A modern congestion pricing policy for urban traffic: subsidy plus toll

Railway Engineering Science - Congestion pricing is seen as an effective policy to address traffic congestion. In such policies where money, people and authorities are involved, the success...     

Zinc Oxide/Graphene Oxide as a Robust Active Catalyst for Direct Oxidative Synthesis of Nitriles from Alcohols in Water

Catalysis Letters - In this work, without using any linker or chemical modification of graphene oxide, a zinc oxide immobilized graphene oxide-based catalyst was used for the direct aerobic...     

Enhancing electrical properties of MWCNTs in immiscible blends of poly(methyl methacrylate) and styrene‐acrylonitrile copolymer by selective localization

Multiwalled carbon nanotubes (MWCNTs) were introduced into poly(methyl methacrylate) (PMMA) and styrene‐acrylonitrile copolymer (SAN) blends by melt mixing in an asymmetric miniature mixer (APAM). A composition of 70 wt% of PMMA and 30 wt% of SAN was mixed to create a co‐continuous morphology. Transmission electron microscopy images of ultra‐microtomed samples (70 nm in thickness) showed selective localization of MWCNTs inside the percolated SAN phase. The occurrence of the double percolation phenomenon resulted in lower electrical percolation thresholds of PMMA/SAN/MWCNT blends molded at high temperatures. Dielectric spectroscopy indicated a higher electrical permittivity for samples that were compression molded at 260°C. Due to the higher affinity of MWCNTs to SAN, there was a migration of MWCNTs into the SAN phase during the melt processing. Conductivity measurements revealed a significant decrease in electrical percolation threshold (0.4 wt%) for PMMA70/SAN30 blends compared with MWCNT‐filled SAN and MWCNT‐filled PMMA (ca. 0.8 wt%). POLYM. COMPOS., 37:1523–1530, 2016. © 2014 Society of Plastics Engineers     

Impact of various solid carriers and spray drying on pre/post compression properties of solid SNEDDS loaded with glimepiride: in vitro-ex vivo evaluation and cytotoxicity assessment

Development of self-nanoemulsifying drug delivery systems (SNEDDS) of glimepiride is reported with the aim to achieve its oral delivery. Lauroglycol FCC, Tween-80, and ethanol were used as oil, surfactant, and co-surfactant, respectively as independent variables. The optimized composition of SNEDDS formulation (F1) was 10% v/v Lauroglycol FCC, 45% v/v Tween 80, 45% v/v ethanol, and 0.005% w/v glimepiride. Further, the optimized liquid SNEDDS were solidified through spray drying using various hydrophilic and hydrophobic carriers. Among the various carriers, Aerosil 200 was found to provide desirable flow, compression, dissolution, and diffusion. Both, liquid and solid-SNEDDS have shown release of more than 90% within 10?min. Results of permeation studies performed on Caco-2 cell showed that optimized SNEDDS exhibited 1.54 times higher drug permeation amount and 0.57 times lower drug excretion amount than that of market tablets at 4?hours (p?p?>?.05, i.e. 0.74). The formulation was found stable with temperature variation and freeze thaw cycles in terms of droplet size, zeta potential, drug precipitation and phase separation. Crystalline glimepiride was observed in amorphous state in solid SNEDDS when characterized through DSC, PXRD, and FT-IR studies. The study revealed successful formulation of SNEDDS for glimepiride.     

A Hybrid Branch‐and‐Bound and Benders Decomposition Algorithm for the Network Design Problem

Given a set of candidate road projects associated with costs, finding the best subset with respect to a limited budget is known as the network design problem (NDP). The NDP is often cast in a bilevel programming problem which is known to be NP‐hard. In this study, we tackle a special case of the NDP where the decision variables are integers. A variety of exact solutions has been proposed for the discrete NDP, but due to the combinatorial complexity, the literature has yet to address the problem for large‐size networks, and accounting for the multimodal and multiclass traffic flows. To this end, the bilevel problem is solved by branch‐and‐bound. At each node of the search tree, a valid lower bound based on system optimal (SO) traffic flow is calculated. The SO traffic flow is formulated as a mixed integer, non‐linear programming (MINLP) problem for which the Benders decomposition method is used. The algorithm is coded on a hybrid and synchronized platform consisting of MATLAB (optimization engine), EMME 3 (transport planning module), MS Access (database), and MS Excel (user interface). The proposed methodology is applied to three examples including Gao's network, the Sioux‐Falls network, and a real size network representing the city of Winnipeg, Canada. Numerical tests on the network of Winnipeg at various budget levels have shown promising results.     

