Improved approaches to stability criteria for neural networks with time-varying delays

In this paper, the problem of stability analysis for neural networks with time-varying delays is considered. By the use of a newly augmented Lyapunov functional and some novel techniques, sufficient conditions to guarantee the asymptotic stability of the concerned networks are established in terms of linear matrix inequalities (LMIs). Three numerical examples are given to show the improved stability region of the proposed works.     

A microfluidic device for simultaneous measurement of viscosity and flow rate of blood in a complex fluidic network

Blood viscosity has been considered as one of important biophysical parameters for effectively monitoring variations in physiological and pathological conditions of circulatory disorders. Standard previous methods make it difficult to evaluate variations of blood viscosity under cardiopulmonary bypass procedures or hemodialysis. In this study, we proposed a unique microfluidic device for simultaneously measuring viscosity and flow rate of whole blood circulating in a complex fluidic network including a rat, a reservoir, a pinch valve, and a peristaltic pump. To demonstrate the proposed method, a twin-shaped microfluidic device, which is composed of two half-circular chambers, two side channels with multiple indicating channels, and one bridge channel, was carefully designed. Based on the microfluidic device, three sequential flow controls were applied to identify viscosity and flow rate of blood, with label-free and sensorless detection. The half-circular chamber was employed to achieve mechanical membrane compliance for flow stabilization in the microfluidic device. To quantify the effect of flow stabilization on flow fluctuations, a formula of pulsation index (PI) was analytically derived using a discrete fluidic circuit model. Using the PI formula, the time constant contributed by the half-circular chamber is estimated to be 8 s. Furthermore, flow fluctuations resulting from the peristaltic pumps are completely removed, especially under periodic flow conditions within short periods (T < 10 s). For performance demonstrations, the proposed method was applied to evaluate blood viscosity with respect to varying flow rate conditions [(a) known blood flow rate via a syringe pump, (b) unknown blood flow rate via a peristaltic pump]. As a result, the flow rate and viscosity of blood can be simultaneously measured with satisfactory accuracy. In addition, the proposed method was successfully applied to identify the viscosity of rat blood, which circulates in a complex fluidic network. These observations confirm that the proposed method can be used for simultaneous measurement of viscosity and flow rate of whole blood circulating in the complex fluid network, with sensorless and label-free detection. Furthermore, the proposed method will be used in evaluating variations in the viscosity of human blood during cardiopulmonary bypass procedures or hemodialysis.     

On-demand electrostatic droplet charging and sorting

This study reports a droplet-based microfluidic device for on-demand electrostatic droplet charging and sorting. This device combines two independent modules: one is a hydrodynamic flow focusing structure to generate water-in-oil droplets, and the other is the two paired-electrodes for charging and sorting of the droplets. Depending on the polarity on charging electrodes, water-in-oil droplets can be electrostatically charged positively or negatively, followed by automatic real-time electric sorting. This approach will be useful when preformed droplets, with a positive, a negative, or with no charge, need to be manipulated for further on-chip droplet manipulation.     

秦始皇兵马俑博物馆室内含硫颗粒物的SEM-EDX研究

本文对秦始皇兵马俑博物馆室内采集的长短期降尘、大气悬浮颗粒物和彩绘漆层进行了SEM-EDX研究，在大气悬浮颗粒、短期和长期降尘中，含硫颗粒的粒径分别为0.9～22.8μm、1.0～29.0μm和1.2～37.7μm，多为石膏与粘土或石英以内部混合的状态存在。降尘中的附着型絮状硫酸钙多于大气悬浮颗粒物中，显示降尘和大气中的二氧化硫发生了累进的化学反应。彩绘漆层表面的坑和裂隙附近观察到原位生长的硫酸钙晶体，显示漆层材料或降尘颗粒物与大气二氧化硫之间的酸化学反应，可能是彩绘漆层表面受侵蚀形成微小坑和裂隙的原因之一。     

Dependency structure language model for topic detection and tracking

In this paper, we propose a new language model, namely, a dependency structure language model, for topic detection and tracking (TDT) to compensate for weakness of unigram and bigram language models. The dependency structure language model is based on the Chow expansion theory and the dependency parse tree generated by a linguistic parser. So, long-distance dependencies can be naturally captured by the dependency structure language model. We carried out extensive experiments to verify the proposed model on topic tracking and link detection in TDT. In both cases, the dependency structure language models perform better than strong baseline approaches.     

Parsimonious translation models for information retrieval

In the KL divergence framework, the extended language modeling approach has a critical problem of estimating a query model, which is the probabilistic model that encodes the user’s information need. For query expansion in initial retrieval, the translation model had been proposed to involve term co-occurrence statistics. However, the translation model was difficult to apply, because the term co-occurrence statistics must be constructed in the offline time. Especially in a large collection, constructing such a large matrix of term co-occurrences statistics prohibitively increases time and space complexity. In addition, reliable retrieval performance cannot be guaranteed because the translation model may comprise noisy non-topical terms in documents. To resolve these problems, this paper investigates an effective method to construct co-occurrence statistics and eliminate noisy terms by employing a parsimonious translation model. The parsimonious translation model is a compact version of a translation model that can reduce the number of terms containing non-zero probabilities by eliminating non-topical terms in documents. Through experimentation on seven different test collections, we show that the query model estimated from the parsimonious translation model significantly outperforms not only the baseline language modeling, but also the non-parsimonious models.     

An empirical study of query expansion and cluster-based retrieval in language modeling approach

The term mismatch problem in information retrieval is a critical problem, and several techniques have been developed, such as query expansion, cluster-based retrieval and dimensionality reduction to resolve this issue. Of these techniques, this paper performs an empirical study on query expansion and cluster-based retrieval. We examine the effect of using parsimony in query expansion and the effect of clustering algorithms in cluster-based retrieval. In addition, query expansion and cluster-based retrieval are compared, and their combinations are evaluated in terms of retrieval performance by performing experimentations on seven test collections of NTCIR and TREC.     

Campus commons: The calculus of grief

Lee Burdette Williams shares her firsthand experience of her students' resilience in the face of loss.