高速公路施工区车辆强制换道耗时生存模型

为研究高速公路施工区车辆强制换道行为及其影响因素,采用生存分析中的半参数分析方法建立强制换道耗时的乘法风险率模型.通过无人机拍摄采集高速公路施工区的车辆换道耗时及其影响因素数据,最终确立换道耗时Cox比例风险模型,对换道耗时数据进行Cox回归建模分析.结果表明:近77%的换道车辆在10 s内完成换道;小型车和中型车经养护施工区的换道耗时未发现显著性差别;对于相同的换道耗时,平峰期的累积生存率明显低于高峰期和过渡时期,而高峰期的累积生存率最高.建立的强制换道耗时生存模型可有效的定量分析车型和交通时段对高速公路施工区车辆换道行为的影响,可为高速公路施工区交通管理控制及车辆换道行为建模及仿真奠定一定的理论基础.     

Near infrared light-induced disassembly of polymeric micelles based on methylene blue conjugated polyethylene glycol

Light-sensitive drug delivery systems are considered ideal for applications in the biomedical fields for their ability to release the payload in an on-demand spatiotemporal controlled manner through the manipulation of the light source. Among the broad radiation spectrum, near infrared (NIR) light is considered advantageous compared to UV and visible light, due to its inherently lower photodamage to normal tissues and deeper penetration to lesion areas. In this study, we report a successful synthesis of a polymer capable of undergoing partial degradation upon irradiation with NIR light by conjugating 10-N-carbamoyl linkage methylene blue (MB) moiety, a NIR photocleavable ligand, with polyethylene glycol (PEG). Through effective coupling of MB, a hydrophobic moiety, to the hydrophilic PEG molecule, an amphiphilic polymer was synthesized, as demonstrated by a lowered surface tension (55 mN/m at 0.1% wt/vol). Subsequently, photo-induced reversal of surface activity associated with self-assembled structure disruption, was displayed by surface tension measurements, size distribution analysis, and burst release profile of paclitaxel (PTX) from polymeric micelles upon the exposure to NIR irradiation.     

Preparation and cell-materials interactions of plasma sprayed strontium-containing hydroxyapatite coating

The use of strontium-containing hydroxyapatite (Sr-HA) as a biomaterial has been reported recently. In vitro and in vivo studies have shown that Sr-HA promotes osteoblast response and stimulates new bone formation. In order to extend its usage to major load-bearing applications, such as artificial hip replacement, it has been proposed that the material could be used in the form of a coating on implant surfaces. This paper reports a preliminary study of biocompatibility of plasma sprayed Sr-HA coatings on a metallic substrate. Coatings of Sr-HA containing 10 mol% Sr²⁺ was produced on titanium alloy substrates. The coating exhibited good bonding with the substrate. The bioactivity of Sr-HA coating was evaluated in vitro by immersion in simulated body fluid (SBF). After immersion in SBF, Sr-HA coating exhibited great ability to induce apatite precipitation on its surface. The possible effects of cell-materials interactions of Sr-HA coating were examined by culturing osteoprecursor cells (OPC1) on coating surfaces. The effect of Sr-HA was also compared to a hydroxyapatite (HA) coating, which is widely used in orthopedics and dentistry. The results indicated that Sr-HA coating had good biocompatibility with human osteoblasts. OPC1 cells survived and proliferated well on the surface of coating. Sr-HA coating promoted OPC1 cells attachment, and more local contacts were produced on the surface. The presence of Sr stimulated OPC1 cell differentiation and ALP expression. No deleterious effect on ECM formation and mineralization was found with Sr-HA coating. The results indicated that Sr-HA coating had good mechanical properties and bioactivity in vitro.     

Multi-objective reactive power market clearing in competitive electricity market using HFMOEA

This paper presents an application of a hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA) for solving a highly constraint, mixed integer type, complex multi-objective reactive power market clearing (RPMC) problem for the competitive electricity market environment. In HFMOEA based multi-objective optimization approach, based on the output of a fuzzy logic controller crossover and mutation probabilities are varied dynamically. It enhances stochastic search capabilities of HFMOEA. In multi-objective RPMC optimization framework, two objective functions namely the total payment function (TPF) for reactive power support from generators and synchronous condensers and the total real transmission loss (TRTL) are minimized simultaneously for clearing the reactive power market. The proposed HFMOEA based multi-objective RPMC scheme is tested on a standard IEEE 24 bus reliability test system and its performance is compared with five other multi-objective evolutionary techniques such as MOPBIL, NSGA-II, UPS-EMOA and SPEA-2 and a new extended form of NSGA (ENSGA-II). Applying all these six evolutionary techniques, a detailed statistical analysis using T-test and boxplots is carried out on three performance metrics (spacing, spread and hypervolume) data for RPMC problem. The obtained simulation results confirm the overall superiority of HFMOEA to generate better Pareto-optimal solutions with higher convergence rate as compared to above mentioned algorithms. Further, TPF and TRTL values corresponding to the best compromise solutions are obtained using said multi-objective evolutionary techniques. These values are compared with one another to take better market clearing decisions in competitive electricity environment.     

