BETA: Behavioral testability analyzer and its application to high-level test generation and synthesis for testability

In this thesis, a behavioral-level testability analysis approach is presented. This approach is based on analyzing the circuit behavioral description (similar to a C program) to estimate its testability by identifying controllable and observable circuit nodes. This information can be used by a test generator to gain better access to internal circuit nodes and to reduce its search space. The results of the testability analyzer can also be used to select test points or partial scan flip-flops in the early design phase. Based on selection criteria, a novel Synthesis for Testability approach call Test Statement Insertion (TSI) is proposed, which modifies the circuit behavioral description directly. Test Statement Insertion can also be used to modify circuit structural description to improve its testability. As a result, Synthesis for Testability methodology can be combined with an existing behavioral synthesis tool to produce more testable circuits

Digit Recognition Using Composite Features With Decision Tree Strategy

At present, check transactions are one of the most common forms of money transfer in the market. The information for check exchange is printed using magnetic ink character recognition (MICR), widely used in the banking industry, primarily for processing check transactions. However, the magnetic ink card reader is specialized and expensive, resulting in general accounting departments or bookkeepers using manual data registration instead. An organization that deals with parts or corporate services might have to process 300 to 400 checks each day, which would require a considerable amount of labor to perform the registration process. The cost of a single-sided scanner is only 1/10 of the MICR; hence, using image recognition technology is an economical solution. In this study, we aim to use multiple features for character recognition of E13B, comprising ten numbers and four symbols. For the numeric part, we used statistical features such as image density features, geometric features, and simple decision trees for classification. The symbols of E13B are composed of three distinct rectangles, classified according to their size and relative position. Using the same sample set, MLP, LetNet-5, Alexnet, and hybrid CNN-SVM were used to train the numerical part of the artificial intelligence network as the experimental control group to verify the accuracy and speed of the proposed method. The results of this study were used to verify the performance and usability of the proposed method. Our proposed method obtained all test samples correctly, with a recognition rate close to 100%. A prediction time of less than one millisecond per character, with an average value of 0.03 ms, was achieved, over 50 times faster than state-of-the-art methods. The accuracy rate is also better than all comparative state-of-the-art methods. The proposed method was also applied to an embedded device to ensure the CPU would be used for verification instead of a high-end GPU

Smart management energy systems in industry 4.0

In its origins, the term Industry 4.0 was associated with the computerization of manufacturing and with the diffusion of new network technologies in order to improve the communication paradigm. Today, the definition and implementation of Industry 4.0 include a number of trends, such as the Internet of Things (IoT), digital manufacturing and cyber-physical systems. Among these key elements, energy aware systems in factory automation are emerging as a challenging trend of Industry 4.0. Technological advancements in the ability to collect, transfer and analyze data by using smart energy aware systems are at the aim of this trend. Smart solutions in order to limit the power consumption of manufacturing line aim at developing and integrating new technologies and methods into smart factories in order to rapidly adapt and respond to changes in the markets’ demands for high-quality products. In fact, smart energy aware systems in factories lie at the core of both Industry 4.0 and smart manufacturing. A variety of recent advanced technologies and approaches play important roles, by exploiting innovative technologies and solutions and/or optimization methods. They allow higher levels of adaptively and flexibility in energy aware systems

Antioxidant and tyrosinase inhibitor from Leucaena leucocephala

The experimental design is divided into two parts: chemical analysis and bioactive assay. One antioxidant lupeol (4) and one inhibition of tyrosinase pheophorbide a methyl ester (7) were identified in Leucaena leucocephala. Both showed effective 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity compared with vitamin C, and mushroom tyrosinase compared with kojic acid. These results suggest that these constituents of L. leucocephala act as natural antioxidants and play a potential role in prevention of pigmentation.Keywords: Leucaenana leucocephala, lupeol, pheophorbide a methyl ester, antioxidant, mushroom tyrosinas

A Bayesian measurement error model for two-channel cell-based RNAi data with replicates

RNA interference (RNAi) is an endogenous cellular process in which small double-stranded RNAs lead to the destruction of mRNAs with complementary nucleoside sequence. With the production of RNAi libraries, large-scale RNAi screening in human cells can be conducted to identify unknown genes involved in a biological pathway. One challenge researchers face is how to deal with the multiple testing issue and the related false positive rate (FDR) and false negative rate (FNR). This paper proposes a Bayesian hierarchical measurement error model for the analysis of data from a two-channel RNAi high-throughput experiment with replicates, in which both the activity of a particular biological pathway and cell viability are monitored and the goal is to identify short hair-pin RNAs (shRNAs) that affect the pathway activity without affecting cell activity. Simulation studies demonstrate the flexibility and robustness of the Bayesian method and the benefits of having replicates in the experiment. This method is illustrated through analyzing the data from a RNAi high-throughput screening that searches for cellular factors affecting HCV replication without affecting cell viability; comparisons of the results from this HCV study and some of those reported in the literature are included.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS496 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

E2F transcription factor 1 overexpression as a poor prognostic factor in patients with nasopharyngeal carcinomas

AbstractNasopharyngeal carcinoma (NPC) is an endemic head and neck epithelial malignancy in Southeastern Asia and Taiwan. The E2 factor (E2F) family of transcription factors is downstream targets of the retinoblastoma protein 1. The E2F family of transcription factors is the key regulator of genes involved in cell cycle progression, cell fate determination, DNA damage repair and apoptosis. E2F1 is unique in that it contributes both to the control of cellular proliferation and cellular death. However, the expression of E2F1 protein and its clinicopathological associations in patients with NPC are yet to be evaluated. Immunoexpression of E2F1 was retrospectively assessed in biopsies of 124 consecutive NPC patients without initial distant metastasis and treated with consistent guidelines. The outcomes were correlated with clinicopathological features and patient survivals. Results indicated that high E2F1 protein level (50%) was correlated with primary tumor (p < 0.001) and stage (p = 0.002; 7th American Joint Committee on Cancer). In multivariate analyses, high E2F1 expression emerged as an independent prognosticator for worse disease-specific survival (p = 0.003), distal metastasis-free survival (p = 0.003), and local recurrence-free survival (p = 0.039). In conclusion, high E2F1 protein level is common, associated with adverse prognosticators, and might confer tumor aggressiveness through tumor cell proliferation and metastasis