LiCamGait: Gait Recognition in the Wild by Using LiDAR and Camera Multi-modal Visual Sensors

LiDAR can capture accurate depth information in large-scale scenarios without the effect of light conditions, and the captured point cloud contains gait-related 3D geometric properties and dynamic motion characteristics. We make the first attempt to leverage LiDAR to remedy the limitation of view-dependent and light-sensitive camera for more robust and accurate gait recognition. In this paper, we propose a LiDAR-camera-based gait recognition method with an effective multi-modal feature fusion strategy, which fully exploits advantages of both point clouds and images. In particular, we propose a new in-the-wild gait dataset, LiCamGait, involving multi-modal visual data and diverse 2D/3D representations. Our method achieves state-of-the-art performance on the new dataset. Code and dataset will be released when this paper is published

Application of gene expression programing in predicting the concentration of PM2.5 and PM10 in Xi’an, China: a preliminary study

Introduction: Traditional statistical methods cannot find quantitative relationship from environmental data.Methods: We selected gene expression programming (GEP) to study the relationship between pollutant gas and PM2.5 (PM10). They were used to construct the relationship between pollutant gas and PM2.5 (PM10) with environmental monitoring data of Xi’an, China. GEP could construct a formula to express the relationship between pollutant gas and PM2.5 (PM10), which is more explainable. Back Propagation neural networks (BPNN) was used as the baseline method. Relevant data from January 1st 2021 to April 26th 2021 were used to train and validate the performance of the models from GEP and BPNN.Results: After the models of GEP and BPNN constructed, coefficient of determination and RMSE (Root Mean Squared Error) are used to evaluate the fitting degree and measure the effect power of pollutant gas on PM2.5 (PM10). GEP achieved RMSE of [8.7365–14.6438] for PM2.5; RMSE of [13.2739–45.8769] for PM10, and BP neural networks achieved average RMSE of [13.8741–34.7682] for PM2.5; RMSE of [29.7327–52.8653] for PM10. Additionally, experimental results show that the influence power of pollutant gas on PM2.5 (PM10) situates between −0.0704 and 0.6359 (between −0.3231 and 0.2242), and the formulas are obtained with GEP so that further analysis become possible. Then linear regression was employed to study which pollutant gas is more relevant to PM2.5 (PM10), the result demonstrates CO (SO2, NO2) are more related to PM2.5 (PM10).Discussion: The formulas produced by GEP can also provide a direct relationship between pollutant gas and PM2.5 (PM10). Besides, GEP could model the trend of PM2.5 and PM10 (increase and decrease). All results show that GEP can be applied smoothly in environmental modelling

Biological Invasion Data Gaps in China: Examples of Distribution, Inventories, and Impact

The impact of invasive alien species (IAS) on nature and society is increasing globally. It is crucial to utilize information systems for evidence-based management, enabling the assessment and supporting survey and control actions. However, the lack of accessible and comprehensive baseline IAS data often impedes the ability to prioritize and allocate resources efficiently. Despite the increased public awareness of biological invasions in China over the past decades, the critical importance of data requirements has not been fully recognized, leading to gaps in available data. Here, we outline the key data demands for the management of biological invasions and highlight the current lack of high-quality data for invasion management in China, and critically assess data gaps in IAS distribution, inventory, and impact. Additionally, we propose a conceptual framework to illustrate the data requirements throughout the invasion management process, along with indicators to assess data quality within three dimensions: relevance, resolution, and reliability

Exploring ways to lighten AMS Food and Beverage Department's ecological footprint : Blue Chip Cookies

Globally, the over consumption of animal products, unsustainable farming practices, and excessive transportation and packaging have resulted in a food system that has an immensely detrimental effect on the environment and that cannot be sustained. The University of British Columbia started the Food System Project, an ongoing collaborative research project involving several key stakeholders, in order to make their own food system more sustainable and to create a food system model that will positively influence the global food system. This paper specifically looks at reducing the ecological footprint of the Alma Matter Society's Food and Beverage Department outlet, Blue Chip Cookies through the creation of a lower footprint menu item. Primary research in the form of a survey and taste test as well as secondary sources in the form of literature review s were utilized for this study. To tackle this issue, our group created a vegan breakfast bar that incorporates local British Columbia produce. Based on a taste test, survey and cost analysis we determined that our breakfast bar would be well received by Blue Chip Cookies customers. In order to increase awareness of the new product and educate people about the importance of reducing their ecological footprint, a marketing strategy and informative pamphlets were created. We also did market research and found that the product was more likely to appeal to the general public if it was advertised as a low ecological footprint product, rather than vegan. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”Land and Food Systems, Faculty ofUnreviewedUndergraduat