Home energy efficiency and radon: An observational study

Exposure to radon gas is the second leading cause of lung cancer worldwide behind smoking. Changing the energy characteristics of a dwelling can influence both its thermal and ventilative properties, which can affect indoor air quality. This study uses radon measurements made in 470 689 UK homes between 1980 and 2015, linked to dwelling information contained within the Home Energy Efficiency Database (HEED). The linked dataset, the largest of its kind, was used to analyze the association of housing and energy performance characteristics with indoor radon concentrations in the UK. The findings show that energy efficiency measures that increase the airtightness of properties are observed to have an adverse association with indoor radon levels. Homes with double glazing installed had radon measurements with a significantly higher geometric mean, 67% (95% CI: 44, 89) greater than those without a recorded fabric retrofit. Those with loft insulation (47%, 95% CI: 26, 69) and wall insulation (32%, 95% CI: 11, 53) were also found to have higher radon readings. Improving the energy performance of the UK's housing stock is vital in meeting carbon emission reduction targets. However, compromising indoor air quality must be avoided through careful assessment and implementation practices.     

Pedestrian and Bicycle Volume Data Collection Using Drone Technology

ABSTRACT

Pedestrian and bicycle volume data is one of the most fundamental types of data for active transportation planning. However, they are not yet well developed. This paper explores an innovative method that uses drone technology to collect pedestrian and bicycle volume data. It confirms the feasibility of the technology as an alternative method to collect complex movements of pedestrians and bicycles. Furthermore, this paper presents a method that converts the video footage to a spatiotemporal dataset. The dataset includes not only the pedestrian and bicycle count data, but also their behavior and characteristics. The spatiotemporal data can become a valuable resource for a variety of active transportation planning practices and research, including the collection of pedestrian and bicycle volume data in parks and recreational areas, the study of collisions between pedestrians and bicycles, and the analysis of the social path. Since it is possible to envision that the development of technologies can overcome current technical difficulties, such as battery lifespan, it is worth considering the application of drone technology to active transportation planning practice and research.     

跨模态脑图谱数据融合研究进展

脑图谱是研究脑结构和功能及脑疾病的基础,不同类型的脑图谱从不同角度提供了脑的组织模式或连接信息。随着图像采集和生物检测技术的发展,不同模态的脑影像和生物组学数据迅速增长。相较于单模态,多模态融合数据能够同时考察不同模态数据间的多元化信息,挖掘蕴含的未知新信息。因此,开展跨模态脑图谱数据融合研究有助于更全面地理解大脑的结构和功能,并辅助加深对脑发育、老化和病变机理的理解。本文根据参与融合的模态是否具有空间信息,将近年来有代表性的跨模态脑图谱融合技术分为脑影像融合和脑数据融合两大类。脑影像融合是指对宏观脑影像(磁共振等)和组织学脑影像(胞体染色、轴突染色等)等具有空间信息的数据进行融合,构建涵盖脑结构和功能信息的跨模态多尺度脑图谱,为研究宏观特征的介观机制以及介观特征的宏观表征提供了重要途径。脑数据融合是指对缺乏脑空间信息的生物大数据,包括基因组、电生理、认知和行为等,利用脑图谱提供精细空间信息,挖掘高维、异构生物大数据蕴含的信息,明确脑图谱的生理意义,并提升其应用价值。本文将针对这两类融合类型阐述国内外有代表性的研究进展,并对比国内外研究现状的差异。此外,为促进跨模态脑图谱数据融合领域的交流和发展,总结了部分有代表性的大样本公开数据集。最后讨论了当前该领域待解决的问题以及未来的发展趋势。     

面向不均衡数据集的过抽样算法

合成少数类过抽样技术（SMOTE）中的噪声样本可能参与合成新样本，所以难以保证新样本的合理性。针对这个问题，结合聚类算法提出了改进算法CSMOTE。该算法抛弃了SMOTE在最近邻间线性插值的思想，使用少数类的簇心与其对应簇中的样本进行线性插值合成新样本，并且对参与合成的样本进行了筛选，降低了噪声样本参与合成的可能。在六个实际数据集上，将CSMOTE算法与四个SMOTE的改进算法以及两种欠抽样算法进行了多次的对比实验，CSMOTE算法在所有数据集上均获得了最高的AUC值。实验结果表明，CSMOTE算法具有更高的分类性能，可以有效解决数据集中样本分布不均衡的问题。     

