用于电弧检测的电力专用SoC设计

针对传统的电弧电路故障检测结果不准确的问题,设计用于电弧检测的SoC系统,并且在55nm工艺下进行流片验证。采用包含两种结构的模数转换器的片上电压源,设计了锁相环以及复位电路,精度最高可达8.67 bit。验证结果表明,本设计可提高电弧检测的准确性。     

Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

Establishment of a color tolerance for yarn-dyed fabrics from different color-depth yarns

Yarn-dyed fabric is often woven from warp and weft yarns in the same color depth to ensure a uniform color appearance. The difference in color depth between warp and weft tends to result in the uneven color of the yarn-dyed fabric. This article aims to establish a color tolerance for yarn-dyed fabric that can be woven with a qualified color appearance but from the warp and weft yarns in different color depths. A total of 27 yarn-dyed fabric samples in three color series (red, yellow, and blue) were evaluated by using the yarn-dyed fabric from warp and weft yarns in the same color depth of 2% (on weight of fabric, owf) as the standard. Visual assessment and instrumental measurement of color were carried out to establish the color tolerance ellipse that was defined as CMC (Color Measurement Committee) color differences (2:1) of no more than 1.00. It was found that the color strengths (K/S) and color differences (ΔE_CMC(2:1)) of these fabric samples for each color series had linear relationships with the color depths of warp and weft yarns. The color tolerance ellipses indicated that, even though the warp and weft yarns had an apparent color difference, they could be woven in fabrics with relatively uniform color appearance and meet the requirements for yarn-dyed fabric. This work provided valuable insight into the production of qualified yarn-dyed fabrics from unqualified dyed yarns.     

Green Measurements for Software Product Based on Sustainability Dimensions

Software is a central component in the modern world and vastly affects the environment’s sustainability. The demand for energy and resource requirements is rising when producing hardware and software units. Literature study reveals that many studies focused on green hardware; however, limited efforts were made in the greenness of software products. Green software products are necessary to solve the issues and problems related to the long-term use of software, especially from a sustainability perspective. Without a proper mechanism for measuring the greenness of a particular software product executed in a specific environment, the mentioned benefits will not be attained. Currently, there are not enough works to address this problem, and the green status of software products is uncertain and unsure. This paper aims to identify the green measurements based on sustainable dimensions in a software product. The second objective is to reveal the relationships between the elements and measurements through empirical study. The study is conducted in two phases. The first phase is the theoretical phase, where the main components, measurements and practices that influence the sustainability of a software product are identified. The second phase is the empirical study that involved 103 respondents in Malaysia investigating current practices of green software in the industrial environment and further identifying the main sustainability dimensions and measurements and their impact on achieving green software products. This study has revealed seven green measurements of software product: Productivity, Usability, Cost Reduction, Employee Support, Energy Efficiency, Resource Efficiency and Tool Support. The relationships are statistically significant, with a significance level of less than 0.01 (p = 0.000). Thus, the hypothesised relationships were all accepted. The contributions of this study revolve around the research perspectives of the measurements to attain a green software product.     

Adaptation to Endoplasmic Reticulum Stress Enhances Resistance of Oral Cancer Cells to Cisplatin by Up-Regulating Polymerase η and Increasing DNA Repair Efficiency

