Test Point Insertion with Control Point by Greater Use of Existing Functional Flip‐Flops

This paper presents a novel test point insertion (TPI) method for a pseudo‐random built‐in self‐test (BIST) to reduce the area overhead. Recently, a new TPI method for BISTs was proposed that tries to use functional flip‐flops to drive control test points instead of adding extra dedicated flip‐flops for driving control points. The replacement rule used in a previous work has limitations preventing some dedicated flip‐flops from being replaced by functional flip‐flops. This paper proposes a logic cone analysis–based TPI approach to overcome the limitations. Logic cone analysis is performed to find candidate functional flop‐flops for replacing dedicated flip‐flops. Experimental results indicate that the proposed method reduces the test point area overhead significantly with minimal loss of testability by replacing the dedicated flip‐flops.     

Performance Evaluation of Existing Bridge Fenders for Ship Impact

Bridge fender systems are used to guide vessels through navigational channels under bridges. They appear to be an attractive and inexpensive solution for protecting the integrity of bridge piers. However, until now the fenders have not been designed to withstand any specific lateral design force. Thus, their existing impact capacity is unknown. The paper presents results of the research project funded by the Florida Department of Transportation aimed at evaluating the crashworthiness of the system and developing possible retrofit recommendations. The research efforts concentrated on computational analysis of a Jumbo Hopper barge impacting a commonly constructed fender. LS-DYNA, a nonlinear explicit dynamic finite-element code, was used for analysis. Various initial velocities and impact angles were used to represent possible collision conditions. Computational analyses were adopted to assess the crashworthiness performance of the constructed fender system and to identify its weakest components for possible retrofit. It appears that such retrofits could further improve the energy absorbing capabilities of existing bridge fenders.     

Probiotic potential of lactic acid bacteria from fermented Malaysian food or milk products

This study investigated the probiotic characteristics of lactic acid bacteria (LAB) isolated from 14 Malaysian fermented food or milk products. In total, 22.3% (121 of 542) of the LAB isolated from the local fermented products exhibited antibacterial activity against Micrococcus luteus. Twelve LAB that demonstrated better antibacterial activity against M. luteus as compared to a commercial strain Lactobacillus casei strain Shirota (LABPC) were selected for further characterisation. Based on 16S rRNA gene sequence, the LAB were identified as pediococci (seven) and lactobacilli (five). All 12 LAB showed bile tolerance, but only eight were acid tolerant at pH ≥ 3.0. The highest level of adhesion to HT‐29 cells was observed among the Lactobacillus sp. LAB 1 and 10. The LAB also showed antimicrobial activity against Escherichia coli and Staphylococcus aureus through the production of organic acids. LAB isolated from Malaysian fermented food and milk products, especially fermented tapioca, contains potential probiotic candidates.     

Comparison of gene nature used in real-time PCR for porcine identification and quantification: A review

Pork adulteration has been a major concern nowadays for Halal verification. Unintentional pork inclusion by contamination in highly processed food materials involves a minute amount of porcine DNA to be detected, emphasizing the need of specific and sensitive method for porcine detection. Real-time PCR is a widely used technique for species identification that can serve this purpose besides providing a powerful quantification method. Incorporation of a highly sensitive and specific probe can greatly improve the specificity and sensitivity of the assay. However, derivation of PCR primers, either from nuclear DNA (nDNA) or mitochondrial DNA (mtDNA) can relatively affect the sensitivity and specificity of the reaction as well as the quantitative measurement. In this review, both types of DNA are compared in terms of their characteristics and their influence on species identification and quantification using real-time PCR.     

Social Engineering Attack Classifications on Social Media Using DeepLearning

In defense-in-depth, humans have always been the weakest link in cybersecurity. However, unlike common threats, social engineering poses vulnerabilities not directly quantifiable in penetration testing. Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware. Social Engineering (SE) in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic. In this paper, a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory (RNN-LSTM) to identify well-disguised SE threats in social media posts. We use a custom dataset crawled from hundreds of corporate and personal Facebook posts. First, the social engineering attack detection pipeline (SEAD) is designed to filter out social posts with malicious intents using domain heuristics. Next, each social media post is tokenized into sentences and then analyzed with a sentiment analyzer before being labelled as an anomaly or normal training data. Then, we train an RNN-LSTM model to detect five types of social engineering attacks that potentially contain signs of information gathering. The experimental result showed that the Social Engineering Attack (SEA) model achieves 0.84 in classification precision and 0.81 in recall compared to the ground truth labeled by network experts. The experimental results showed that the semantics and linguistics similarities are an effective indicator for early detection of SEA.     

Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced Data: Improving Machine Failure Prediction

Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure rate. The common approach to handle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling, random oversampling, or Synthetic Minority Oversampling Technique (SMOTE) algorithms. This paper compared the classification performance of three popular classifiers (Logistic Regression, Gaussian Naïve Bayes, and Support Vector Machine) in predicting machine failure in the Oil and Gas industry. The original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945 (97%) ‘non-failure’ and 528 (3%) ‘failure data’. The three independent variables to predict machine failure were pressure indicator, flow indicator, and level indicator. The accuracy of the classifiers is very high and close to 100%, but the sensitivity of all classifiers using the original dataset was close to zero. The performance of the three classifiers was then evaluated for data with different imbalance rates (10% to 50%) generated from the original data using SMOTE, SMOTE-Support Vector Machine (SMOTE-SVM) and SMOTE-Edited Nearest Neighbour (SMOTE-ENN). The classifiers were evaluated based on improvement in sensitivity and F-measure. Results showed that the sensitivity of all classifiers increases as the imbalance rate increases. SVM with radial basis function (RBF) kernel has the highest sensitivity when data is balanced (50:50) using SMOTE (Sensitivity_test =0.5686, F_test= 0.6927) compared to Naïve Bayes (Sensitivity_test =0.4033, F_test= 0.6218) and Logistic Regression (Sensitivity_test =0.4194, F_test= 0.621). Overall, the Gaussian Naïve Bayes model consistently improves sensitivity and F-measure as the imbalance ratio increases, but the sensitivity is below 50%. The classifiers performed better when data was balanced using SMOTE-SVM compared to SMOTE and SMOTE-ENN.     

Numerical Simulations of One-Directional Fractional PharmacokineticsModel

In this paper, we present a three-compartment of pharmacokinetics model with irreversible rate constants. The compartment consists of arterial blood, tissues and venous blood. Fick’s principle and the law of mass action were used to develop the model based on the diffusion process. The model is modified into a fractional pharmacokinetics model with the sense of Caputo derivative. The existence and uniqueness of the model are investigated and the positivity of the model is established. The behaviour of the model is investigated by implementing numerical algorithms for the numerical solution of the system of fractional differential equations. MATLAB software is used to plot the graphs for illustrating the variation of drug concentration concerning time. Therefore, the numerical simulations of the model are presented for different values of α which verified the theoretical analysis. Besides, we also observed the pattern of the simulations at the three-compartment of the model by using different values of initial conditions.     

An investigation on process of seeded granulation in a continuous drum granulator using DEM

Numerical simulation of wet granulation in a continuous granulator is carried out using Discrete Element Method (DEM) to discover the possibility of formation of seeded granules in a continuous process with the aim of reducing number of experimental trials and means of process control. Simple and scooped drum granulators are utilized to attain homogenous seeded granules in which the effects of drum rotational speed, particles surface energy, and particles size ratio are investigated. To reduce the simulation time a scale-up scheme is designed in which a dimensionless number (Cohesion number) is defined based on the work of cohesion and gravitational potential energy of the particles. Also a mathematical/numerical method along with a MATLAB code is developed by which the percentage of surface coverage of each granule is predicted precisely. The results show that use of continuous granulator for seeded granulation is promising provided that a high level of shear is considered in the granulator design, i.e. using baffles inside drum granulators is essential for producing seeded granules. It is observed that the optimum surface energy for seeded granulation in scooped granulator (used in this study) with rotational speed of 50 rpm is 3 J/m², which is close to the value predicted by the concept of Cohesion number. It is also shown that increasing the seed/fine size ratio enhances the seeded granulation both quantitatively (60% increase in seeds surface coverage) and qualitatively (more homogeneous granules).     

Properties enhancement using oil palm shell nanoparticles of fibers reinforced polyester hybrid composites

Oil palm shell (OPS) nanoparticles were utilized as filler in fibers reinforced polyester hybrid composites. The OPS nanoparticles were successfully produced from the raw OPS using high-energy ball milling process. Fundamental properties including morphology, crystalline size, and particle size of the OPS nanoparticles were determined. Tri-layer natural fiber reinforcement (kenaf–coconut–kenaf fiber mat) polyester hybrid composites were prepared by hand lay-up techniques. The influences of the OPS nanoparticles loading in the natural fibers reinforced polyester hybrid composites were determined by analyzing physical, mechanical, morphological, and thermal properties of the composites. Results showed that the incorporation of the OPS nanoparticles into the hybrid composites enhanced the composite properties. Further, the natural fibers reinforced polyester hybrid composite had the highest physical, mechanical, morphological, and thermal characteristics at 3 wt.% OPS nanoparticles loading.     

一种基于小波变换的鲁棒彩色图像水印算法(英文)

随着网络化多媒体技术的迅速发展,安全的数据通信与传输需求日益增加,如卫星遥感、医疗图像、远程监控等领域都迫切需要安全的通信与传输.而数据完整性不能完全解决数据传输过程中的安全性问题,数字水印技术是一种解决该问题的方案,基于小波变换,提出了一种鲁棒的彩色数字水印算法.算法以一随机实值序列为水印,首先对彩色图像G分量进行三层小波变换,然后将水印嵌入到G分量的低频子带中.通过阈值选择比较水印嵌入前后G分量低频子带系数以提取水印.实验结果表明提出算法能抵抗多种不同类型攻击,如椒盐噪声、剪切、尺度、JPEG压缩、中值滤波、复制*/粘贴等.