A new five-level inverter with reduced leakage current for photovoltaic system applications

A general growth is being seen in the use of renewable energy resources, and photovoltaic cells are becoming increasingly popular for converting green renewable solar energy into electricity. Since the voltage produced by photovoltaic cells is DC, an inverter is required to connect them to the grid with or without transformers. Transformerless inverters are often used for their low cost and low power loss, and light weight. However, these inverters suffer from leakage current in the system, a challenge that needs to be addressed. In this paper, a topology with two alternative connection models is presented to stabilize the common mode voltage and reduce the leakage current. The output voltage characteristic of the proposed inverter is five-level, which reduces the harmonic distortion in the output current compared to the two- and three-level inverters. The operation modes and output of the proposed topology are described and analyzed. The structures of the proposed inverter are simulated in MATLAB/Simulink and are compared with some well-known structures. Results show that the proposed structure with both connection models effectively reduces leakage current and improves grid current THD.     

Microsimulation of Freeway Ramp Merging Processes Under Congested Traffic Conditions

This paper describes a microsimulation program developed to study freeway ramp merging phenomena under congested traffic conditions. The results of extensive macroscopic and microscopic studies are used to establish a model for the behavior of merging drivers. A theoretical framework for modeling the ramp and freeway lag driver acceleration-deceleration behavior guided the model development. This methodology uses the stimuli-response psychophysical concept as a fundamental rule and is formulated as a modified form of the conventional car-following models. Data collected at the two merging points are used to calibrate the hypothesized ramp and freeway lag vehicle acceleration models. Drawing on this behavioral model, the freeway merging capacity simulation program (FMCSP) is developed to simulate actual traffic conditions. This model evaluates the capacity of a merging section for a given geometric design and flow condition. Validation of FMCSP is performed using the observed flow, vehicle trajectories, and lane-changing maneuvers. The simulation model is applied to investigate a variety of merging strategies. The results indicated that the FMCSP is capable of simulating the actual traffic conditions of congested freeway ramp merging sections and will aid in the development of traffic management strategies for complex freeway ramp merging areas.     

Simulation of safety: A review of the state of the art in road safety simulation modelling

Recent decades have seen considerable growth in computer capabilities, data collection technology and communication mediums. This growth has had considerable impact on our ability to replicate driver behaviour and understand the processes involved in failures in the traffic system. From time to time it is necessary to assess the level of development as a basis of determining how far we have come. This paper sets out to assess the state of the art in the use of computer models to simulate and assess the level of safety in existing and future traffic systems. It reviews developments in the area of road safety simulation models. In particular, it reviews computer models of driver and vehicle behaviour within a road context. It focuses on stochastic numerical models of traffic behaviour and how reliable these are in estimating levels of safety on the traffic network. Models of this type are commonly used in the assessment of traffic systems for capacity, delay and general performance. Adding safety to this assessment regime may allow more comprehensive assessment of future traffic systems. To date the models have focused primarily on vehicular traffic that is, cars and heavy vehicles. It has been shown that these models have potential in measuring the level of conflict on parts of the network and the measure of conflict correlated well with crash statistics. Interest in the prediction of crashes and crash severity is growing and new models are focusing on the continuum of general traffic conditions, conflict, severe conflict, crash and severe crashes. The paper also explores the general data types used to develop, calibrate and validate these models. Recent technological development in in-vehicle data collection, driver simulators and machine learning offers considerable potential for improving the behavioural base, rigour and application of road safety simulation models. The paper closes with some indication of areas of future development.     

Emissions from large-scale medium-speed diesel engines: 1. Influence of engine operation mode and turbocharger

The operation of four – stroke diesel engines in either propulsion or generator mode application has a strong influence on gaseous, smoke (soot) and particulates emissions. Tests were made with a supercharged after-cooled large-scale diesel engine (mean speed  500 rpm, power per cylinder  1 MW) burning mainly heavy fuel oil. Gaseous emissions (NOx, CO, HC) were measured according to the IMO technical code, smoke (soot) emissions were determined optically and particulate matter (PM) was measured using a gravimetric impactor for five size fractions. Impact on gaseous emissions, smoke (soot) and PM was found when analysing the effects of the engine operating mode, fuel nozzle, start of injection (SOI), and load (speed). Results show that the exhaust emission was also highly dependent on the engine turbocharger system, especially the by-pass control, but was not affected by waste gate control. The gaseous and soot emissions were less for the generator mode in the total load region, decreasing with the load. PM emissions were found to decrease with the load for the propulsion mode, while showing an increase with the load for the generator mode.