Optimal harvesting of a spatial renewable resource

In this paper we investigate the optimal harvesting of a renewable natural resource. While in most standard approaches the resource is located at a single point, we allow the resource to be distributed spatially. Consequently, an agent who exploits the resource has to travel from one location to another. For a fixed planning horizon, we investigate the speed and the path of harvesting chosen by the agent. We show that the agent adjusts this speed so as to visit each location only once, even in the absence of travelling cost. Since the agent does not return to any location for a second harvest, it is optimal to fully deplete the resource upon arrival. A similar type of bang–bang solution results when we drop the assumption of a constant harvesting rate: allowing for a variable harvesting rate, the agent chooses to fully exploit the resource either in the last or in the first travelling period. A society interested in conserving some of the resource thus has to take measures to limit the exploitative behaviour of the agent.     

A systematic mapping study on enterprise architecture mining

Enterprise architecture allows companies to deal with digital transformation through the proactive presentation and alignment of business and IT in a holistic manner. One important challenge is EA modelling since it is time-consuming (thus expensive), error-prone, and biased owing to experts’ subjective opinions. This challenge might consequently be addressed through automatic EA modelling. We conducted a systematic mapping study to classify and evaluate the research concerning EA mining proposals developed specifically for TOGAF and Archimate. After analysing results, we concluded that the research field is not sufficiently mature and further research on EA mining is necessary.     

基于过程挖掘的物流运作风险发现研究

为了探究物流运作过程中存在的风险,提高物流运作效率,增强物流企业管理能力,以真实的事件日志为基础,运用过程挖掘方法发现物流企业运作过程中的风险。首先以Petri网理论为基础,构建了理想的工作模型,然后使用过程挖掘工具ProM,运用模糊挖掘算法和启发式挖掘算法,对物流运作过程中产生的真实事件日志进行挖掘,获得实际的客观视图,最后利用ProM工具的一致性检查发现物流运作过程中的异常行为模式,并分析物流运作过程中的偏差以及存在的风险。结果表明,过程挖掘能客观地揭示事件日志中活动之间的依赖关系,并发现物流企业自身运作过程中的偏差和风险。研究结果为全面、系统地分析物流企业潜在风险提供了新思路,有助于物流企业发现运作过程中的问题,对物流企业优化运作过程和提高管理效率具有重要的借鉴意义。     

Complex Network of Aviation E-Services in the Belt and Road Initiative: A Heuristic Study of Small Data based on Block Modeling

ABSTRACT

The aviation e-service system is an important part of support for business communication in regions covered by Belt and Road initiative. In exploring the role of airline companies in this system, we examine the structure of the network, which consists of airlines interconnections based on small data of individual attributes of aviation companies in aviation e-services, and study these interconnections across different groups using block modeling. The heuristic solution of airline companies in development of international communication and cooperation is further discussed. Among our policy implications for global managers, we suggest the enhancement of cross-regional cooperation among airlines.     

基于多维频谱地图的干扰规避

针对复杂电磁环境下卫星移动通信系统可靠通信需求，利用频谱地图多维数据呈现优势，提出了基于多维频谱地图的干扰规避方法。通过建立卫星移动通信系统波束、时隙、子频带、干扰强度等多维信息关联的频谱地图，利用数据挖掘方法对频谱可用情况进行预测，基于预测结果及用户QoS(Quality of Service)需求生成干扰规避策略，通过星地联合频谱资源调配实现系统干扰规避。该方法为卫星移动通信系统干扰规避提供了一种全新的思路，可提升卫星移动通信系统干扰防护能力。     

Crowdsourcing-based semantic relation recognition for natural language questions over RDF data

ABSTRACT

Natural language query systems over RDF data need to rely on the semantic relations in query. First, we propose the new crowdsourcing model that used to produce semantic relations dataset. The model not only inherits completeness of the iterative model and accuracy of the parallel model, but also saves human resources. Second, we mine the rules of semantic relation recognition from the correlations between dependency structures and semantic relations. Third, we propose an algorithm of semantic relation recognition for natural language query over RDF data, and experiments demonstrate that it can recognize more semantic relations than existing methods.     

EMPIRICAL PERFORMANCE OF GARCH MODELS WITH HEAVY‐TAILED INNOVATIONS

We introduce a new type of heavy‐tailed distribution, the normal reciprocal inverse Gaussian distribution (NRIG), to the GARCH and Glosten‐Jagannathan‐Runkle (1993) GARCH models, and compare its empirical performance with two other popular types of heavy‐tailed distribution, the Student's t distribution and the normal inverse Gaussian distribution (NIG), using a variety of asset return series. Our results illustrate that there is no overwhelmingly dominant distribution in fitting the data under the GARCH framework, although the NRIG distribution performs slightly better than the other two types of distribution. For market indexes series, it is important to introduce both GJR‐terms and the NRIG distribution to improve the models’ performance, but it is ambiguous for individual stock prices series. Our results also show the GJR‐GARCH NRIG model has practical advantages in quantitative risk management. Finally, the convergence of numerical solutions in maximum‐likelihood estimation of GARCH and GJR‐GARCH models with the three types of heavy‐tailed distribution is investigated.     

Coloring the destination: The role of color psychology on Instagram

Color psychology plays important roles in product packaging, the retail environment, and online marketing. This study analyzed current practices related to color composition in tourism-related photographs on Instagram. Data mining and pictorial content analysis were adopted to investigate how lightness, chroma, and hue may influence Instagram posts' popularity. Generally, individuals appeared more likely to respond to brighter and more saturated destination pictures. Orange, yellow, blue, and violet contributed significantly to post popularity based on different photographic typologies. Overall, color is a complex construct that functions within diverse dimensions and can enrich tourists’ destination-related perceptions. Based on brand post popularity and color theories, the study findings provide valuable implications for effective and efficient destination promotion via Instagram. Relevant limitations and future directions are also discussed.     

Identifying R&D partners through Subject-Action-Object semantic analysis in a problem & solution pattern

Today’s companies still rely heavily on expert knowledge rather than quantitative data with a systematic approach to effectively identify and choose Research and Development (R&D) partners. It is advantageous to identify and select potential R&D partners using a Problem & Solution (P&S) pattern. This paper presents a novel process for identifying R&D partners on the basis of solution similarities that assist technology managers in understanding the relationships between research targets. First, we choose a thematic dataset that contains problems and quantitative data with relative topic terms. Then, we extract Subject-Action-Object semantic structures in a P&S pattern from the dataset, and identify various solutions to a technical problem, with each as a subject. In addition, we provide correlation mapping to visualise the text characters and identify R&D partners. Finally, we validate the proposed method through a case study of the dye-sensitized solar cells sector.     

基于DEMATEL对煤炭企业供应商关键绩效指标间的关系分析

依据企业自身特点和要素间的关系构建评价指标体系,采用DEMATEL方法对淮南矿业集团的供应商选择评价进行研究。通过分析关键绩效指标间的逻辑关系和直接影响关系,揭示系统内要素的结构关系,为供应商的选择与供应链竞争力的提升提出了相应的建议。