Wind flow patterns about buildings

A knowledge of building wake characteristics is useful in a variety of applications including dispersion of pollutants downwind of conventional or nuclear power plants, airport runway interference effect, take-off/landing limitations at heliports, pedestrian wind comfort, and wind loads on structures. Recent investigations have advanced both understanding of physical flow processes occurring in the near and far wake regions and theoretical predictive capability for flow and diffusion in the far wake region. These studies have shown, for example, that separation cavities immediately downwind are not enclosed by free streamlines as in two-dimensional separation/reattachment, that organized vortices can play an important role in wake development, and that theoretical solutions for some wake regions are possible.     

Patients' awareness of stroke signs, symptoms, and risk factors

BACKGROUND AND PURPOSE: We sought to determine knowledge at the time of symptom onset regarding the signs, symptoms, and risk factors of stroke in patients presenting to the emergency department with potential stroke. METHODS: Patients admitted from the emergency department with possible stroke were identified prospectively. A standardized, structured interview with open-ended questions was performed within 48 hours of symptom onset to assess patients' knowledge base concerning stroke signs, symptoms, and risk factors. RESULTS: Of the 174 eligible patients, 163 patients were able to respond to the interview questions. Of these 163 patients, 39% (63) did not know a single sign or symptom of stroke. Unilateral weakness (26%) and numbness (22%) were the most frequently noted symptoms. Patients aged > or = 65 years were less likely to know a sign or symptom of stroke than those aged < 65 years (percentage not knowing a single sign or symptom, 47% versus 28%, P = .016). Similarly, 43% of patients did not know a single risk factor for stroke. The elderly were less likely to know a risk factor than their younger counterparts. CONCLUSIONS: Almost 40% of patients admitted with a possible stroke did not know the signs, symptoms, or risk factor of a stroke. Further public education is needed to increase awareness of the warning signs and risk factors of stroke.     

Polymer-Based Ion Trap Chemical Sensor

Ion traps are widely used in chemical analysis, and they are especially important in current attempts to miniaturize mass spectrometers to create portable instruments. The ultimate aim is to build a handheld device that would require a smaller mass analyzer. To accomplish this task, a robust precision fabrication procedure is desired. In this paper, the authors report a new approach to fabricating ion traps using stereolithography apparatus (SLA), which provides precision monolithic fabrication. An SLA-fabricated rectilinear ion trap, which employs a very simple electrode geometry, is shown to provide detection capabilities within a useful mass range encompassing that of interest in the detection of numerous volatile organic compounds, including those relevant to homeland security applications. Single small ion traps and integrated trap arrays can be made through this approach, which allows higher operating pressures and reduced power requirements     

Space and time efficient parallel algorithms and software for EST clustering

Expressed sequence tags, abbreviated as ESTs, are DNA molecules experimentally derived from expressed portions of genes. Clustering of ESTs is essential for gene recognition and for understanding important genetic variations such as those resulting in diseases. We present the algorithmic foundations and implementation of PaCE, a parallel software system we developed for large-scale EST clustering. The novel features of our approach include 1) design of space-efficient algorithms to limit the space required to linear in the size of the input data set, 2) a combination of algorithmic techniques to reduce the total work without sacrificing the quality of EST clustering, and 3) use of parallel processing to reduce runtime and facilitate clustering of large data sets. Using a combination of these techniques, we report the clustering of 327,632 rat ESTs in 47 minutes, and 420,694 Triticum aestivum ESTs in 3 hours and 15 minutes, using a 60-processor IBM xSeries cluster. These problems are well beyond the capabilities of state-of-the-art sequential software. We also present thorough experimental evaluation of our software including quality assessment using benchmark Arabidopsis EST data.     

沉默的文化——妇女的住房和土地权利

我们即将对第四届妇女大会（北京）召开十年以来的妇女发展状况进行回顾，并且要对千年发展目标的执行情况进行中期评估。因此，我们有必要对那些影响妇女日常生活的关键问题进行审视并采取相应的行动，这些问题一直被各国政府和国际社会所忽视。     

Coagulation Signaling through PAR1 as a Therapeutic Target in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly fatal disease with a 5-year survival rate of less than 10% following diagnosis. The aggressive and invasive properties of pancreatic cancer tumors coupled with poor diagnostic options contribute to the high mortality rate since most patients present with late-stage disease. Accordingly, PDAC is linked to the highest rate of cancer-associated venous thromboembolic disease of all solid tumor malignancies. However, in addition to promoting clot formation, recent studies suggest that the coagulation system in PDAC mediates a reciprocal relationship, whereby coagulation proteases and receptors promote PDAC tumor progression and dissemination. Here, upregulation of tissue factor (TF) by tumor cells can drive local generation of the central coagulation protease thrombin that promotes cell signaling activity through protease-activated receptors (PARs) expressed by both tumor cells and multiple stromal cell subsets. Moreover, the TF-thrombin-PAR1 signaling axis appears to be a major mechanism of cancer progression in general and PDAC in particular. Here, we summarize the current literature regarding the role of PAR1 in PDAC and review possibilities for pharmacologically targeting PAR1 as a PDAC therapeutic approach.     

