No abstract

Steel slag is a kind of alkaline mixture and considered to be a potential CO2 adsorbent. In this work, the CO2-trapping characteristics of two types of steel slag, basic oxygen furnace (BOF) steel slag and electric arc furnace (EAF) steel slag, were experimentally investigated. Generally, the higher the temperature, the larger the Ca use of slag. However, the Ca use at 550 °C would be lower than that at 500 °C for a certain CO2 concentration. The CO2 concentration also has an effect on the Ca use. At higher temperatures, a larger Ca use appears at a lower CO2 concentration (<10%) or higher CO2 concentration (>75%). As the CO2 concentration decreases, the reaction rate of carbonization increases, regardless of the kind of slag used. With regard to the type of steel slag, EAF steel slag is better than BOF steel slag in reactivity and Ca use. All of the results indicate that the carbonation reaction of steel slag is controlled by not only the reaction kinetics but also the diffusion of the reactive gas CO2. Steel slag has a capacity to capture and permanently sequester CO2. However, what is more important is that it can be used in different flue gases, where the CO2 concentration is typically lower (<20%) or fairly higher (>75%). This provides the steel slag a wide application market.

The applications of mobile devices are increasingly becoming computationally intensive while the computing capability of the user’s mobile device is limited. Traditional approaches offload the tasks of mobile applications to the remote cloud. However, the rapid growth of mobile devices has made it a challenge for the remote cloud to provide computing and storage capacities with low communication delays due to the fact that the remote cloud is geographically far away from mobile devices. Reducing the completion time of applications in mobile devices through the technical expending mobile cloudlets which are moving collocated with Access Points (APs) is necessary. To address the above issues, this paper proposes EACP-CA (Enhanced Adaptive Cloudlets Placement approach based on Covering Algorithm), an enhanced adaptive cloudlet placement approach for mobile applications in a given network area. We apply the CA (Covering Algorithm) to adaptively cluster the mobile devices based on their geographical locations, the aggregation regions of the mobile devices are identified, and the cloudlet destination locations are also confirmed according to the clustering centers. In addition, we can also obtain the traces between the original and destination locations of these mobile cloudlets. To increase the efficiency, we parallelize CA on Spark. Extensive experiments show that the proposed approach outperforms the existing approach in both effectiveness and efficiency.

With the rapid growth of web services on the Internet, it becomes more difficult for users who want to choose the high-quality web services from a large number of functionally equivalent candidate services. Therefore, the prediction of quality of service (QoS) values according to the history of web services has received extensive attention. In recent years, deep learning has achieved great success in speech recognition, image processing, and natural language understanding. However, it is rarely applied to the service recommendation field. Therefore, a novel approach for QoS prediction named NDL (neighborhood-aware deep learning) is proposed. NDL first gets the Top-k neighbors of the user and the service through the Pearson correlation coefficient according to the service QoS information. Then, it extracts the potential features of the user neighbor and the service neighbor; after that, it inputs the QoS values of the user and the user neighbor as well as the QoS values of the service and service neighbors as a convolutional neural network. The results of experiments conducted on a real-world dataset demonstrate that the NDL significantly outperforms the current QoS prediction method in prediction accuracy.

Summary As a finite and non‐renewable resource, phosphorus (P) is essential to all life and crucial for crop growth and food production. The boosted agricultural use and associated loss of P to the aquatic environment are increasing environmental pollution, harming ecosystems, and threatening future global food security. Thus, recovering and reusing P from water bodies is urgently needed to close the P cycle. As a natural, eco‐friendly, and sustainable reclamation strategy, microalgae‐based biological P recovery is considered a promising solution. However, the low P‐accumulation capacity and P‐removal efficiency of algal bioreactors restrict its application. Herein, it is demonstrated that manipulating genes involved in cellular P accumulation and signalling could triple the Chlamydomonas P‐storage capacity to ~7% of dry biomass, which is the highest P concentration in plants to date. Furthermore, the engineered algae could recover P from wastewater almost three times faster than the unengineered one, which could be directly used as a P fertilizer. Thus, engineering genes involved in cellular P accumulation and signalling in microalgae could be a promising strategy to enhance P uptake and accumulation, which have the potential to accelerate the application of algae for P recovery from the water body and closing the P cycle.