A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers

The aim of this study is to identify and prioritize the solutions of Knowledge Management (KM) adoption in Supply Chain (SC) to overcome its barriers. It helps organizations to concentrate on high rank solutions and develop strategies to implement them on priority. This paper proposes a framework based on fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS) to identify and rank the solutions of KM adoption in SC and overcome its barriers. The AHP is used to determine weights of the barriers as criteria, and fuzzy TOPSIS method is used to obtain final ranking of the solutions of KM adoption in SC. The empirical case study analysis of an Indian hydraulic valve manufacturing organization is conducted to illustrate the use of the proposed framework for ranking the solutions of KM adoption in SC to overcome its barriers. This proposed framework provides a more accurate, effective and systematic decision support tool for stepwise implementation of the solutions of KM adoption in SC to increase its success rate.     

基于Linux Qt的软键盘设计与实现

介绍在Linux操作系统Ubuntu中,基于Qt的软键盘设计与实现方法,该键盘包含了通用键盘中的绝大部分功能,界面友善,操作简单,可以应用于各种基于LintLx操作系统的带触摸屏设备。     

基于独立分量聚类分析的诱发脑电特征提取技术

发展了一种独立分量聚类分析的诱发脑电特征提取方法,利用诱发成分较强的序间相似性,使用Infomax结合K均值算法对脑电信号中的诱发成分进行分类和提取.该方法可以克服传统独立分量分解方法中诱发分量识别的困难,适用于重复刺激诱发脑电的高维数据自动分析处理.将该方法用于上肢想像动作任务的诱发脑电数据分析,结果显示该方法可以有效剥离背景噪声和提取诱发分量,使得信号的费雪可分性得到显著提升,进而获得更好的识别效果.研究结果表明独立分量自动聚类技术适用于认知行为脑电信号的分析,值得进一步研究.     

相似点区段负责制的改进区域生长法及其应用

区域生长法被广泛应用于对特定目标的选取,但其生长速度缓慢的特点又限制了其在某些领域的应用.为此,提出一种基于相似点区段负责制的像素点进出堆栈管理方法,有效的提高了区域生长的效率.在此基础上,结合链码法边界跟踪对生长过程中区域内部出现的孔洞实现区域补偿,建立了针对链状藻细胞分析统计的有效应用系统.     

Synthesis of SnAgCu nanoparticles with low melting point by the chemical reduction method

SnAgCu alloy with low melting point and good soldering property is a good candidate for the Sn/Pb eutectic. In this paper, SnAgCu nanoparticles were synthesized by a chemical reduction method. The particle size and the melting point are controlled by modifying the process parameters, including reaction temperature, surfactant concentration and dropping speed of precursor. The lowest melting onset temperature is observed at 199.1 °C, which is 18 °C lower than that of commercially available SnAgCu solder alloy. The tensile strength of the as-synthesized reaches 34.3 MPa, which reveals a good solderability property.     

Weighted archetypal analysis of the multi-element graph for query-focused multi-document summarization

Most existing research on applying the matrix factorization approaches to query-focused multi-document summarization (Q-MDS) explores either soft/hard clustering or low rank approximation methods. We employ a different kind of matrix factorization method, namely weighted archetypal analysis (wAA) to Q-MDS. In query-focused summarization, given a graph representation of a set of sentences weighted by similarity to the given query, positively and/or negatively salient sentences are values on the weighted data set boundary. We choose to use wAA to compute these extreme values, archetypes, and hence to estimate the importance of sentences in target documents set. We investigate the impact of using the multi-element graph model for query focused summarization via wAA. We conducted experiments on the data of document understanding conference (DUC) 2005 and 2006. Experimental results evidence the improvement of the proposed approach over other closely related methods and many of state-of-the-art systems.     

A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition

Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques—over-sampling, under-sampling and synthetic minority over-sampling (SMOTE)—along with four popular classification methods—logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates.     

Characterizing a RISC-V SRAM-based FPGA implementation against Single Event Upsets using fault injection

The reliability of microprocessors is a big concern in space environments, where they are exposed to cosmic radiation. This radiation can produce Single Event Upsets (SEUs). Some of these microprocessors, often called soft processors, are implemented on SRAM-based FPGAs instead of being manufactured as an ASIC. Fault injection campaigns are needed in order to estimate the soft processor reliability in this harsh environment. This work, characterizes a new RISC soft-core, called lowRISC, based on the RISC-V ISA. Ten tests have been carried out to characterize the SEU sensitivity of lowRISC. Also, we have performed a comparison among lowRISC and other microprocessors, concluding that their sensitivities are all in the same range.     

Reliability analysis of a newly developed detector for monitoring spine health

Following with tons of people studying and working in a sitting posture for long, spine disease became one of the most important occupational health problems. The patients with condition worsen have to suffer harmful CT scans or even painful serial lumbar puncture. We innovatively developed a biomedical optics based detector for spine health monitoring and particularly improved the circuit design for feeble signal detection. A series of experiments were performed to fully test its reliabilities, including scalability, noise and crosstalk, sensitivity, stability etc. The results showed that this device was very reliable, sensitive, and stabile, suggested the promising clinical use of our novel device in spine/spinal marrow health monitoring and diagnosis.     

Interrelationship of thermal parameters,microstructure and microhardness of directionally solidified Bi–Zn solder alloys

Due to the health and environmental concerns associated with lead usage, the research on alternative lead-free alloys for replacing lead-based solders is a global demand. Despite numerous studies on Sn-based lead-free solders in recent years, there is not yet a standard solder alloy able to cover the spectrum of properties furnished by the classic Sn-Pb alloy. In this sense, particular lead-free alloys compositions have been suited for specific needs and the options have been broadening when elements other than tin are used as the base component, such as indium, gold and bismuth. The later element is well known in the hall of lead-free alloys as an alloying option rather than as a base component. This study aims to establish interrelations of solidification thermal parameters (growth and cooling rates), microstructure features (primary and secondary dendrite arm spacings – λ₁ and λ₂; eutectic spacing – λ_E and interphase spacing – λ_int) and hardness of Bi-Zn alloys (1.5 wt% Zn-hypoeutectic, 2.7 wt% Zn-eutectic, and 5 wt% Zn-hypereutectic alloys) samples, which were directionally solidified in unsteady-state conditions under cooling rates similar to those of found in industrial soldering practice. Examination of the resulting microstructures by scanning electron microscopy (SEM) and energy dispersive spectrometry (EDS) permitted the different phases morphologies to be characterized: Bi-rich trigonal dendrites and long Zn fibers, as primary phases of, hypoeutectic and hypereutectic alloys, respectively, immersed in a fiber-like eutectic mixture. The combined effects of macrosegregation, Zn alloying and representative scales of the phases forming the microstructure (λ_int, λ₂ and λ_E) on hardness of the Bi-Zn alloys are evaluated and Hall–Petch type equations relating λ_int, λ₂ and λ_E to hardness are proposed.