信息融合系统效能灰色评估模型与算法

根据信息融合系统效能评估的需求,建立了基于层次结构的信息融合系统效能评估指标体系,并运用灰色理论方法讨论了效能评估模型和评估算法．通过仿真举例,结果表明该算法简单方便．模型及算法有效可行,评价结果具有客观性．     

基于像全息技术的资料存储

文中利用像全息技术,对二维文字资料作了全息存储,并对实验结果进行了分析,不仅 提高了存诸质量,而且为今后的全息显示打下了基础。     

Development of Optogenetic Dual-Switch System for Rewiring Metabolic Flux for Polyhydroxybutyrate Production

Several strategies, including inducer addition and biosensor use, have been developed for dynamical regulation. However, the toxicity, cost, and inflexibility of existing strategies have created a demand for superior technology. In this study, we designed an optogenetic dual-switch system and applied it to increase polyhydroxybutyrate (PHB) production. First, an optimized chromatic acclimation sensor/regulator (RBS10–CcaS#10–CcaR) system (comprising an optimized ribosomal binding site (RBS), light sensory protein CcaS, and response regulator CcaR) was selected for a wide sensing range of approximately 10-fold between green-light activation and red-light repression. The RBS10–CcaS#10–CcaR system was combined with a blue light-activated YF1–FixJ–PhlF system (containing histidine kinase YF1, response regulator FixJ, and repressor PhlF) engineered with reduced crosstalk. Finally, the optogenetic dual-switch system was used to rewire the metabolic flux for PHB production by regulating the sequences and intervals of the citrate synthase gene (gltA) and PHB synthesis gene (phbCAB) expression. Consequently, the strain RBS34, which has high gltA expression and a time lag of 3 h, achieved the highest PHB content of 16.6 wt%, which was approximately 3-fold that of F34 (expressed at 0 h). The results indicate that the optogenetic dual-switch system was verified as a practical and convenient tool for increasing PHB production.     

Channel-Supermodular Entropies: Order Theory and an Application to Query Anonymization

This work introduces channel-supermodular entropies, a subset of quasi-concave entropies. Channel-supermodularity is a property shared by some of the most commonly used entropies in the literature, including Arimoto–Rényi conditional entropies (which include Shannon and min-entropy as special cases), k-tries entropies, and guessing entropy. Based on channel-supermodularity, new preorders for channels that strictly include degradedness and inclusion (or Shannon ordering) are defined, and these preorders are shown to provide a sufficient condition for the more-capable and capacity ordering, not only for Shannon entropy but also regarding analogous concepts for other entropy measures. The theory developed is then applied in the context of query anonymization. We introduce a greedy algorithm based on channel-supermodularity for query anonymization and prove its optimality, in terms of information leakage, for all symmetric channel-supermodular entropies.     

Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters

Background: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. Methods: A case–control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. Results: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. Conclusions: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections.     

ON THE CULTIVATION OF UNDERGRADUATE STUDENTS MAJORING MECHANICS BASED ON THE IDEA OF UNIVERSITY

21世纪信息技术、微纳米技术和生物技术等新兴领域的高速发展给力学专业的人才培养带来了机遇与挑战。本文简单介绍了高等教育的基本理念与发展及其在中国的演变。通过分析现阶段社会发展的需求和力学发展的趋势,阐述了力学本科的培养目标应该是具有宽广的人文素养和深厚的专业知识的创新型人才。本文结合上海大学二十余年来理论与应用力学本科人才培养的实际情况,对实现这一目标提出了几点参考建议。     

军用计算机系统中的电磁信息泄漏和防护

分析了计算机电磁信息泄漏的途径、机理、特点及接收和还原,提出了军用计算机系统中电磁信息防护的几种方法。     

A New Strategy in Boosting Information Spread

Finding a seed set to propagate more information within a specific budget is defined as the influence maximization (IM) problem. The traditional IM model contains two cardinal aspects: (i) the influence propagation model and (ii) effective/efficient seed-seeking algorithms. However, most of models only consider one kind of node (i.e., influential nodes), ignoring the role of other nodes (e.g., boosting nodes) in the spreading process, which are irrational. Specifically, in the real-world propagation scenario, the boosting nodes always improve the spread of influence from the initial activated seeds, which is an efficient and cost-economic measure. In this paper, we consider the realistic budgeted influence maximization (RBIM) problem, which contains two kind of nodes to improve the diffusion of influence. Facing the newly modified objective function, we propose a novel B-degree discount algorithm to solve it. The novel B-degree discount algorithm which adopts the cost-economic boosting nodes to retweet the influence from the predecessor nodes can greatly reduce the cost, and performs better than other state-of-the-art algorithms in both effect and efficiency on RBIM problem solving.     

An Information Quantity in Pure State Models

When we consider an error model in a quantum computing system, we assume a parametric model where a prepared qubit belongs. Keeping this in mind, we focus on the evaluation of the amount of information we obtain when we know the system belongs to the model within the parameter range. Excluding classical fluctuations, uncertainty still remains in the system. We propose an information quantity called purely quantum information to evaluate this and give it an operational meaning. For the qubit case, it is relevant to the facility location problem on the unit sphere, which is well known in operations research. For general cases, we extend this to the facility location problem in complex projective spaces. Purely quantum information reflects the uncertainty of a quantum system and is related to the minimum entropy rather than the von Neumann entropy.     

Applications of Raman-based techniques to on-site and in-vivo analysis

Raman-based technologies have proved to be excellent tools for on-site and in-vivo analysis, due to the non-invasive nature of their detection, their capability of providing structure information, their high tolerance to aqueous samples, the ultra-sensitivity of surface-enhanced Raman scattering (SERS) and resonance Raman scattering (RRS), the high spatial resolution of tip-enhanced Raman scattering (TERS), and the ultrashort spectra-acquisition time for coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS).In this review, we highlight the recent success of Raman-based technologies in various applications, including:

• (1) 

  on-site surface analysis and chemical-reaction monitoring;

• (2) 

  on-site identification of cultural objects, archeological studies and planetary science;

• (3) 

  in-vivo analysis of cells and microorganisms;

• (4) 

  in-vivo diagnosis inside human and animal bodies;

• (5) 

  in-vivo fast Raman imaging and mapping;

• (6) 

  the study of SERS processes; and,

• (7) 

  assessment of nanomaterial safety.