Background: The Central China Rise Strategy is one of China’s important regional development strategies, and it has been officially implemented since 2006. Despite the obvious economic development resulting from the strategy, its impact on the environment remains unclear. Objective: Previous studies have focused more on the economic benefits of the Central China Rise Strategy while ignoring its environmental impact. This paper focuses on the environmental benefits and aims to promote the coordinated development between the economy and the environment. Methods: Panel data of 30 Chinese provinces from 2000 to 2017 were selected to construct a propensity score matching-difference-in-difference (PSM-DID) framework for systematic research that includes benchmark modeling, as well as dynamic effect and mechanism analyses. Results: 1) The benchmark model and placebo test proved that the Central China Rise Strategy had increased environmental pollution. 2) Dynamic effect analysis revealed that the impact of the Central China Rise Strategy on environmental pollution has gradually increased in the short-and medium-term, with 2012 exhibiting the greatest augmentation, significantly reducing from 2016 on-wards. 3) The mechanism of action considers three mechanisms. The level of economic development is a path through which the Central China Rise Strategy leads to the aggravation of environmental pollution while the city size is not. Foreign direct investment has improved environmental pollution. Conclusion: The Central China Rise Strategy has aggravated environmental pollution, especially in the short and medium-term. In view of the mechanism path, we put forward three targeted suggestions. In the future, we will study some of the limitations of this paper: more mechanisms of action will be considered, and the use of new technologies, such as neural networks, will be compared with our results. © 2023 Bentham Science Publishers.

Spark Streaming是一种典型的批量流式计算平台,可用于处理持续到达的数据流。流式数据最重要的两个特征是波动性和时效性。利用动态调整系统参数和动态调整资源满足不同数据到达速率的响应延迟,但调整参数的方式具有局限性,其用户成本较大。因此提出一种参数和资源协同调整策略,采用动态邻域粒子群算法找到一种满足SLO目标且使用资源最少的系统方案。实验表明,AdaStreaming与DyBBS相比,延迟性降低了70.1%,在资源使用量上比DRA降低了42.1%。

标签作为Web 2.0时代信息分类和检索的有效方式,已经成为近年的热点研究对象。标签推荐系统旨在利用标签数据为用户提供个性化推荐。现有的基于标签的推荐方法在预测用户对物品的兴趣度时往往倾向于赋予热门标签及其对应的热门物品较大的权重,导致权重偏差,降低了推荐结果的新颖性,未能充分反映用户个性化的兴趣。针对上述问题,定义了标签熵的概念来度量标签的不确定性,提出了标签熵特征表示的协同过滤个性化推荐算法。该算法通过引入标签熵来解决权重偏差问题,利用三分图形式描述用户-标签-项目之间的关系;构建基于标签熵特征表示的用户和项目特征表示,并通过特征相似性度量方法计算项目的相似性;最后利用用户标签行为和项目的相似性线性组合预测用户对项目的偏好值,并根据预测偏好值排序生成最终的推荐列表。在Last.fm数据集上的实验结果表明,该方法能够提高推荐准确性和新颖性,满足用户的个性化需求。

为了解决Docker容器在线迁移过程中由容器迁移产生冗余Docker镜像的问题,利用联合文件系统容器层文件与镜像层文件通过联合挂载统一显示以及容器内操作过的文件存在于容器层的特性,提出一个Docker容器在线迁移方案.该方案在迁移容器数据时,采用仅迁移容器层数据的方式,有效避免了冗余Docker镜像的生成.基于上述设计方案,在当前最具代表性的2类联合文件系统存储驱动（aufs和overlay2）上进行了验证实验.实验结果表明,在容器迁移过程中,不仅实现了容器数据的迁移,而且并未产生多余Docker镜像.

标签推荐系统旨在利用标签数据为用户提供个性化推荐。已有的基于标签的推荐方法往往忽视了用户和资源本身的特征,而且在相似性度量时仅针对项目相似性或用户相似性进行计算,并未充分考虑二者之间的有效融合,推荐结果的准确性较低。为了解决上述问题,将标签信息融入到结合用户相似性和项目相似性的协同过滤中,提出融合标签特征与相似性的协同过滤个性化推荐方法。该方法在充分考虑用户、项目以及标签信息的基础上,利用二维矩阵来定义用户-标签以及标签-项目之间的行为。构建用户和项目的标签特征表示,通过基于标签特征的相似性度量方法计算用户相似性和项目相似性。基于用户标签行为和用户与项目的相似性线性组合来预测用户对项目的偏好值,并根据预测偏好值排序,生成最终的推荐列表。在Last.fm数据集上的实验结果表明,该方法能够提高推荐的准确度,满足用户的个性化需求。

