An Efficient Top-k Query Scheme Based on Multilayer Grouping

The top-k query is to find the k data that has the highest scores from a candidate dataset. Sorting is a common method to find out top-k results. However, most of existing methods are not efficient enough. To remove this issue, we propose an efficient top-k query scheme based on multilayer grouping. First, we find the reference item by computing the average score of the candidate dataset. Second, we group the candidate dataset into three datasets: winner set, middle set and loser set based on the reference item. Third, we further group the winner set to the second-layer three datasets according to k value. And so on, until the data number of winner set is close to k value. Meanwhile, if k value is larger than the data number of winner set, we directly return the winner set to the user as a part of top-k results almost without sorting. In this case, we also return the top results with the highest scores from the middle set almost without sorting. Based on above innovations, we almost minimize the sorting. Experimental results show that our scheme significantly outperforms the current classical method on the performance of memory consumption and top-k query

Efficient Q-Value Zero-Leakage Protection Scheme in SRS Regularly Publishing Private Data

Spontaneous Reporting System (SRS) has been widely established to collect adverse drug events. Thus, SRS promotes the detection and analysis of ADR (adverse drug reactions), such as the FDA Adverse Event Reporting System (FAERS). The SRS data needs to be provided to researchers. Meanwhile, the SRS data is publicly available to facilitate the study of ADR detection and analysis. In general, SRS data contains private information of some individual characteristics. Before the information is published, it is necessary to anonymize private information in the SRS data to prevent disclosure of individual privacy. There are many privacy protection methods. The most classic method for protecting SRS data is called as PPMS. However, in the real world, SRS data is growing dynamically and needs to be published regularly. In this case, PPMS has some shortcomings in the memory consumption, anonymity efficiency, data update and data security. To remove these shortcomings, we propose an Efficient Q-value Zero-leakage protection Scheme in SRS regularly publishing private data, called EQZS. EQZS can deal with almost all of potential attacks. Meanwhile, EQZS removes the shortcomings of PPMS. The experimental results show that our scheme EQZS solves the problem of privacy leakage in SRS regularly publishing private data. Meanwhile, EQZS significantly outperforms PPMS on the efficiency of memory consumption, privacy anonymity and data update

Optimization and Coordination of the Fresh Agricultural Product Supply Chain Considering the Freshness-Keeping Effort and Information Sharing

To solve freshness-keeping problems and analyse a retailer’s information sharing strategies in the fresh agricultural product supply chain (FAPSC), often confronted with challenges in keeping agri-products fresh in an uncertain market, we study an FAPSC via a decentralized mode in which the supplier or retailer exerts the freshness-keeping effort while the retailer decides its information sharing strategies regarding private demand forecasting. We consider a contract coordination mode including three incentive contracts, cost-sharing (cs), revenue-sharing (re) and revenue-and-cost-sharing (rc), to facilitate supply chain coordination. The results show that, as opposed to the case where the supplier takes on the freshness-keeping effort, the optimal freshness-keeping effort level, wholesale price and retail price are not only affected by the retailer’s information sharing strategy but also the freshness-keeping efficiency as the retailer exerts the freshness-keeping effort. Regarding the information sharing strategy, when the freshness-keeping effort is undertaken by the retailer, sharing information sometimes benefits the supplier; however, information sharing is never preferable for the retailer. Consequently, it is necessary to explore the supply chain coordination mode via effective incentive contracts which can improve the supplier and retailer’s profit. We also numerically analyze the effects of freshness-keeping efficiency on equilibrium decisions and expected profits in the decentralized mode, and the effects of the three contract parameters on the expected profits in equilibrium in the coordination mode

Recent advances in two-dimensional nanomaterials for bone tissue engineering

Over the last decades, bone tissue engineering has increasingly become a research focus in the field of biomedical engineering, in which biomaterials play an important role because they can provide both biomechanical support and osteogenic microenvironment in the process of bone regeneration. Among these biomaterials, two-dimensional (2D) nanomaterials have recently attracted considerable interest owing to their fantastic physicochemical and biological properties including great biocompatibility, excellent osteogenic capability, large specific surface area, and outstanding drug loading capacity. In this review, we summarize the state-of-the-art advances in 2D nanomaterials for bone tissue engineering. Firstly, we introduce the most explored biomaterials used in bone tissue engineering and their advantages. We then highlight the advances of cutting-edge 2D nanomaterials such as graphene and its derivatives, layered double hydroxides, black phosphorus, transition metal dichalcogenides, montmorillonite, hexagonal boron nitride, graphite phase carbon nitride, and transition metal carbonitrides (MXenes) used in bone tissue engineering. Finally, the current challenges and future prospects of 2D nanomaterials for bone tissue regeneration in process of clinical translation are discussed

Contributions of Machine Learning to Remote Sensing Data Analysis

reserved3simixedScheunders, P.; Tuia, D.; Moser, G.Scheunders, P.; Tuia, D.; Moser, G