The role of district heating in future renewable energy systems

Based on the case of Denmark, this paper analyses the role of district heating in future Renewable Energy Systems. At present, the share of renewable energy is coming close to 20 per cent. From such point of departure, the paper defines a scenario framework in which the Danish system is converted to 100 per cent Renewable Energy Sources (RES) in the year 2060 including reductions in space heating demands by 75 per cent. By use of a detailed energy system analysis of the complete national energy system, the consequences in relation to fuel demand, CO₂ emissions and cost are calculated for various heating options, including district heating as well as individual heat pumps and micro CHPs (Combined Heat and Power). The study includes almost 25 per cent of the Danish building stock, namely those buildings which have individual gas or oil boilers today and could be substituted by district heating or a more efficient individual heat source. In such overall perspective, the best solution will be to combine a gradual expansion of district heating with individual heat pumps in the remaining houses. Such conclusion is valid in the present systems, which are mainly based on fossil fuels, as well as in a potential future system based 100 per cent on renewable energy.     

基于可持续社区建构的城市公共服务设施配置方法——以成都市新都城区为例

针对中国当前以市场为主导的 住区建设中原有基于“组团—小区—居住 区”城市住区层次架构的公共服务设施 配置方法失效问题,运用美国新城市主 义TND模型和英国可持续邻里相关理 论,结合成都市新都城区实际,提出基于 可持续社区建构的城市公共服务设施配 置思路,进而建构出由“划分社区类型— 建立社区层次—探寻公共服务设施布局 形态—分级配置”所组成的城市公共服 务设施配置方法。     

东北地区城市文化景观群落演替初探

本文从文化生态学的角度出发,提出城市文化景观群落的概念。以东北地区为例,解析城市文化景观的主要类型,纵向梳理城市文化景观群落的演替过程,由此总结东北地区城市文化景观发展演变的向性特征,提出未来的应对策略。     

安徽省烟田杂草的分布与危害

采用目测法对安徽省主要烟区烟田杂草进行了调查,取样地共62.48hm2。结果查明全省烟田杂草计有42科,94属,122种。根据对每种杂草的优势度和频度的评价,最主要的杂草17种,包括稗草、马唐、墨旱莲、碎米莎草、铁觅菜、香附子和狗尾草等。结果还表明,本省三大自然烟区杂草区系成分和主要杂草群落及草害各有其特点,江南烟区主要杂草为酸模叶寥、碎米莎草、稗草、石胡荽和异型莎草等16种;江淮之间丘陵烟区主要有稗草、墨旱莲、鸭跖草、粟米草、马齿觅和马松子等11种;淮北平原烟区主要杂草是铁定菜、觅属、斑地锦、香附子、马唐、狗尾草等15种。据此,提出了杂草综合防治措施。      

Cultivation of Quinoa on the Peruvian Altiplano

Quinoa cultivation in the Peruvian altiplano occurs under environmental conditions that change from year to year, including adverse climatic conditions such as drought, flooding, hail, and frost. Under such conditions, ensuring harvest security is crucial. Therefore, farmers plant quinoa throughout the various agroecological and physiographic zones of the altiplano to avoid losing their entire crop in a given year in a particular area (Morlon, [1982] Morlon, P. 1982. “Valorización de La diversidad ecológica”. In TecnologÍas Agricolas Tradicionales de los Andes Centrales Perspectivas Para El Desarrollo Lima, Perú: COFIDE, PNUD y UNESCO.  [Google Scholar]). For the same reason, organization of the production of quinoa traditionally includes rotation with a mixture of quinoa cultivars as well as other crops such as potato, cañiwa, field beans, and barley. Selection of cultivars is determined according to the climate, soil, and humidity of the location.

The primary factor for organization of quinoa production is the agroecological zone. Each agroecological zone is divided into physiographic zones, which can be further divided based on specific environmental conditions.     

