Intelligent drug delivery system using UML diagrams analysis

A novel intelligent drug delivery system potential for the more effective therapy of the diabeticswas proposed, and the composition of system was analyzed. Based on the design of micro-electro-mechanicalsystems (MEMS), an iterative modeling process was introduced. Unified modeling language (UML) was em-ployed to describe the function requirement, and different diagrams were built up to explore the static model,the dynamic model and the employment model. The mapping analysis of different diagrams can simply verifythe consistency and completeness of the system model.     

Using high-resolution event-based data for traffic modeling and control: An overview

Research on using high-resolution event-based data for traffic modeling and control is still at early stage. In this paper, we provide a comprehensive overview on what has been achieved and also think ahead on what can be achieved in the future. It is our opinion that using high-resolution event data, instead of conventional aggregate data, could bring significant improvements to current research and practices in traffic engineering. Event data records the times when a vehicle arrives at and departs from a vehicle detector. From that, individual vehicle’s on-detector-time and time gap between two consecutive vehicles can be derived. Such detailed information is of great importance for traffic modeling and control. As reviewed in this paper, current research has demonstrated that event data are extremely helpful in the fields of detector error diagnosis, vehicle classification, freeway travel time estimation, arterial performance measure, signal control optimization, traffic safety, traffic flow theory, and environmental studies. In addition, the cost of event data collection is low compared to other data collection techniques since event data can be directly collected from existing controller cabinet without any changes on the infrastructure, and can be continuously collected in 24/7 mode. This brings many research opportunities as suggested in the paper.     

Improving rail network velocity: A machine learning approach to predictive maintenance

Rail network velocity is defined as system-wide average speed of line-haul movement between terminals. To accommodate increased service demand and load on rail networks, increase in network velocity, without compromising safety, is required. Among many determinants of overall network velocity, a key driver is service interruption, including lowered operating speed due to track/train condition and delays caused by derailments. Railroads have put significant infrastructure and inspection programs in place to avoid service interruptions. One of the key measures is an extensive network of wayside mechanical condition detectors (temperature, strain, vision, infrared, weight, impact, etc.) that monitor the rolling-stock as it passes by. The detectors are designed to alert for conditions that either violate regulations set by governmental rail safety agencies or deteriorating rolling-stock conditions as determined by the railroad.Using huge volumes of historical detector data, in combination with failure data, maintenance action data, inspection schedule data, train type data and weather data, we are exploring several analytical approaches including, correlation analysis, causal analysis, time series analysis and machine learning techniques to automatically learn rules and build failure prediction models. These models will be applied against both historical and real-time data to predict conditions leading to failure in the future, thus avoiding service interruptions and increasing network velocity. Additionally, the analytics and models can also be used for detecting root cause of several failure modes and wear rate of components, which, while do not directly address network velocity, can be proactively used by maintenance organizations to optimize trade-offs related to maintenance schedule, costs and shop capacity. As part of our effort, we explore several avenues to machine learning techniques including distributed learning and hierarchical analytical approaches.     

Including passengers’ response to crowding in the Dutch national train passenger assignment model

Transit passengers’ response to crowded conditions has been studied empirically, yet is limitedly included in transport models currently used in the design of policy and infrastructure investments. This has consequences for the practical applicability of these models in studies on, for instance, timetabling, train capacity management strategies, project appraisal, and passenger satisfaction. Here we propose four methods to include the effect of crowding, based on existing studies on passengers’ perception and response as well as often-used crowding indicators. These four alternative methods are implemented in the train passenger assignment procedure of the Dutch national transport model, and evaluated with respect to their impacts on the model results for the Dutch railway network. The four methods relate to four different ways in which an additive trip penalty and/or time-multiplier can be incorporated in the train utility function for different travel purposes, to capture the disutility of crowding as measured by the load factor. The analyses of the test case favor the hybrid method using both a boarding penalty (capturing seat availability upon boarding) and a time-multiplier (capturing physical comfort and safety throughout the trip). This method produces consistent results, while the additional computational effort that it imposes is acceptable. Further empirical underpinning is needed to conclusively show which of these methods best captures passengers’ response behavior quantitatively (for different travel purposes and conditions).     

