Understanding children’s preference for park features that encourage physical activity: an adaptive choice based conjoint analysis

Background Parks are a key setting for physical activity for children. However, little is known about which park features children prefer and which features are most likely to encourage them to be active in parks. This study examined the relative importance of park features among children for influencing their choice of park for engaging in park-based physical activity. Methods Children (n = 252; 8-12 years, 42% male) attending three primary schools in Melbourne, Australia completed a survey at school. They were required to complete a series of Adaptive Choice-Based Conjoint analysis tasks, with responses used to identify the part-worth utilities and relative importance scores of selected park features using Hierarchical Bayes analyses within Sawtooth Software. Results For the overall sample and both boys and girls, the most important driver of choice for a park that would encourage them to be active was presence of a flying fox (overall conjoint analysis relative importance score: 15.8%; 95%CI = 14.5, 17.1), followed by a playground (13.5%; 95%CI = 11.9, 15.2). For the overall sample, trees for climbing had the third highest importance score (10.2%; 95%CI = 8.9, 11.6); however, swings had 3rd highest importance for girls (11.1, 95%CI = 9.3, 12.9) and an obstacle course/parkour area had the 3rd highest importance score for boys (10.7, 95%CI = 9.0, 12.4). For features with two levels, part-worth utility scores showed that the presence of a feature was always preferred over the absence of a feature. For features with multiple levels, long flying foxes, large adventure playgrounds, lots of trees for climbing, large round swings, large climbing equipment, and large grassy open space were the preferred levels. Conclusion To ensure parks appeal as a setting that encourages children to engage in physical activity, park planners and local authorities and organisations involved in park design should prioritise the inclusion of a long flying fox, large adventure playgrounds, lots of trees for climbing, large round swings and obstacle courses/parkour areas.

Optimal Bands Combination Selection for Extracting Garlic Planting Area with Multi-Temporal Sentinel-2 Imagery

Garlic is one of the main economic crops in China. Accurate and timely extraction of the garlic planting area is critical for adjusting the agricultural planting structure and implementing rural policy actions. Crop extraction methods based on remote sensing usually use spectral–temporal features. Still, for garlic extraction, most methods simply combine all multi-temporal images. There has been a lack of research on each band’s function in each multi-temporal image and optimal bands combination. To systematically explore the potential of the multi-temporal method for garlic extraction, we obtained a series of Sentinel-2 images in the whole garlic growth cycle. The importance of each band in all these images was ranked by the random forest (RF) method. According to the importance score of each band, eight different multi-temporal combination schemes were designed. The RF classifier was employed to extract garlic planting area, and the accuracy of the eight schemes was compared. The results show that (1) the Scheme VI (the top 39 bands in importance score) achieved the best accuracy of 98.65%, which is 6% higher than the optimal mono-temporal (February, wintering period) result, and (2) the red-edge band and the shortwave-infrared band played an essential role in accurate garlic extraction. This study gives inspiration in selecting the remotely sensed data source, the band, and phenology for accurately extracting garlic planting area, which could be transferred to other sites with larger areas and similar agriculture structures.

Plant diversity assessment of karst limestone, a case study of Malaysia’s Batu Caves

Batu Caves hill is typical of karst hills in Peninsular Malaysia due to its small size and high biodiversity. It harbours 366 vascular plant species that represent about 25% of the Peninsula’s limestone flora. Five species are endemic to Batu Caves and 23 are threatened species. This high biodiversity is the result of many microhabitats, each with their own assemblages of species. Threats are especially severe as the area of Batu Caves is surrounded by urbanisation that encroaches to the foot of cliffs, is vulnerable to fire, habitat disturbance and, formerly, by quarrying. Assigning a Conservation Importance Score (CIS) to all species is quantitative and accurate, can be implemented rapidly and produces reproducible results. Species with highest CIS are native species of primary vegetation, restricted to limestone substrates, endangered conservation status and, in this case, endemic to Batu Caves. It allows not only species, but microhabitats, sites within a hill and different hills to be compared. By identifying and surveying all microhabitats and focusing on locating endemic and threatened species, maximum biodiversity can be captured. Of the 16 microhabitats identified, the most threatened were the buffer zone, lower levels of steep earth-covered slopes and cave entrances. Application of this method provides a scientific basis for balancing the need to protect microhabitats and sites with the highest CIS, with their multiple uses by various stakeholders, which, at Batu Caves, include the activities of cave temples and eco-recreation. It also provides a scientific quantitative method to compare hills to ensure that those hills with highest CIS are not released for mining.

