The Rise and Fall of Popular Australian Lawn Bowls 1864–2021

Lawn bowls is one of the oldest formally established British sports in Australia. This paper provides an overview of the place of lawn bowls in Australian history and society from 1864 to 2021 through four eras: British nationalism (1864–1900); middle class sport (1900–1945); leading seniors sport (1945–1990); and decline (1990–2021). The four eras cover the span of Australian lawn bowls and are based on historical data from both primary and secondary sources tracing its rise and decline. The decline in lawn bowls has been a combination of both internal and external factors. This paper is not a purely chronological account of lawn bowls, but rather provides a framework to better understand the place and role of the sport in Australian culture and society. The origins of lawn bowls in Australia are directly linked to the cultural heritage that stemmed from British settlement. Lawn bowls provides a vehicle to develop insight and understanding into several past, current, and future social and cultural issues including seniors sport, sport participation rates, gender relations, nationalism, class structures, urbanisation, and the development of contemporary cities.

Personalized, Sequential, Attentive, Metric-Aware Product Search

The task of personalized product search aims at retrieving a ranked list of products given a user’s input query and his/her purchase history. To address this task, we propose the PSAM model, a Personalized, Sequential, Attentive and Metric-aware (PSAM) model, that learns the semantic representations of three different categories of entities, i.e., users, queries, and products, based on user sequential purchase historical data and the corresponding sequential queries. Specifically, a query-based attentive LSTM (QA-LSTM) model and an attention mechanism are designed to infer users dynamic embeddings, which is able to capture their short-term and long-term preferences. To obtain more fine-grained embeddings of the three categories of entities, a metric-aware objective is deployed in our model to force the inferred embeddings subject to the triangle inequality, which is a more realistic distance measurement for product search. Experiments conducted on four benchmark datasets show that our PSAM model significantly outperforms the state-of-the-art product search baselines in terms of effectiveness by up to 50.9% improvement under NDCG@20. Our visualization experiments further illustrate that the learned product embeddings are able to distinguish different types of products.

Exploiting Group Information for Personalized Recommendation with Graph Neural Networks

Personalized recommendation has become more and more important for users to quickly find relevant items. The key issue of the recommender system is how to model user preferences. Previous work mostly employed user historical data to learn users’ preferences, but faced with the data sparsity problem. The prevalence of online social networks promotes increasing online discussion groups, and users in the same group often have similar interests and preferences. Therefore, it is necessary to integrate group information for personalized recommendation. The existing work on group-information-enhanced recommender systems mainly relies on the item information related to the group, which is not expressive enough to capture the complicated preference dependency relationships between group users and the target user. In this article, we solve the problem with the graph neural networks. Specifically, the relationship between users and items, the item preferences of groups, and the groups that users participate in are constructed as bipartite graphs, respectively, and the user preferences for items are learned end to end through the graph neural network. The experimental results on the Last.fm and Douban Movie datasets show that considering group preferences can improve the recommendation performance and demonstrate the superiority on sparse users compared

Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction

Traffic flow prediction is the upstream problem of path planning, intelligent transportation system, and other tasks. Many studies have been carried out on the traffic flow prediction of the spatio-temporal network, but the effects of spatio-temporal flexibility (historical data of the same type of time intervals in the same location will change flexibly) and spatio-temporal correlation (different road conditions have different effects at different times) have not been considered at the same time. We propose the Deep Spatio-temporal Adaptive 3D Convolution Neural Network (ST-A3DNet), which is a new scheme to solve both spatio-temporal correlation and flexibility, and consider spatio-temporal complexity (complex external factors, such as weather and holidays). Different from other traffic forecasting models, ST-A3DNet captures the spatio-temporal relationship at the same time through the Adaptive 3D convolution module, assigns different weights flexibly according to the influence of historical data, and obtains the impact of external factors on the flow through the ex-mask module. Considering the holidays and weather conditions, we train our model for experiments in Xi’an and Chengdu. We evaluate the ST-A3DNet and the results show that we have better results than the other 11 baselines.

