Augmented context-based recommendation service framework using knowledge over the Linked Open Data cloud

This research proposes ACARDS (Augmented-Context bAsed RecommenDation Service) framework that is able to utilize knowledge over the Linked Open Data (LOD) cloud to recommend context-based services to users. To improve the level of user satisfaction with the result of the recommendation, the ACARDS framework implements a novel recommendation algorithm that can utilize the knowledge over the LOD cloud. In addition, the noble algorithm is able to use new concepts like the enriched tags and the augmented tags that originate from the hashtags on the SNSs materials. These tags are utilized to recommend the most appropriate services in the user’s context, which can change dynamically. Last but not least, the ACARDS framework implements the context-based reshaping algorithm on the augmented tag cloud. In the reshaping process, the ACARDS framework can recommend the highly receptive services in the users’ context and their preferences. To evaluate the performance of the ACARDS framework, we conduct four kinds of experiments using the Instagram materials and the LOD cloud. As a result, we proved that the ACARDS framework contributes to increasing the query efficiency by reducing the search space and improving the user satisfaction on the recommended services.     

Capsiate Intake with Exercise Training Additively Reduces Fat Deposition in Mice on a High-Fat Diet,but Not without Exercise Training

While exercise training (ET) is an efficient strategy to manage obesity, it is recommended with a dietary plan to maximize the antiobesity functions owing to a compensational increase in energy intake. Capsiate is a notable bioactive compound for managing obesity owing to its capacity to increase energy expenditure. We aimed to examine whether the antiobesity effects of ET can be further enhanced by capsiate intake (CI) and determine its effects on resting energy expenditure and metabolic molecules. Mice were randomly divided into four groups (n = 8 per group) and fed high-fat diet. Mild-intensity treadmill ET was conducted five times/week; capsiate (10 mg/kg) was orally administered daily. After 8 weeks, resting metabolic rate and metabolic molecules were analyzed. ET with CI additively reduced the abdominal fat rate by 18% and solely upregulated beta-3-adrenoceptors in adipose tissue (p = 0.013) but did not affect the metabolic molecules in skeletal muscles. Surprisingly, CI without ET significantly increased the abdominal fat rate (p = 0.001) and reduced energy expenditure by 9%. Therefore, capsiate could be a candidate compound for maximizing the antiobesity effects of ET by upregulating beta-3-adrenoceptors in adipose tissue, but CI without ET may not be beneficial in managing obesity.     

计及Spark和属性权重的售电套餐推荐方法

针对电力市场用户群庞大，交易过程中售电套餐选择困难的问题，在Spark环境下设计了一种售电套餐推荐方法，同时也解决了售电套餐推荐过程中在大数据环境下的可扩展性及实时性问题。首先，计算出每个套餐属性的权重值，从而计算得到售电套餐综合相似度。然后，计及用户和套餐两方面提出一种售电套餐推荐方法，实现售电套餐的精准推荐。实验表明，提出的推荐方法能够明显提高推荐的准确度，并且在分布式环境下具有良好的推荐效率和可扩展性。     

The Gut Microbiota: A Potential Gateway to Improved Health Outcomes in Breast Cancer Treatment and Survivorship

Breast cancer is the most frequently diagnosed cancer in women worldwide. The disease and its treatments exert profound effects on an individual’s physical and mental health. There are many factors that impact an individual’s risk of developing breast cancer, their response to treatments, and their risk of recurrence. The community of microorganisms inhabiting the gastrointestinal tract, the gut microbiota, affects human health through metabolic, neural, and endocrine signaling, and immune activity. It is through these mechanisms that the gut microbiota appears to influence breast cancer risk, response to treatment, and recurrence. A disrupted gut microbiota or state of ‘dysbiosis’ can contribute to a biological environment associated with higher risk for cancer development as well as contribute to negative treatment side-effects. Many cancer treatments have been shown to shift the gut microbiota toward dysbiosis; however, the microbiota can also be positively manipulated through diet, prebiotic and probiotic supplementation, and exercise. The objective of this review is to provide an overview of the current understanding of the relationship between the gut microbiota and breast cancer and to highlight potential strategies for modulation of the gut microbiota that could lead to improved clinical outcomes and overall health in this population.     

黄河下游滩区居民心理研究的启示与建议

黄河下游滩区居民因特殊自然环境而形成了特定的心理活动、心理需求，这既影响到他们的生产与生活．又对政府在滩区的有关政策产生或积极或消极的影响。国家制定和实施滩区政策时，应根据滩区居民的心理需要．树立“以人为本，，的观念，在洪水淹没补偿、村台建设与搬迁、基层组织建设、外迁政策的落实等方面切实解决滩区居民的具体困难，并采取一系列综合措施促进滩区社会经济健康发展及人与自然的和谐。     

多媒体通信的新发展

本文介绍了各种多媒体通信终端的新标准,对于开发PSTN 多媒体可视电话终端,提出了有关建议,分析了适用于多媒体通信的各种通信网络,最后论述了多媒体通信中的两项关键技术,即多媒体信息压缩技术和同步技术。     

A connectionist implementation of the theory of planned behavior: Association of beliefs with exercise intention.

The theory of planned behavior suggests attitudes are a product of salient beliefs. This study examined whether aggregating salient beliefs was plausible within a more biologically centered information-processing environment. A neural network was used to examine associations among beliefs relating to exercise intention. Data on intentions and behavioral, normative, and control beliefs from 114 respondents were used to train (by error backpropagation) a neural network to associate beliefs with intention. The R2 between the network's estimated and self-reported intention was .66. The network's representation comprised 6 belief profiles associated with high, moderate, or low behavioral intention. The neural network accommodated complex relationships among beliefs and belief-intention associations and indicated how high-level constructs such as attitudes may be viewed as the best fit (compromise state) between aroused beliefs. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于聚类分析的新书推荐

针对大多数图书馆管理系统中的个性化服务中的新书推荐只是定时将新到馆的图书罗列出来让读者浏览进行推荐,推荐效果差的问题。本文在比较三大常用的推荐技术的优缺点后,采用基于聚类分析的推荐算法推荐新书,并具体给出实现的方法。     

基于知识图谱的互补项目推荐

在缺乏用户交互互补项目方面数据的情况下,将用户对项目的偏好融合到只考虑项目关系的互补项目推荐中,提高推荐模型的性能。提出一种基于知识图谱的互补项目推荐方法,在用户历史交互项目集中推测用户交互的互补项目,基于知识图谱提取用户对互补项目的偏好,利用图像与文本学习项目之间的互补关系,最后基于神经网络实现二者的共同学习。提出的方法在Amazon数据集上与次优的基线方法相比,ACC提升了7%,precision提升了3%,这说明提出的方法性能优异。该算法共同学习用户对项目的偏好与项目之间的互补关系,提升了推荐性能。