UAV Path Following in Windy Urban Environments

This paper considers UAV path following in cluttered environments under windy conditions. Unstructured wind patterns in cluttered environments can make path following difficult resulting in high errors and possibly collisions with buildings. Combining a pursuit guidance law philosophy with a line-of-sight guidance law, we develop a novel guidance law that has low computational complexity and can track straight line paths, circular paths, a combination of both and waypaths accurately in the presence of wind blowing as high as fifty percent of the UAV’s air speed. Performance of the guidance law is demonstrated through numerical simulations.     

Solution to profit based unit commitment problem using particle swarm optimization

In this paper, an algorithm to solve the profit based unit commitment problem (PBUCP) under deregulated environment has been proposed using Particle Swarm Optimization (PSO) intelligent technique to maximize the GENCOs profit. Deregulation in power sector increases the efficiency of electricity production and distribution, offer lower prices, higher quality, a secure and a more reliable product. The proposed algorithm has been developed from the view point of a generation company wishing to maximize its profit in the deregulated power and reserve markets. UC schedule depends on the market price in the deregulated market. In deregulated environment utilities are not required to meet the demand. GENCO can consider a schedule that produce less than the predicted load demand and reserve but creates maximum profit. More number of units are committed when the market price is higher. When more number of generating units are brought online more power is generated and participated in the deregulated market to get maximum profit. This paper presents a new approach of GENCOs profit based unit commitment using PSO technique in a day ahead competitive electricity markets. The profit based unit commitment problem is solved using various PSO techniques such as Chaotic PSO (CPSO), New PSO (NPSO) and Dispersed PSO (DPSO) and the results are compared. Generation, spinning reserve, non-spinning reserve, and system constraints are considered in proposed formulation. The proposed approach has been tested on IEEE-30 bus system with 6 generating units as an individual GENCO. The results obtained are quite encouraging and useful in deregulated market. The algorithm and simulation are carried out using Matlab software.     

An ontology based multi-agent system to support HABIO outsourcing framework

In recent years the widespread application of outsourcing to help improve finance and maintain competitiveness is growing rapidly, and has been accompanied by a growth of academic research in this field. Most of the outsourcing literature has focused primarily on the social and cultural impacts, the organisational and management perspectives of outsourcing and very few tools have been developed to assist managers in making outsourcing decisions. Incorrect decision-making in outsourcing can lead to devastating consequences for their business. To guide decision makers in managing the complexity of outsourcing a holistic approach to outsourcing has been developed by taking business, information and organisational perspectives (HABIO). The purpose of this paper is to demonstrate how HABIO can be applied to help managers evaluate the impact of their decision-making by providing them with a set of ‘what-if’ scenarios, which are enacted by intelligent agents. The case example, discussed in this paper, demonstrates that HABIO can be applied effectively using a multi-agent system to analyse the outsourcing decision for a call centre sector in the retail industry. A prototype of the multi-agent system is presented where intelligent agents are endowed with specific domain ontologies, appropriate to solve a specific problem, and with a reasoning mechanism to achieve specific goals and to collaborate together to produce a set of “what-if scenarios” to the outsourcing managers.     

Fractional-step dimensionality reduction

Linear projections for dimensionality reduction, computed using linear discriminant analysis (LDA), are commonly based on optimization of certain separability criteria in the output space. The resulting optimization problem is linear, but these separability criteria are not directly related to the classification accuracy in the output space. Consequently, a trial and error procedure has to be invoked, experimenting with different separability criteria that differ in the weighting function used and selecting the one that performed best on the training set. Often, even the best weighting function among the trial choices results in poor classification of data in the subspace. In this short paper, we introduce the concept of fractional dimensionality and develop an incremental procedure, called the fractional-step LDA (F-LDA) to reduce the dimensionality in fractional steps. The F-LDA algorithm is more robust to the selection of weighting function and for any given weighting function, it finds a subspace in which the classification accuracy is higher than that obtained using LDA