基于多通道的ICU脑血管疾病死亡风险预测模型

死亡风险预测指根据病人临床体征监测数据来预测未来一段时间的死亡风险。对于ICU病患，通过死亡风险预测可以有针对性地对病人做出临床诊断，以及合理安排有限的医疗资源。基于临床使用的MEWS和Glasgow昏迷评分量表，针对ICU病人临床监测的17项生理参数，提出一种基于多通道的ICU脑血管疾病死亡风险预测模型。引入多通道概念应用于BiLSTM模型，用于突出每个生理参数对死亡风险预测的作用。采用Attention机制用于提高模型预测精度。实验数据来自MIMIC [Ⅲ]数据库，从中提取3?080位脑血管疾病患者的16?260条记录用于此次研究，除了六组超参数实验之外，将所提模型与LSTM、Multichannel-BiLSTM、逻辑回归（logistic regression）和支持向量机（support vector machine, SVM）四种模型进行了对比分析，准确率Accuracy、灵敏度Sensitive、特异性Specificity、AUC-ROC和AUC-PRC作为评价指标，实验结果表明，所提模型性能优于其他模型，AUC值达到94.3%。     

4D Printing of Shape Memory Materials for Textiles: Mechanism,Mathematical Modeling,and Challenges

Shape memory materials (SMMs) in 3D printing (3DP) technology garnered much attention due to their ability to respond to external stimuli, which direct this technology toward an emerging area of research, “4D printing (4DP) technology.” In contrast to classical 3D printed objects, the fourth dimension, time, allows printed objects to undergo significant changes in shape, size, or color when subjected to external stimuli. Highly precise and calibrated 4D materials, which can perform together to achieve robust 4D objects, are in great demand in various fields such as military applications, space suits, robotic systems, apparel, healthcare, sports, etc. This review, for the first time, to the best of the authors’ knowledge, focuses on recent advances in SMMs (e.g., polymers, metals, etc.) based wearable smart textiles and fashion goods. This review integrates the basic overview of 3DP technology, fabrication methods, the transition of 3DP to 4DP, the chemistry behind the fundamental working principles of 4D printed objects, materials selection for smart textiles and fashion goods. The central part summarizes the effect of major external stimuli on 4D textile materials followed by the major applications. Lastly, prospects and challenges are discussed, so that future researchers can continue the progress of this technology.     

The Function of Sialidase Revealed by Sialidase Activity Imaging Probe

Sialidase cleaves sialic acid residues from glycans such as glycoproteins and glycolipids. In the brain, desorption of the sialic acid by sialidase is essential for synaptic plasticity, learning and memory and synaptic transmission. BTP3-Neu5Ac has been developed for sensitive imaging of sialidase enzyme activity in mammalian tissues. Sialidase activity in the rat hippocampus detected with BTP3-Neu5Ac increases rapidly by neuronal depolarization. It is presumed that an increased sialidase activity in conjunction with neural excitation is involved in the formation of the neural circuit for memory. Since sialidase inhibits the exocytosis of the excitatory neurotransmitter glutamate, the increased sialidase activity by neural excitation might play a role in the negative feedback mechanism against the glutamate release. Mammalian tissues other than the brain have also been stained with BTP3-Neu5Ac. On the basis of information on the sialidase activity imaging in the pancreas, it was found that sialidase inhibitor can be used as an anti-diabetic drug that can avoid hypoglycemia, a serious side effect of insulin secretagogues. In this review, we discuss the role of sialidase in the brain as well as in the pancreas and skin, as revealed by using a sialidase activity imaging probe. We also present the detection of influenza virus with BTP3-Neu5Ac and modification of BTP3-Neu5Ac.     

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.     