Liquid flow through a diverging microchannel

In this work, experiments and three-dimensional numerical calculations of fluid flow through diverging microchannels were carried out with the aim of bringing out differences between flow in uniform and nonuniform passages. Deionized water was used as the working fluid in the experiments where the effects of mass flow rate (8.33 × 10^?6 to 8.33 × 10^?5 kg/s), microchannel hydraulic diameter (118–177 µm), length (10–30 mm) and divergence angle (4°–16°) on pressure drop were studied. The results are analyzed in detail with the help of numerical data. The pressure drop exhibits a linear dependence on the mass flow rate, whereas it is inversely proportional to the divergence angle and square of the hydraulic diameter. The pressure drop increases anomalously at 16°, suggesting that flow reversal occurs between 12° and 16°, which agrees with the corresponding value at the conventional scale. For the purpose of predicting pressure drop using straight microchannel theory, an equivalent hydraulic diameter was defined. It is observed that the equivalent hydraulic diameter, located at one-third of the diverging microchannel length from the inlet, becomes mostly independent of the mass flow rate, microchannel hydraulic diameter, length and divergence angle. The pressure drop for a diverging microchannel becomes equal to an equivalent hydraulic diameter uniform cross-section microchannel, suggesting that conventional correlations for straight microchannels can also be applied to diverging microchannels. The data presented in this work are of fundamental importance and can help in optimization of diffuser design used for example in valveless micropumps.     

On semantic annotation of decision models

The growth of service sector in recent years has led to renewed research interests in the design and management of service systems. Decision support systems (DSS) play an important role in supporting this endeavor, through management of organizational resources such as models and data, thus forming the “back stage” of service systems. In this article, we identify the requirements for semantically annotating decision models and propose a model representation scheme, termed Semantically Annotated Structure Modeling Markup Language (SA-SMML) that extends Structure Modeling Markup Language (SMML) by incorporating mechanisms for linking semantic models such as ontologies that represent problem domain knowledge concepts. This model representation format is also amenable to a scalable Service-Oriented Architecture (SOA) for managing models in distributed environments. The proposed model representation technique leverages recent advances in the areas of semantic web, and semantic web services. Along with design considerations, we demonstrate the utility of this representation format with an illustrative usage scenarios with a particular emphasis on model discovery and composition in a distributed environment.     

Robust real-time unusual event detection using multiple fixed-location monitors

We present a novel algorithm for detection of certain types of unusual events. The algorithm is based on multiple local monitors which collect low-level statistics. Each local monitor produces an alert if its current measurement is unusual, and these alerts are integrated to a final decision regarding the existence of an unusual event. Our algorithm satisfies a set of requirements that are critical for successful deployment of any large-scale surveillance system. In particular it requires a minimal setup (taking only a few minutes) and is fully automatic afterwards. Since it is not based on objects' tracks, it is robust and works well in crowded scenes where tracking-based algorithms are likely to fail. The algorithm is effective as soon as sufficient low-level observations representing the routine activity have been collected, which usually happens after a few minutes. Our algorithm runs in realtime. It was tested on a variety of real-life crowded scenes. A ground-truth was extracted for these scenes, with respect to which detection and false-alarm rates are reported.     

Optimizing one-shot learning with binary synapses

A network of excitatory synapses trained with a conservative version of Hebbian learning is used as a model for recognizing the familiarity of thousands of once-seen stimuli from those never seen before. Such networks were initially proposed for modeling memory retrieval (selective delay activity). We show that the same framework allows the incorporation of both familiarity recognition and memory retrieval, and estimate the network's capacity. In the case of binary neurons, we extend the analysis of Amit and Fusi (1994) to obtain capacity limits based on computations of signal-to-noise ratio of the field difference between selective and non-selective neurons of learned signals. We show that with fast learning (potentiation probability approximately 1), the most recently learned patterns can be retrieved in working memory (selective delay activity). A much higher number of once-seen learned patterns elicit a realistic familiarity signal in the presence of an external field. With potentiation probability much less than 1 (slow learning), memory retrieval disappears, whereas familiarity recognition capacity is maintained at a similarly high level. This analysis is corroborated in simulations. For analog neurons, where such analysis is more difficult, we simplify the capacity analysis by studying the excess number of potentiated synapses above the steady-state distribution. In this framework, we derive the optimal constraint between potentiation and depression probabilities that maximizes the capacity.     

A methodology to incorporate product mix variations in cellular manufacturing

The identification of part families and machine groups that form the cells is a major step in the development of a cellular manufacturing system and, consequently, a large number of concepts, theories and algorithms have been proposed. One common assumption for most of these cell formation algorithms is that the product mix remains stable over a period of time. In today’s world, the market demand is being shaped by consumers resulting in a highly volatile market. This has given rise to a new class of products characterized by low volume and high variety. To incorporate product mix changes into an existing cellular manufacturing system many important issues have to be tackled. In this paper, a methodology to incorporate new parts and machines into an existing cellular manufacturing system has been presented. The objective is to fit the new parts and machines into an existing cellular manufacturing system thereby increasing machine utilization and reducing investment in new equipment.