富营养化评价中数据分析的研究进展

数据处理方法是富营养化评价的关键因素。简要阐述富营养化的定义,比较详细地评述富营养化评价过程中数据处理方法的研究现状及国内外常用的几种富营养化评价方法。重点介绍几种定量评价水质富营养化的方法:综合指数法、统计分析方法(主成分、聚类和判别分析),以及遥感技术、多标准评价分析、模型分析等。指出富营养评价技术的发展将为水质管理提供可靠的依据,并对富营养化评价技术的研究做出了展望。     

黄土高原典型流域网格化逐时降水融合产品CMPA V1.0的精度分析

选取黄土高原无定河、窟野河和皇甫川流域为研究区,从水文应用角度评估了中国自动站与CMORPH融合的逐时降水产品(CMPA V1.0)的精度。结果表明,CMPA V1.0降雨数据总体上较雨量站实测值偏低;与6、9月相比,8月降水产品精度较低。随降水量级的增加,CMPA V1.0对降水事件发生频次的监测能力和对降水事件监测的稳定性有所降低,误报率整体升高;CMPA V1.0低估了小雨的发生频率及降水贡献率,高估了雨强大于2.5mm/h降雨的发生频率及降水贡献率;CMPA V1.0能合理地反映研究区降水的日变化过程,但低估了研究区的时段平均降水量和时段平均降水频率,高估了研究区14:00前的时段平均降水强度。     

大规模连续中国手语数据集的创建与分析

绝大多数健听人不懂手语导致听障人在找工作、就医、法律咨询等各生活、工作领域中遇到了极大的沟通障碍，而手语翻译员需要提前预约，成本也非常高，所以很多科研工作者都开始利用机器学习来开发手语自动翻译器，但其中的大部分研究都因为受到了数据集规模和质量的影响而效果不佳。为解决上述矛盾和问题，创建了目前全球最大的中国连续手语数据集，并使用了考虑身体关节的位置、面部表情及手指关节的端到端的深度学习模型进行有效训练。结论突显了现代深度学习技术在识别复杂手语方面的巨大优势，针对较小子集的BLEU-4已达到30.8。     

Learning hierarchical spatio-temporal pattern for human activity prediction

Human activity prediction has become increasingly valuable in many applications. This paper, initially from the perspective of cognition science, presents a novel approach to learning a hierarchical spatio-temporal pattern of human activities to predict ongoing activities from videos that contain only the onsets of the activities. Spatio-temporal pattern can be learned by a Hierarchical Self-Organizing Map (HSOM), which consists of two self-organizing maps (i.e., action map and actionlet map) connected via associative links trained by Hebbian learning. Ongoing activities can be predicted by Variable order Markov Model (VMM), which provides the means for capturing both large and small order Markov dependencies based on the training actionlet sequences. Experiments of the proposed method on four challenging 3D action datasets captured by commodity depth cameras show promising results.     

Risk-aware intermediate dataset backup strategy in cloud-based data intensive workflows

Data-intensive workflows are generally computing- and data-intensive with large volume of data generated during their execution. Therefore, some of the data should be saved to avoid the expensive re-execution of tasks in case of exceptions. However, cloud-based data storage services come at some expense. In this paper, we introduce the risk evaluation model tailored for workflow structure to measure and achieve the trade-off between the overhead of backup storage and the cost of data regeneration in failure, making the service selection and execution more efficient and robust. The proposed method computes and compares the potential loss with and without data backup to achieve the trade-off between overhead of intermediate dataset backup and task re-execution after exceptions. We also design the utility function with the model and apply a genetic algorithm to find the optimized schedule. The results show that the robustness of the schedule is increased while the possible risk of failure is minimized, especially when the volume of generated data is not large in comparison with the input.