Endoplasmic reticulum (ER) stress response is an adaptive program to cope with cellular stress that disturbs the function and homeostasis of ER, which commonly occurs during cancer progression to late stage. Late-stage cancers, mostly requiring chemotherapy, often develop treatment resistance. Chemoresistance has been linked to ER stress response; however, most of the evidence has come from studies that correlate the expression of stress markers with poor prognosis or demonstrate proapoptosis by the knockdown of stress-responsive genes. Since ER stress in cancers usually persists and is essentially not induced by genetic manipulations, we used low doses of ER stress inducers at levels that allowed cell adaptation to occur in order to investigate the effect of stress response on chemoresistance. We found that prolonged tolerable ER stress promotes mesenchymal–epithelial transition, slows cell-cycle progression, and delays the S-phase exit. Consequently, cisplatin-induced apoptosis was significantly decreased in stress-adapted cells, implying their acquisition of cisplatin resistance. Molecularly, we found that proliferating cell nuclear antigen (PCNA) ubiquitination and the expression of polymerase η, the main polymerase responsible for translesion synthesis across cisplatin-DNA damage, were up-regulated in ER stress-adaptive cells, and their enhanced cisplatin resistance was abrogated by the knockout of polymerase η. We also found that a fraction of p53 in stress-adapted cells was translocated to the nucleus, and that these cells exhibited a significant decline in the level of cisplatin-DNA damage. Consistently, we showed that the nuclear p53 coincided with strong positivity of glucose-related protein 78 (GRP78) on immunostaining of clinical biopsies, and the cisplatin-based chemotherapy was less effective for patients with high levels of ER stress. Taken together, this study uncovers that adaptation to ER stress enhances DNA repair and damage tolerance, with which stressed cells gain resistance to chemotherapeutics.     

High-Fat Diet Aggravates Cerebral Infarct,Hemorrhagic Transformation and Neuroinflammation in a Mouse Stroke Model

Background: Stroke in context of type 2 diabetes (T2D) is associated with a poorer outcome than in non-diabetic conditions. We aimed at creating a new reproducible mouse model of stroke in impaired glucose tolerance conditions induced by high-fat diet. Methods: Adult C57BL6 mice were fed for 2 months with either normal diet (ND) or high-fat diet (HFD). We used a model of Middle Cerebral Artery Occlusion (MCAO) for 90 min. Oral Glucose Tolerance Test (OGTT) and Insulin Tolerance Test (ITT) were used to assess pre-diabetic status. Brain infarct volume, hemorrhagic transformation (HT) as well as systemic and cerebral inflammatory markers were evaluated. Results: HFD was associated with an increased body weight and glycemia following OGTT. The HFD group presented a significant increase in brain infarct volume (38.7 (IQR 30–46.7%) vs. 28.45 (IQR 21–30%); p = 0.016) and HT (HFD: 2 (IQR 1–5) vs. ND: 0 (IQR 0–1); p = 0.012) and higher levels of IL-6 and MCP-1 in infarcted hemisphere compared to the ND group. Conclusion: Two months of HFD in adult mice were sufficient to alter the lipid profile and the control of hyperglycemia. These metabolic perturbations were significantly associated with increased infarct volume and hemorrhagic complications.     

基于PBFT区块链技术的电网企业仓储系统

提出了一种基于实用拜占庭容错(PBFT)算法的区块链技术,首先对传统的实用拜占庭容错算法原理进行了阐述,该传统算法包含前期、需求、预准备、准备、确认、答复6个阶段,但传统算法具有实时性差、缺乏惩罚机制、带宽高的缺点。针对出现的这些问题,又对传统算法进行了改进,具体涉及记账节点、共识过程以及视图切换过程。通过测试进一步证明了该改进算法的实用性,并将该算法应用于电网企业中,构建的虚拟仓库实现了联储联备,降低了库存资金的耗费,并且提高了电网企业库存管理的效率。     

基于启发式BP算法的软件缺陷预测模型

针对软件缺陷预测时缺陷数据集中存在的类别分布不平衡问题，结合上采样算法SMOTE与Edited Nearest Neighbor (ENN) 数据清洗策略，提出了一种基于启发式BP神经网络算法的软件缺陷预测模型。模型中采用上采样算法SMOTE增加少数类样本以改善项目中的数据不平衡状况，并针对采样后数据噪声问题进行ENN数据清洗，结合基于启发式学习的模拟退火算法改进四层BP神经网络后建立分类预测模型，在AEEEM数据库上使用交叉验证对提出的方案进行性能评估，结果表明所提出的算法能够有效提高模型在预测类不平衡数据时的分类准确度。