase have shown great potential for promoting phosphorus removal via chemical precipitation. Herein, we investigated the efficiency of T-P removal using various chemical-based cations (Fe2+, Fe3+, Mg2+, and Al3+); ferric ions (Fe3+) showed the highest T-P-removal efficiency (33.1%), regardless of the type of anion (Cl−, NO3−, and SO42−). To prepare natural Fe3+-rich solutions, three different Fe(III)-rich minerals (hematite, lepidocrocite, and magnetite) were treated with various HCl concentrations to maximize the dissolved Fe3+ amounts. Lepidocrocite in 2 N HCl showed the most effective Fe3+-leaching ability (L-Fe dissolved solution). Almost no significant difference in Fe3+ leaching was observed between HCl and H2SO4, whereas lepidocrocite-2 N H2SO4 showed the highest T-P-removal ability (91.5%), with the formation of amorphous Fe(III)-P precipitates. The L-Fe dissolved solution exhibited a higher T-P-removal efficiency than polyammonium chloride under real wastewater conditions. Our results can provide fundamental knowledge about the effect of cations on T-P removal in wastewater treatment and the feasibility of using the Fe3+ leaching solution prepared from Fe(III)-containing minerals for efficient T-P removal via chemical precipitation

A Ternary Neural Network Computing-in-Memory Processor with 16T1C Bitcell Architecture

A highly energy-efficient Computing-in-Memory (CIM) processor for Ternary Neural Network (TNN) acceleration is proposed in this paper. Previous CIM processors for multi-bit precision neural networks showed low energy efficiency and throughput. Lightweight binary neural networks were accelerated with CIM processors for high energy efficiency but showed poor inference accuracy. In addition, most previous works suffered from poor linearity of analog computing and energy-consuming analog-to-digital conversion. To resolve the issues, we propose a Ternary-CIM (T-CIM) processor with 16T1C ternary bitcell for good linearity with compact area and a charge-based partial sum adder circuit to remove analog-to-digital conversion that consumes a large portion of the system energy. Furthermore, configurable data mapping enables execution of the whole convolution layers with smaller bitcell memory capacity. Designed with 65 nm CMOS technology, the proposed T-CIM achieves 1,316 GOPS of peak performance and 823 TOPS/W of energy efficiency

Characterization of Cold-Tolerant Saccharomyces cerevisiae Cheongdo Using Phenotype Microarray

The cold-tolerant yeast Saccharomyces cerevisiae is industrially useful for lager fermentation, high-quality wine, and frozen dough production. S. cerevisiae Cheongdo is a recent isolate from frozen peach samples which has a good fermentation performance at low temperatures and desirable flavor profiles. Here, phenotype microarray was used to investigate industrial potentials of S. cerevisiae Cheongdo using 192 carbon sources. Compared to commercial wine yeast S. cerevisiae EC1118, Cheongdo showed significantly different growth rates on 34 substrates. The principal component analysis of the results highlighted that the better growth of Cheongdo on galactose than on EC1118 was the most significant difference between the two strains. The intact GAL4 gene and the galactose fermentation performance at a low temperatures suggested that S. cerevisiae Cheongdo is a promising host for industrial fermentation rich in galactose, such as lactose and agarose