船用螺旋桨三维建模方法研究

从分析船用螺旋桨三维模型主要几何特征入手,阐述其三维坐标计算方法。在探讨了螺旋桨某些特殊部位的处理方法后,运用三维模型设计软件,以某螺旋桨建模实例演绎其方法步骤。     

Simultaneous modeling of individuals’ duration and expenditure decisions in out-of-home leisure activities

In this paper, we propose an activity model under time and budget constraints to simultaneously predict the allocation of time and money to out-of-home leisure activities. The proposed framework considers the activity episode level, given that the activity is scheduled. Thus, the model considers the decision of the quantities for duration and expenditure spent during the activity. We use a flexible utility function and show how the simultaneous equations can be estimated by using structural equations model (SEM) estimation techniques to handle the endogeneity problem of time and expenditure. The estimation results are based on a large national leisure diary data set collected in 2008 in the Netherlands, which provides detailed information about time and money spent as well as timing and location attributes of the activities. The analysis reveals that socio-demographics, travel party, timing and location variables influence the duration and expenditure of activity episodes. It shows that various socio-demographic groups display different preferences in terms of the time and money spent on activities. The results also indicate substitution relationships between spending more time and money for various activity categories. Thus it is concluded that the analysis provides useful results for a better understanding of combined time and money allocation decisions for leisure activities.     

An integrated analysis of workers’ physically active activity and active travel choice behavior

Using Texas add-on sample data from the 2009 National Household Travel Survey, this study examines adult workers’ daily active choice decisions in the context of physical activity and attendant health benefits. The study looked at workers’ two choice behaviors: active activity and active travel. The first choice behavior, active activity, is developed as an ordered-response model based on the number of physically active recreational activities pursued during the workday. The second choice behavior, active travel, is developed as a binary-response model that examines workers’ active travel choices—whether or not the worker used any active mode of travel during the same workday. The study improves the understanding and knowledge of observed factors influencing workers’ physically active activity-travel behavior. The study also provides several observations regarding the role (and constraints) of employment in individuals’ active choices. Using a flexible copula modeling methodology, we explore the true correlation (or dependence) between the two behavior choices that could occur due to the presence of unobserved factors, suggesting a simultaneously low or simultaneously high propensity for being physically active across workers. The study findings suggest that transportation and public health policy makers can mutually benefit from encouraging workers to be physically active (from an activity and/or travel perspective). Overall, the study draws attention to the integrated nature of the public health and transportation fields, thereby providing a distinct view of active/inactive choice behavior. To our knowledge, this is the first study exploring a rich variety of components for workers’ active activity-travel behavior through a robust copula approach.     

基于多维数据库的联机处理技术在装备维修费管理中的应用

论文以装备维修费分析为应用背景,按照需求分析、概念设计、逻辑建模、系统实现的软件工程过程,对基于多维数据库的联机分析处理（OLAP）技术及其实现方法进行了研究.     

隧道岩爆防治作用机理初探

研究目的：采用离散单元法对几种常用的岩爆控制方法进行了数值模拟，旨在为岩爆防治设计提供理论依据，确保隧道施工安全。研究结论：数值模拟结果表明：注水围岩软化法是通过降低岩石弹性模量达到减少围岩内切向应力的目的，只适用于吸水性较强的围岩；而钻孔应力释放法是通过钻孔实现围岩内切向应力向内部围岩转移，适用于弱-较强岩爆；超前应力解除爆破法是通过超前爆破，更大程度地实现围岩内切向应力向内部围岩转移，适用于强岩爆洞段。     

A new approach for evaluating regional exposure to particulate matter emissions from motor vehicles

Exposure to fine particulate matter from vehicle exhaust is associated with increased health risk. This study develops a new approach for creating spatially detailed regional maps of fine particulate matter concentration from vehicle exhaust using a dispersion model to better evaluate these risks. The spatial extent, diurnal, and seasonal patterns of concentration fields across Los Angeles County, California are evaluated and population exposure and exposure equity by race and income are investigated. The results demonstrate how this modeling approach can create new knowledge about vehicle emissions exposure. This approach also provides a method for proactively screening out regional plans, or specific projects within these plans, that are likely to cause air quality concerns. A proactive and regional air quality assessment can identify potential problems earlier in the planning process and a wider range of solutions, saving time, money and protecting public health. The detailed concentration maps can also be used to improve the siting of regulatory air quality monitors and provide more accurate exposure data for epidemiology studies.