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。     

Cognitive abilities,digital games and arithmetic performance enhancement: A study comparing the effects of a math game and paper exercises

Besides entertainment, games have shown to have the potential to impact a broader variety of cognitive abilities. Research has consistently shown that several aspects in cognition such as visual short-memory, multitasking and spatial skills can be enhanced by game play. In a previous study, it was found that playing Monkey Tales, a game aimed at training arithmetic skills, helped second grade pupils to increase their accuracy in mental calculation as compared to paper exercises. In this follow up study we explore whether traditional methods and game training differ in terms of the cognitive processes that both are able to impact. We incorporated standardized measures of working memory and visuo-motor skills. Additionally, the mathematics game was modified and its contents extracted to allow precise comparison between the gaming and paper exercises condition. Thus each single math exercise, type of question (e.g., multiple choice), quantity and order was perfectly matched in the game training and the traditional training conditions. Gains in arithmetical performance, and self-reported measures of enjoyment were also investigated. We found some evidence suggesting that arithmetic performance enhancement induced by game play and paper exercises differ not only in terms of enjoyment but also of working memory capacity improvements.     

An industrial case study on variability handling in large enterprise software systems

ContextEnterprise software systems (e.g., enterprise resource planning software) are often deployed in different contexts (e.g., different organizations or different business units or branches of one organization). However, even though organizations, business units or branches have the same or similar business goals, they may differ in how they achieve these goals. Thus, many enterprise software systems are subject to variability and adapted depending on the context in which they are used.ObjectiveOur goal is to provide a snapshot of variability in large scale enterprise software systems. We aim at understanding the types of variability that occur in large industrial enterprise software systems. Furthermore, we aim at identifying how variability is handled in such systems.MethodWe performed an exploratory case study in two large software organizations, involving two large enterprise software systems. Data were collected through interviews and document analysis. Data were analyzed following a grounded theory approach.ResultsWe identified seven types of variability (e.g., functionality, infrastructure) and eight mechanisms to handle variability (e.g., add-ons, code switches).ConclusionsWe provide generic types for classifying variability in enterprise software systems, and reusable mechanisms for handling such variability. Some variability types and handling mechanisms for enterprise software systems found in the real world extend existing concepts and theories. Others confirm findings from previous research literature on variability in software in general and are therefore not specific to enterprise software systems. Our findings also offer a theoretical foundation for describing variability handling in practice. Future work needs to provide more evaluations of the theoretical foundations, and refine variability handling mechanisms into more detailed practices.     

Resistive switching characteristics of ZnO–graphene quantum dots and their use as an active component of an organic memory cell with one diode-one resistor architecture

We investigated the resistive switching characteristics of a polystyrene:ZnO–graphene quantum dots system and its potential application in a one diode-one resistor architecture of an organic memory cell. The log–log I–V plot and the temperature-variable I–V measurements revealed that the switching mechanism in a low-current state is closely related to thermally activated transport. The turn-on process was induced by a space-charge-limited current mechanism resulted from the ZnO–graphene quantum dots acting as charge trap sites, and charge transfer through filamentary path. The memory device with a diode presented a ∼10³ I_ON/I_OFF ratio, stable endurance cycles (10² cycles) and retention times (10⁴ s), and uniform cell-to-cell switching. The one diode-one resistor architecture can effectively reduce cross-talk issue and realize a cross bar array as large as ∼3 kbit in the readout margin estimation. Furthermore, a specific word was encoded using the standard ASCII character code.     

300M超高强钢在中性盐雾环境中的腐蚀行为及机制

摘要：为了研究300M超高强钢在中性盐雾环境中的腐蚀行为及腐蚀机制，采用失重法，宏观、微观腐蚀形貌分析，三维表面轮廓分析及电化学分析的研究方法，来表征腐蚀实验现象并进行分析。结果表明：300M超高强钢在中性盐雾环境中的腐蚀产物为FeOOH、Fe2O3、Fe(OH)3和Fe3O4；腐蚀速率随着腐蚀时间逐渐降低，腐蚀后期（72h）腐蚀速率降低50%；腐蚀初期以点蚀为主，点蚀坑通过横向扩展，逐渐发展为后期的均匀腐蚀，腐蚀表面形貌呈沟壑状；外腐蚀层对基体的保护能力很弱，Cr元素在锈层靠近基体的一侧偏聚使内腐蚀层具有一定的抗腐蚀性。     

APP软件性能效率研究

性能效率是APP软件的重要质量属性，但目前缺乏APP软件性能效率的通用模型。分析了APP软件的性能特征，基于ISO/IEC 25010标准提出了APP软件的性能效率模型，定义了APP软件性能效率的子特性和度量指标。基于提出的APP软件性能效率模型，通过实验对APP软件的性能效率进行了度量及相关分析。