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This paper aims to report a novel preparation method of ZnO particles with different structures and their photocatalytic activity.

ZnO powders are prepared by a facile, economical and environment-friendly aqueous solution route. X-ray diffraction, field emission scanning electron microscopy, energy dispersive spectroscopy, transmission electron microscopy and UV-vis diffuse reflectance spectra are used to characterize the products. Photocatalytic activity of the samples is evaluated by degradation of organic pollutant pentachlorophenol under UV-vis irradiation.

It is found that three-dimensional ZnO hierarchical structures can be prepared via aqueous solution route without using any template or structure-directing agent, and the alkalinity of reaction solution is the key factor. All the as-prepared ZnO products have good catalytic activity under UV-vis light irradiation.

This report presents a simple method for the preparation of ZnO particles with excellent photocatalytic activity. Experimental results could provide useful reference for the treatment of chlorophenols in the future.

This paper aims to report a novel preparation method of titanium dioxide (TiO₂)/zinc oxide (ZnO) composites with different mole ratios of TiO₂:ZnO and their photocatalytic activity.

TiO₂/ZnO composites are prepared by a facile route. X-ray diffraction (XRD), field-emission scanning electron microscopy (FESEM) and ultra-violet–visible diffuse reflectance spectra (UV-vis DRS) are used to characterize the products. Photocatalytic activity of the samples is evaluated by degradation of persistent organic pollutant pentachlorophenol under ultra-violet (UV) irradiation.

It is found that all the as-prepared TiO₂/ZnO composites not only have good catalytic activity under UV light irradiation, but also have excellent circulation stability. The optimal mole ratio of TiO₂:ZnO is 0.75:1.

This report presents a simple and rapid method for the preparation of TiO₂/ZnO composites with excellent photocatalytic activity. Experimental results could provide useful reference for the treatment of chlorophenols in the future.

Using only zinc nitrate, ferric nitrate and sodium hydroxide as reactants, rod-like and flower-like α-Fe₂O₃/ZnO composites were prepared via a simple and rapid solution route by controlling the composition of precursor solution. The products were characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM) and UV-vis diffuse reflectance spectra (UV-vis DRS). Photocatalytic activity of the as-prepared α-Fe₂O₃/ZnO composites was evaluated by degradation of methyl orange (MO) under simulated solar light. The results indicated that both α-Fe₂O₃/ZnO composites possess higher photocatalytic activity than commercial P25 TiO₂. In addition, possible reasons why α-Fe₂O₃/ZnO composites have excellent photocatalytic performance were discussed.

The paper aims to investigate the optimal service quality and pricing for a mobile application (App) service supply chain (SSC) and analyze the impact of network externality on App SSC members' utilities. After that, the corresponding management inspirations and suggestions are put forward.

The paper developed a SSC consisted of an App service supplier and an App service operator. Our models aim to maximize the SSC members' utilities. By utilizing the game theory, equilibrium solutions are obtained. Numerical examples are used to manifest the impact of parameters on decisions by Matlab. Some management enlightenment could be obtained by comparison analysis.

Cooperating with an App service operator that asks for a lower revenue sharing ratio will enable the App service supplier to have sufficient funds to provide high-quality update service. With the increase of network externality, adopting a high-quality service strategy can bring higher utility to the App service operator and users. Pouring attention into consumer welfare moderately will improve the App service supplier's utility. Scenario CRS can achieve a win–win goal for App SSC members and consumers.

The innovations of this paper are as follows: Firstly, the authors investigate the optimal service quality and pricing for the App SSC, which has been discussed little in previous literature. Secondly, the authors discuss how network externality and enterprises' attention to consumer welfare affect the optimal decisions and utilities of App service supply chain members. Thirdly, this paper considers four different circumstances and determines the optimal operation scenario for App SSC through comparative analysis.

Because of the properties of loess, the occurrence of collapse following deformation of a large settlement is a common problem during the excavation of tunnels on loess ground. Hence, risk management for safer loess tunnel construction is of great significance. The purpose of this paper is to explore the influence of factors on collapse risk of loess tunnels and establish a risk assessment model using rough set theory and extension theory.

The surrounding rock level, groundwater conditions, burial depth, excavation method and support close time were selected as the factors and settlement deformation was the verification index for risk assessment. First, using rough set theory, the influence of risk factors on the collapse risk of loess tunnels was calculated by researching engineering data of excavated sections. Then, a collapse risk assessment model was developed based on extension theory. As the final step, the model was applied to practical engineering in the Loess Plateau of China.

The weights of surrounding rock level, groundwater conditions, burial depth, excavation method and support close time obtained using rough set theory were respectively 10.811 per cent, 18.919 per cent, 24.324 per cent, 40.541 per cent and 5.406 per cent. The assessment results obtained using the model were in good agreement with field observations.

This study highlights key points in collapse risk management of loess tunnels, which could be very useful for future construction methods. The model, using easily obtained parameters, helps in predicting the collapse risk level of loess tunnels excavated under different geological conditions and by different construction organizations and provides a reference for future studies.

This study aims to evaluate the effectiveness of different prefabricated construction (PC) policies using a case study in Wuhan, considering the local context.

The effectiveness of PC policies is falling behind expectations. The main reason lies in an insufficient understanding of the policy impacts. An agent-based model was built by choosing the residential sector in a typical large city of Wuhan, China, as the study case. Different cost reduction scenarios were introduced for investigating the PC policy effectiveness. The proposed model and simulation approach can be used for other cities and generalized to the whole Chinese PC industry with the potential to include more local policies and corresponding data.

Simulation results show that carbon emission reduction will be between 60,000 and 80,000 tons with policy incentives, nearly double that of the no policy intervention scenario. The target of 30% PC in all new buildings by 2026 in China is achievable with the subsidy policies of linear cost reduction, or cost reduction conforms to the learning curve.

Simulation results of three kinds of policy show that subsidy policy optimization is necessary regarding reducing the level of subsidy needed. The carbon credit policy is not essential since it has little influence on PC development. Implementing the project procurement restriction policy is not recommended if the scale of development of PC is more important than achieving the development target.

This study can help the government and developers make better policy and strategic decisions on PC development and boost the sustainability transition of the construction industry.

A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran.

In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted.

The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed.

The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient’s condition becomes more dangerous.