How to Implement User-Friendly BLMs in the Absence of DOC Monitoring Data: A Case Study on Bulgarian Surface Waters

The metal bioavailability concept is implemented in the Water Framework Directive (WFD) compliance assessment. The bioavailability assessment is usually performed by the application of user-friendly Biotic Ligand Models (BLMs), which require dissolved metal concentrations to be used with the “matching” data of the supporting physicochemical parameters of dissolved organic carbon (DOC), pH and Cadissolved. Many national surface water monitoring networks do not have sufficient matching data records, especially for DOC. In this study, different approaches for dealing with the missing DOC data are presented: substitution using historical data; the appropriate percentile of DOC concentrations; and combinations of the two. The applicability of the three following proposed substitution approaches is verified by comparison with the available matching data: (i) calculations from available TOC data; (ii) the 25th percentile of the joint Bulgarian monitoring network DOC data (measured and calculated by TOC); and (iii) the 25th percentile of the calculated DOC from the matching TOC data for the investigated surface water body (SWB). The application of user-friendly BLMs (BIO-MET, M-BAT and PNEC Pro) to 13 surface water bodies (3 reservoirs and 10 rivers) in the Bulgarian surface waters monitoring network outlines that the suitability of the substitution approaches decreases in order: DOC calculated by TOC > the use of the 25th percentile of the data for respective SWB > the use of the 25th percentile of the Bulgarian monitoring network data. Additionally, BIO-MET is the most appropriate tool for the bioavailability assessment of Cu, Zn and Pb in Bulgarian surface water bodies.

The Effect of Concealed-Carry and Handgun Restrictions on Gun-Related Deaths: Evidence from the Sullivan Act of 1911

The 1911 NY State Sullivan Act (SA) outlawed carrying concealable firearms without a licence, established strict licencing rules, and regulated the sale and possession of handguns. We analyse the effects of the SA using historical data on mortality rates, pistol permits, and citations for illegal carrying. Our analysis of pistol permits and citations reveal clear initial effects of the SA on gun-related behaviours. Using synthetic control and difference-in-differences methodologies, our main analyses show no effects on overall homicide rates, evidence of a reduction in overall suicide rates, and strong evidence of a large and sustained decrease in gun-related suicide rates.

Facebook as Data: A Primer for Educational Technology Researchers Using Public Internet Data Mining Methods

Digital trace data has helped us understand teaching and learning on social media sites other than Facebook. Moreover, while we know a few things about the educational uses of Facebook, that knowledge has been limited because Facebook has not—until recently—been open to researchers. This paper introduces how Facebook can serve as a data source for educational technology researchers. It provides a walkthrough, from developing research questions and identifying public pages of interest, considering ethics, accessing and downloading historical data through the CrowdTangle platform, and analyzing that data. An example list of pages is provided with code for the open-source statistical software to analyze the data. Future directions for the use of Facebook for research on teaching, learning, and educational systems and their intersection with educational technologies are discussed.

Integrated Intellectual Investment Portfolio as an Efficient Instrument to Manage Personal Financial Investment

The redistribution of resources in global stock markets is prevalent: the capital is transferred from one investor to another. Sometimes, earning a substantial return in the stock market seems complicated to implement for an individual investor. Investing contributes to the welfare of society and the wealth of citizens. This is why people should look for efficient ways to invest. Investment should become a natural part of personal finance management in the majority of households. For this reason, an investment model is developed where stocks are selected based only on market intelligence using historical data. The model helps find one or several stocks that generate the highest return on a separate step. Applying this model, experiments were performed with daily data from German, US, and UK stock markets. The possibility of obtaining higher than average returns in these markets has been noticed. In the German market, during the 97-day period, the authors obtained a 1.46 return, which implies a 2.31 annual return: in the USA market, a 2.37 return (7.93 annual return), and in the UK market, a 1.90 return (4.09 annual return). Thus, the proposed investment decision-making system could be an efficient tool for forming a sustainable individual or household portfolio. It can generate higher investment returns for an investor and, moreover, make the market more efficient by applying market intelligence and related historical data.