Taxonomy of digital community currency platforms*

ABSTRACT

Community currencies are used to pay for products or services within specific groups defined by geographical boundaries or specific common interests. Financial crises, social emergence in developing countries, and increased access to digital devices have stimulated a growing number of communities worldwide to develop digital currency projects. These projects use technologies ranging from traditional plastic cards to mobile phones and blockchain technologies. Following the design science research approach, this paper analyzes digital community currencies (DCCs) by developing a taxonomy based on platform architecture, governance, transactionality and virtuality. By investigating 22 DCC platforms around the world, 4 groups were distinguished: local, proprietary, commons and cyber. The identification of these four different groups of digital community currencies allows us to better discuss the potentials and limitations of each one of them. The presented taxonomy can be useful to researchers and practitioners both to explain and to design DCC platforms. Discussing each of the emerging categories from the proposed taxonomy helps us to provide insights into DCCs, offering a new theoretical frame for investigating the particular case of digital payment platforms.     

基于PU学习的链接预测方法

基于分类的链接预测方法中,由于链接未知节点对的大规模性与不确定性,选择可靠负例成为构造链接预测分类器的难点问题.为此,文中提出基于正例和无标识样本(PU)学习的链接预测方法.首先,提取节点对的拓扑信息以构造样本集.再利用社区结构确定候选负例的分布,基于分布进行多次欠采样,获得多个候选负例子集,集成多个负例集与正例集中构建的分类器选择可靠负例.最后基于正例与可靠负例构造链接预测分类器.在4个网络数据集上的实验表明文中方法预测结果较优.     

Automatic construction and evaluation of macro-actions in reinforcement learning

In this paper, we propose a new subgoal-based method for automatic construction of useful macro-actions modeled with option framework. We propose a new community detection algorithm to provide an appropriate partitioning of the agent’ transition graph. Subgoals are considered as the border states of the transition graph communities and options are constructed for taking the agent from one community to other communities. Despite the importance of considering the effect of each macro-action on learning speed, there is no generic known mechanism for evaluating macro-actions in the literature. We show that using all of the detected macro-actions are not useful and even in a simple environment, the augmentation of the action space with useless or wrong macro-actions may easily worsen learning performance. We propose four different heuristics for evaluating options. We identify, in this way, inappropriate options and discard them from the agent choices. Experimental results show significant improvements in the speed of learning after pruning options.     

A Multi-Level Trend-Renewal Process for Modeling Systems With Recurrence Data

A repairable system is a system that can be restored to an operational state after a repair event. The system may experience multiple events over time that are called recurrent events. To model the recurrent event data, the renewal process (RP), the nonhomogenous Poisson process (NHPP), and the trend-renewal process (TRP) are often used. Compared to the RP and NHPP, the TRP is more flexible for modeling, because it includes both RP and NHPP as special cases. However, for a multi-level system (e.g., system, subsystem, and component levels), the original TRP model may not be adequate if the repair is effected by a subsystem replacement and if subsystem-level replacement events affect the rate of occurrence of the component-level replacement events. In this article, we propose a general class of models to describe replacement events in a multi-level repairable system by extending the TRP model. We also develop procedures for parameter estimation and the prediction of future events based on historical data. The proposed model and method are validated by simulation studies and are illustrated by an industrial application. This article has online supplementary materials.     

基于边密度聚类的重叠社区发现算法

基于边聚类的社区发现算法以边为聚类对象,自然发现重叠社区,但也存在生成的社区集边界归属模糊、社区结构过度重叠等问题.基于此种情况,文中提出基于边密度聚类的重叠社区发现算法.首先,以边为研究对象,通过密度聚类检测连接紧密的核心边社区.然后,根据边界边归属策略将边界边划分到离它最近的核心边社区.针对孤立边,提出基于边的度与边的社区归属的孤立边处理策略,进一步处理未划分的孤立边,避免社区结构过度重叠的问题.最后,将边社区还原为节点社区,实现重叠社区的发现.在人工数据集和真实数据集上的实验表明,文中算法可以快速准确地检测复杂网络中的重叠社区.