Deep Hash-based Relevance-aware Data Quality Assessment for Image Dark Data

Data mining can hardly solve but always faces a problem that there is little meaningful information within the dataset serving a given requirement. Faced with multiple unknown datasets, to allocate data mining resources to acquire more desired data, it is necessary to establish a data quality assessment framework based on the relevance between the dataset and requirements. This framework can help the user to judge the potential benefits in advance, so as to optimize the resource allocation to those candidates. However, the unstructured data (e.g., image data) often presents dark data states, which makes it tricky for the user to understand the relevance based on content of the dataset in real time. Even if all data have label descriptions, how to measure the relevance between data efficiently under semantic propagation remains an urgent problem. Based on this, we propose a Deep Hash-based Relevance-aware Data Quality Assessment framework, which contains off-line learning and relevance mining parts as well as an on-line assessing part. In the off-line part, we first design a Graph Convolution Network (GCN)-AutoEncoder hash (GAH) algorithm to recognize the data (i.e., lighten the dark data), then construct a graph with restricted Hamming distance, and finally design a Cluster PageRank (CPR) algorithm to calculate the importance score for each node (image) so as to obtain the relevance representation based on semantic propagation. In the on-line part, we first retrieve the importance score by hash codes and then quickly get the assessment conclusion in the importance list. On the one hand, the introduction of GCN and co-occurrence probability in the GAH promotes the perception ability for dark data. On the other hand, the design of CPR utilizes hash collision to reduce the scale of graph and iteration matrix, which greatly decreases the consumption of space and computing resources. We conduct extensive experiments on both single-label and multi-label datasets to assess the relevance between data and requirements as well as test the resources allocation. Experimental results show our framework can gain the most desired data with the same mining resources. Besides, the test results on Tencent1M dataset demonstrate the framework can complete the assessment with a stability for given different requirements.

Unraveling city-specific signature and identifying sample origin locations for the data from CAMDA MetaSUB challenge

Background Composition of microbial communities can be location-specific, and the different abundance of taxon within location could help us to unravel city-specific signature and predict the sample origin locations accurately. In this study, the whole genome shotgun (WGS) metagenomics data from samples across 16 cities around the world and samples from another 8 cities were provided as the main and mystery datasets respectively as the part of the CAMDA 2019 MetaSUB “Forensic Challenge”. The feature selecting, normalization, three methods of machine learning, PCoA (Principal Coordinates Analysis) and ANCOM (Analysis of composition of microbiomes) were conducted for both the main and mystery datasets. Results Features selecting, combined with the machines learning methods, revealed that the combination of the common features was effective for predicting the origin of the samples. The average error rates of 11.93 and 30.37% of three machine learning methods were obtained for main and mystery datasets respectively. Using the samples from main dataset to predict the labels of samples from mystery dataset, nearly 89.98% of the test samples could be correctly labeled as “mystery” samples. PCoA showed that nearly 60% of the total variability of the data could be explained by the first two PCoA axes. Although many cities overlapped, the separation of some cities was found in PCoA. The results of ANCOM, combined with importance score from the Random Forest, indicated that the common “family”, “order” of the main-dataset and the common “order” of the mystery dataset provided the most efficient information for prediction respectively. Conclusions The results of the classification suggested that the composition of the microbiomes was distinctive across the cities, which could be used to identify the sample origins. This was also supported by the results from ANCOM and importance score from the RF. In addition, the accuracy of the prediction could be improved by more samples and better sequencing depth.

Derivation of an electronic frailty index for short-term mortality in heart failure: a machine learning approach

ObjectiveFrailty may be found in heart failure patients especially in the elderly and is associated with a poor prognosis. However, assessment of frailty status is time-consuming and the electronic frailty indices developed using health records have served as useful surrogates. We hypothesized that an electronic frailty index developed using machine learning can improve short-term mortality prediction in patients with heart failure.MethodsThis was a retrospective observational study included patients admitted to nine public hospitals for heart failure from Hong Kong between 2013 and 2017. Age, sex, variables in the modified frailty index, Deyo’s Charlson comorbidity index (≥2), neutrophil-to-lymphocyte ratio (NLR) and prognostic nutritional index (PNI) were analyzed. Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Comparisons were made with decision tree and multivariate logistic regression.ResultsA total of 8893 patients (median: age 81, Q1-Q3: 71-87 years old) were included, in whom 9% had 30-day mortality and 17% had 90-day mortality. PNI, age and NLR were the most important variables predicting 30-day mortality (importance score: 37.4, 32.1, 20.5, respectively) and 90-day mortality (importance score: 35.3, 36.3, 14.6, respectively). Gradient boosting significantly outperformed decision tree and multivariate logistic regression (area under the curve: 0.90, 0.86 and 0.86 for 30-day mortality; 0.92, 0.89 and 0.86 for 90-day mortality).ConclusionsThe electronic frailty index based on comorbidities, inflammation and nutrition information can readily predict mortality outcomes. Their predictive performances were significantly improved by gradient boosting techniques.

Investigating Learning with Few Shot Data

In this project, I explore a teaching strategy called learning to teach (L2T) in which a teacher model could provide high-quality training samples to a student model. However, one major problem of L2T is that the teacher model will only select a subset of the training dataset as the final training data for the student. A learning to teach small-data learning strategy (L2TSDL) is proposed to solve this problem. In this strategy, the teacher model will calculate the importance score for every training sample and help student to make use of all training samples.

Intelligent Identification of Potential Customers for Electricity Substitution

This paper proposes an intelligent method for the identification of potential customers for electricity substitution. This is developed on the basis of an original model, where a related indicator system of potential customers is constructed through exploratory analysis, improving the results. At the same time, ANOVA is used to screen the indicators and the XGBoost algorithm is employed to output the index importance score and identify likely electricity substitution customers. This method can accurately identify such customers, accelerate the fundamental transformation of energy development, and adapt to the new strategy of Energy Internet development.