Kinetics of liquid phase semiconductor photoassisted reactions : supporting observations for a pseudo-steady-state model

The kinetics of liquid phase semiconductor photocatalytic and photoassisted reactions are an area of some debate, reignited recently by an article by Ollis1 in which he proposed a simple pseudo-steady-state model to interpret the Langmuir-Hinshelwood type kinetics, commonly observed in such systems. In the current article, support for this model, over other models, is provided by a reinterpretation of the results of a study, reported initially in 1999,2 of the photoassisted mineralization of 4-chlorophenol, 4-CP, by titania films and dispersions as a function of incident light intensity, I. On the basis of this model, these results indicate that 4-CP is adsorbed more strongly on P25 TiO2 when it is in a dispersed, rather than a film form, due to a higher rate constant for adsorption, k1. In addition, the kinetics of 4-CP removal appear to depend on Iâ, where â ) 1 or 0.6 for when the TiO2 is in a film or a dispersed form, respectively. These findings are discussed both in terms of the pseudo-steady-state model and other popular kinetic models

Freeway travel time estimation based on the general motors model: a genetic algorithm calibration framework

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166267/1/itr2bf00710.pd

A rapid method of assessing the photocatalytic activity of thin TiO2 films using an ink based on the redox dye 2,6-dichloroindophenol

An indicator ink based on the redox dye 2,6-dichloroindophenol ( DCIP) is described, which allows the rapid assessment of the activity of thin, commercial photocatalytic films, such as Activ. The ink works via a photoreductive mechanism, DCIP being reduced to dihydro-DCIP within ca. 7.5 minutes exposure to UVA irradiation of moderate intensity ( ca. 4.8mW cm(-2)). The kinetics of photoreduction are found to be independent of the level of dye present in the ink formulation, but are highly sensitive to the level of glycerol. This latter observation may be associated with a solvatochromic effect, whereby the microenvironment in which the dye finds itself and, as a consequence, its reactivity is altered significantly by small changes in the glycerol content. The kinetics of photoreduction also appear linearly dependent on the UVA light intensity with an observed quantum efficiency of ca. 1.8 x 10(-3)

A rapid method of assessing the photocatalytic activity of thin TiO2 films using an ink based on the redox dye 2,6-dichloroindophenol

An indicator ink based on the redox dye 2,6-dichloroindophenol ( DCIP) is described, which allows the rapid assessment of the activity of thin, commercial photocatalytic films, such as Activ. The ink works via a photoreductive mechanism, DCIP being reduced to dihydro-DCIP within ca. 7.5 minutes exposure to UVA irradiation of moderate intensity ( ca. 4.8mW cm(-2)). The kinetics of photoreduction are found to be independent of the level of dye present in the ink formulation, but are highly sensitive to the level of glycerol. This latter observation may be associated with a solvatochromic effect, whereby the microenvironment in which the dye finds itself and, as a consequence, its reactivity is altered significantly by small changes in the glycerol content. The kinetics of photoreduction also appear linearly dependent on the UVA light intensity with an observed quantum efficiency of ca. 1.8 x 10(-3)

The Relationship Between Investor Sentiment and Stock Market Volatility: Based on the VAR Model

Using web crawling technology crawls investors’ comments of SANY stock(Stock Code: 600031) and Fujian Expressway stock(Stock Code: 600033) from February 11, 2015 to August 16, 2017. Then using semi-supervised machine learning method construct investor sentiment index. Moreover, collecting the daily closing stock price and trading volume data from Qianlong software explore the relationship between investor sentiment and stock market volatility based on VAR model and Granger Test Method. The results show that the rate of return and trading volume have a two-way Granger causality, while negative emotion and the rate of return have a one-way Granger causality. Furthermore, with the impulse response function and variance decomposition, the results show that trading volume has significant effects on rate of return and negative emotions of investors have significant negative effects on rate of return and trading volume

COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning

Neural network compression empowers the effective yet unwieldy deep convolutional neural networks (CNN) to be deployed in resource-constrained scenarios. Most state-of-the-art approaches prune the model in filter-level according to the "importance" of filters. Despite their success, we notice they suffer from at least two of the following problems: 1) The redundancy among filters is not considered because the importance is evaluated independently. 2) Cross-layer filter comparison is unachievable since the importance is defined locally within each layer. Consequently, we must manually specify layer-wise pruning ratios. 3) They are prone to generate sub-optimal solutions because they neglect the inequality between reducing parameters and reducing computational cost. Reducing the same number of parameters in different positions in the network may reduce different computational cost. To address the above problems, we develop a novel algorithm named as COP (correlation-based pruning), which can detect the redundant filters efficiently. We enable the cross-layer filter comparison through global normalization. We add parameter-quantity and computational-cost regularization terms to the importance, which enables the users to customize the compression according to their preference (smaller or faster). Extensive experiments have shown COP outperforms the others significantly. The code is released at https://github.com/ZJULearning/COP.Comment: 7 pages, 4 figures, has been accepted by IJCAI201

A Robust Integrated Multi-Strategy Bus Control System via Deep Reinforcement Learning

An efficient urban bus control system has the potential to significantly reduce travel delays and streamline the allocation of transportation resources, thereby offering enhanced and user-friendly transit services to passengers. However, bus operation efficiency can be impacted by bus bunching. This problem is notably exacerbated when the bus system operates along a signalized corridor with unpredictable travel demand. To mitigate this challenge, we introduce a multi-strategy fusion approach for the longitudinal control of connected and automated buses. The approach is driven by a physics-informed deep reinforcement learning (DRL) algorithm and takes into account a variety of traffic conditions along urban signalized corridors. Taking advantage of connected and autonomous vehicle (CAV) technology, the proposed approach can leverage real-time information regarding bus operating conditions and road traffic environment. By integrating the aforementioned information into the DRL-based bus control framework, our designed physics-informed DRL state fusion approach and reward function efficiently embed prior physics and leverage the merits of equilibrium and consensus concepts from control theory. This integration enables the framework to learn and adapt multiple control strategies to effectively manage complex traffic conditions and fluctuating passenger demands. Three control variables, i.e., dwell time at stops, speed between stations, and signal priority, are formulated to minimize travel duration and ensure bus stability with the aim of avoiding bus bunching. We present simulation results to validate the effectiveness of the proposed approach, underlining its superior performance when subjected to sensitivity analysis, specifically considering factors such as traffic volume, desired speed, and traffic signal conditions

With Brexit, inward investment will fall in the UK

Supply chains cross borders many times before components go into a final product in any EU country, write David Bailey, Nigel Driffield and Michail Karoglo

Lateral and torsional vibrations of cable-guided hoisting system with eccentric load

Theoretical investigation of the lateral and torsional vibrations of the hoisting cage in the cable-guided hoisting system caused by the eccentric load and the flexibility of the guiding cable is presented in this paper. The assumed modes method (AMM) is adopted to discretize the hoisting cable and two guiding cables, then Lagrange equations of the first kind are used to derive the equations of motion, while the geometric relationships between the hoisting cage and the cables are accounted for by the Lagrangian multiplier. Considering all the geometric matching conditions are approximately linear, the differential algebraic equations (DAEs) are transformed to the ordinary differential equations (ODEs). The dynamic responses of the hoisting cage are calculated, and especially the lateral displacements of the guiding cable and the constraints forces at the interfaces are obtained. Preload plays a vital role in affecting the cage vibration, thus, the effects of the total preload and the tension difference are analyzed. The numerical results indicate increasing the total preload can decrease the vibration displacements, while the tension difference has little impact on the vibration but can obviously change the constraint forces. In addition, the vibration displacements are directly proportional to the eccentric load, but less sensitive to the hoisting mass