Lose Some, Save Some: Obesity, Automobile Demand, and Gasoline Consumption in the U.S.

This paper examines the unexplored link between the prevalence of overweight and obesity and vehicle demand in the United States. Exploring annual sales data of new passenger vehicles at the model level in 48 U.S. counties from 1999 to 2005, we find that a 10 percentage point increase in the rate of overweight and obesity reduces the average MPG of new vehicles demanded by 2.5 percent: an effect that requires a 30 cent increase in gasoline prices to counteract. Our findings suggest that policies to reduce overweight and obesity can have additional benefits for energy security and the environment.

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GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection

In recent years, video anomaly detection has been extensively investigated in both unsupervised and weakly supervised settings to alleviate costly temporal labeling. Despite significant progress, these methods still suffer from unsatisfactory results such as numerous false alarms, primarily due to the absence of precise temporal anomaly annotation. In this paper, we present a novel labeling paradigm, termed "glance annotation", to achieve a better balance between anomaly detection accuracy and annotation cost. Specifically, glance annotation is a random frame within each abnormal event, which can be easily accessed and is cost-effective. To assess its effectiveness, we manually annotate the glance annotations for two standard video anomaly detection datasets: UCF-Crime and XD-Violence. Additionally, we propose a customized GlanceVAD method, that leverages gaussian kernels as the basic unit to compose the temporal anomaly distribution, enabling the learning of diverse and robust anomaly representations from the glance annotations. Through comprehensive analysis and experiments, we verify that the proposed labeling paradigm can achieve an excellent trade-off between annotation cost and model performance. Extensive experimental results also demonstrate the effectiveness of our GlanceVAD approach, which significantly outperforms existing advanced unsupervised and weakly supervised methods. Code and annotations will be publicly available at https://github.com/pipixin321/GlanceVAD.Comment: 21 page

STW-MD: A Novel Spatio-Temporal Weighting and Multi-Step Decision Tree Method for Considering Spatial Heterogeneity in Brain Gene Expression Data

Motivation: Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Due to the lack of comprehensive integration of spatial and temporal dimensions of brain gene expression data, previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of the mechanisms underlying brain development or disorders associated with abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. Results: In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle, and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development. Availability: The code of STW-MD is available at https://github.com/tsnm1/STW-MD.Comment: 11 pages, 6 figure

A Rule-Based Expert System for Automatic Segmentation of Cerebral MRI Images

The interior boundary of medical image is fuzzy in nature. In this paper, proposed is a novel method to segment and classify the MR image of head by fuzzy clustering and fuzzy reasoning. Traditional fuzzy clustering methods are basically statistical ones in which only intensity affinities of the image are reflected. Considering the characteristics of MR image, we constructed a set of knowledge-based rules to set the fuzzy memberships of the pixels of the image by generally using the intensity similarities, positional relationships among multiple spectra MR images, and the shape features of the brain tissues and the mathematics morphological analogy of the brain tissues. Then a coarse-to-fine reasoning method is used to combine the fuzzy memberships of the pixels of the T1- and T2- channels of the image to segment the cerebral tissues into gray matter, white matter, and CSF. Experimental results showed the efficiency of the method

DWT-Based Watermarking Using QR Code

Increased commercial activity on the Internet and media industries demandsprotection of multimedia contents. In this paper, we introduce a novel watermarkingmethod to embed QR codes in digital images. The method is based on discrete wavelettransform (DWT). The original image is divided into blocks, and QR codes are added toparticular bits of LL2 level coefficients of the selected block according to the visual maskingeffect of the human visual system. It has been shown that this method is robust for JPEGcompression and has good transparency. The embedded information can be extractedcorrectly even if the images are compressed to 11% of the original according to the contentsof the images

Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery

Aboveground biomass (AGB) is an essential assessment of plant development and guiding agricultural production management in the field. Therefore, efficient and accurate access to crop AGB information can provide a timely and precise yield estimation, which is strong evidence for securing food supply and trade. In this study, the spectral, texture, geometric, and frequency-domain variables were extracted through multispectral imagery of drones, and each variable importance for different dimensional parameter combinations was computed by three feature parameter selection methods. The selected variables from the different combinations were used to perform potato AGB estimation. The results showed that compared with no feature parameter selection, the accuracy and robustness of the AGB prediction models were significantly improved after parameter selection. The random forest based on out-of-bag (RF-OOB) method was proved to be the most effective feature selection method, and in combination with RF regression, the coefficient of determination (R2) of the AGB validation model could reach 0.90, with root mean square error (RMSE), mean absolute error (MAE), and normalized RMSE (nRMSE) of 71.68 g/m2, 51.27 g/m2, and 11.56%, respectively. Meanwhile, the regression models of the RF-OOB method provided a good solution to the problem that high AGB values were underestimated with the variables of four dimensions. Moreover, the precision of AGB estimates was improved as the dimensionality of parameters increased. This present work can contribute to a rapid, efficient, and non-destructive means of obtaining AGB information for crops as well as provide technical support for high-throughput plant phenotypes screening

Research and application of coal exploration data management method in working face based on GIS

In order to improve the efficiency of dynamic calculation of coal reserves in the mining process of coal working face and enrich the dynamic updated data required in the construction of 3D geological model of working face in intelligent mining, a GIS based coal mining face data management method is proposed through the full analysis and in-depth research on the professional needs, business processes, technical routes and data structure of coal mining face data management. At the data level, the business process of coal exploration data management was optimized, the data structure and storage method of different types of coal exploration data were designed, the coal exploration data sharing and management of mining face based on spatial relational database were realized from using the spatial data organization and management mode. At the presentation level, the interactive management of coal exploration data and graphics was realized based on the domestic geographic information system platform, LongRuanGIS, independently developed from the bottom. At the business level, a drawing algorithm of coal exploration line was proposed to realize the rapid drawing of different coal exploration lines. The data structure, drawing style, drawing method and data management method of coal exploration point were designed to ensure the beautiful mapping of coal exploration data and efficient reuse of data; a method of dynamically updating the geological model of coal mining face by using the data of coal thickness detection was proposed, which enriches the dynamic updating data source of high-precision three-dimensional dynamic geological model of working face. The results of normalization application in many mines show that the management method of coal exploration data based on GIS realizes the unified management and sharing of different types of coal exploration data, realizes the rapid automatic mapping and dynamic updating of coal exploration data and improves the drawing efficiency of coal mine geologists. At the same time, the timely updating of coal exploration data provides convenient and effective data management measures for dynamic calculation of reserves and dynamic updating of high-precision 3D geological model, ensuring the efficient use of coal exploration data in many aspects

Design and operational parameters optimisation of a citrus substrate filling and transporting machine

Aiming to address the problem of low mechanisation of filling and transporting citrus seedling pots in China, a new type of pot filling and transporting machine with 120 pots at a time was designed. Based on the study of flow characteristics of the seedling substrate, key components of the filling and transporting machines, such as the hopper component, transmission mechanism, flip mechanism, and steering mechanism, were designed. The effects of the opening width of the hopper, the rotating speed of the stirring shaft, the moisture content of the seedling substrate, and the forward speed of the transporting device on the filling effect of the seedling pot were studied by the experimental method, and the optimal operation parameters were determined. The prototype tests were repeated 3 times with the best combination of parameters. The test results indicate that the machine was in good condition for loading and unloading. The number of filling pots was 120 once, and the average filling time was 40 s. The average filling mass was 1.881 kg, 0.006 kg different from the predicted value of 1.887 kg, and the relative error was 0.32%. The coefficient of variation of the mass was 2.97%, which was 0.12% different from the predicted value of 2.85%, and the relative error was 4.0%. This designed machine can provide a reference for developing and optimising the citrus substrate filling and transporting machine