Comparison of Various Pipelined and Non-Pipelined SCl 8051 ALUs

This paper describes the development of an 8-bit SCL 8051 ALU with two versions: SCL 8051 ALU with nsleep and sleep signals and SCL 8051 ALU without nsleep. Both versions have combinational logic (C/L), registers, and completion components, which all utilize slept gates. Both three-stage pipelined and non-pipelined designs were examined for both versions. The four designs were compared in terms of area, speed, leakage power, average power and energy per operation. The SCL 8051 ALU without nsleep is smaller and faster, but it has greater leakage power. It also has lower average power, and less energy consumption than the SCL 8051 ALU with both nsleep and sleep signals. The pipelined SCL 8051 ALU is bigger, slower, and has larger leakage power, average power and energy consumption than the non-pipelined SCL 8051 ALU

Hong Kong protests: A quantitative and bottom-up account of resistance against Chinese social media (sina weibo) censorship

Chinese online censorship, though has been deeply explored by many scholars from a top-down perspective and has mostly concentrated on the macro level, it appears that there are few, if any, existing studies that features a bottom-up perspective and explores the micro-level aspects of online media censorship. To fill this research gap, this article uses the Occupy movement in Hong Kong as a research case to analyze social media users’ resistance under conditions of heavy censorship from a bottom-up perspective. That is, the research questions seek to uncover what novel ways Weibo users use to try and circumvent Weibo censorship. It is confirmed that the microbloggers tend to use embedded pictures and user ID names, instead of using text messages to camouflage the sensitive information to share with other users; that Weibo users tend to create new accounts once their original ones have been closed or monitored

MSIQ: Joint Modeling of Multiple RNA-seq Samples for Accurate Isoform Quantification

Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challenging due to the information loss in sequencing experiments. A recent accumulation of multiple RNA-seq data sets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification. However, existing statistical or computational methods for multiple RNA-seq samples either pool the samples into one sample or assign equal weights to the samples when estimating isoform abundance. These methods ignore the possible heterogeneity in the quality of different samples and could result in biased and unrobust estimates. In this article, we develop a method, which we call "joint modeling of multiple RNA-seq samples for accurate isoform quantification" (MSIQ), for more accurate and robust isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. Our method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples by allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy and effectiveness of MSIQ compared with alternative methods through simulation studies on D. melanogaster genes. We justify MSIQ's advantages over existing approaches via application studies on real RNA-seq data from human embryonic stem cells, brain tissues, and the HepG2 immortalized cell line