Many actions have been carried out to promote the low-carbon society construction in China, emission trading system (ETS) and energy efficiency trading (EET) are two typical instruments during the past five years. This paper focused on the coexistence between ETS and EET aiming to explore whether policy-mix could enhance industries' interests through the partial-equilibrium model. The results showed that when overlapping transaction scope is allowed, the shadow price of energy saving is certain for the industries. When is not, setting up the policy-mix would not improve energy savings of industries but would make the two trading systems complementary, via the internal mechanism of the policy-mix and providing multiple policy options. The analysis indicated that EET and ETS could coexist and yield better results. Energy saving quota and CO2 quotas can be mutually recognized in a certain method avoiding the repeated accounting between two quotas, which will not increase the burden of enterprises but encourage them to conduct flexible emission reduction to enhance the competitiveness. (c) 2021 The Author(s). Published by Elsevier Ltd.

With the development of mobile networks and the rapid prevalence of location-based social networks (LBSN), a massive volume of spatiotemporal data has been generated, which is valuable for points of interest (POI) recommendation. However, current studies have not unleashed the full power of such spatiotemporal data, which either explore only a single dimension of the data or consider multiple factors in an asynchronous fashion. In this article, we propose a novel spatiotemporal network-based recommender framework (STNBR) to effectively recommend POIs for users. Specifically, we first establish a comprehensive conceptual model of spatiotemporal data, involving various essential factors for POIs recommendation. On top of the conceptual model, we design a series of meaningful meta-paths that simultaneously consider the time and location factors to precisely capture the semantics of user behaviours. By profiling users based on their embedded meta-paths, our approach can yield meaningful POIs recommendations. We have evaluated our proposal using a realistic dataset obtained from Foursquare and Gowalla, the results of which show that our STNBR model outperforms existing approaches.

As the world's largest coal consumer, China's coal consumption in 2021 was 2934.4 million tons of standard coal. Thermal coal occupies an important position in the coal market and industry system, as an important raw material in the power industry, steel industry and other industries. The price of thermal coal in 2021 was at its highest level in a decade, and reached a historical level of about 2587.5 yuan per ton in October 2021. In the same month, the government intervened in the thermal coal price, which fell 51.9% by the end of the year under the influence of the policy. In previous studies, there has been little research on thermal coal and the impact of the variable "policy" on the thermal coal price. Thus, this paper analyzed the factors that affect the price fluctuation of thermal coal, and the impact of economic policy uncertainty on the thermal coal price. The cointegration test and forecast-error variance decomposition (FEVD) are adopted in this study. Our results show that the impact of policy uncertainty on the thermal coal price gradually increases over time, but the impact of policy uncertainty on price is negative and not as strong as expected. On the contrary, inventory and other energy prices have a greater positive impact on the price of thermal coal. The results of this study are of significance for the prediction of thermal coal prices in the future.

In recent years, undesirable disasters attacked the cities frequently, leaving heavy casualties and serious economic losses. Meanwhile, disaster detection based on the Internet of Things(IoT) has become a hot spot that benefited from the established development of smart city construction. And the IoT is visibly sensitive to the management and monitoring of disasters, but massive amounts of monitoring data have brought huge challenges to data storage and data analysis. This article develops a new and much more general framework for disaster emergency management under the IoT environment. The framework is a bottom-up integration of highly scalable Raw Data Storages(RD-Stores) technology, hybrid indexing and queries technology, and machine learning technology for emergency disasters. Experimental results show that hybrid index and query technology have better performance under the condition of supporting multi-modal retrieval, and providing a better solution to offer real-time retrieval for the massive sensor sampling data in the IoT. In addition, further works to evaluate the top-level sub-application system in this framework were performed based on the GPS trajectory data of 35,000 Beijing taxis and the volumetric ground truth data of 7,500 images. The results show that the framework has desirable scalability and higher utility. © 2021

This paper aims to address the problem of post-disaster emergency material dispatching from multiple supply points to multiple demand points. In the case of large-scale natural disasters, it is very important for multiple emergency material supply points to serve as the source of material supply for multiple disaster sites and to accurately determine the emergency material scheduling solutions. Moreover, the quantity of emergency materials required for each disaster site is uncertain. To address this issue, in this work, we mainly present an emergency material scheduling model with multiple logistics supply points to multiple demand points based on the grey interval number. Aiming at the multi-supply points and multi-demand points emergency material scheduling model proposed, a multi-objective optimization algorithm based on the genetic algorithm is adopted to optimize the multi-objective scheduling model. The experimental results show that the multi-objective optimization method can solve the emergency logistics scheduling problem better than the particle swarm multi-objective solution algorithm. At the same time, the multi-supply points and multi-demand points emergency material dispatch model and optimization algorithm provide robust support for emergency management system decision-makers when they need to respond quickly to disaster relief activities. © 2022