Graph Construction with Flexible Nodes for Traffic Demand Prediction

Graph neural networks (GNNs) have been widely applied in traffic demand prediction, and transportation modes can be divided into station-based mode and free-floating traffic mode. Existing research in traffic graph construction primarily relies on map matching to construct graphs based on the road network. However, the complexity and inhomogeneity of data distribution in free-floating traffic demand forecasting make road network matching inflexible. To tackle these challenges, this paper introduces a novel graph construction method tailored to free-floating traffic mode. We propose a novel density-based clustering algorithm (HDPC-L) to determine the flexible positioning of nodes in the graph, overcoming the computational bottlenecks of traditional clustering algorithms and enabling effective handling of large-scale datasets. Furthermore, we extract valuable information from ridership data to initialize the edge weights of GNNs. Comprehensive experiments on two real-world datasets, the Shenzhen bike-sharing dataset and the Haikou ride-hailing dataset, show that the method significantly improves the performance of the model. On average, our models show an improvement in accuracy of around 25\% and 19.5\% on the two datasets. Additionally, it significantly enhances computational efficiency, reducing training time by approximately 12% and 32.5% on the two datasets. We make our code available at https://github.com/houjinyan/HDPC-L-ODInit

VDD: Varied Drone Dataset for Semantic Segmentation

Semantic segmentation of drone images is critical to many aerial vision tasks as it provides essential semantic details that can compensate for the lack of depth information from monocular cameras. However, maintaining high accuracy of semantic segmentation models for drones requires diverse, large-scale, and high-resolution datasets, which are rare in the field of aerial image processing. Existing datasets are typically small and focus primarily on urban scenes, neglecting rural and industrial areas. Models trained on such datasets are not sufficiently equipped to handle the variety of inputs seen in drone imagery. In the VDD-Varied Drone Dataset, we offer a large-scale and densely labeled dataset comprising 400 high-resolution images that feature carefully chosen scenes, camera angles, and varied light and weather conditions. Furthermore, we have adapted existing drone datasets to conform to our annotation standards and integrated them with VDD to create a dataset 1.5 times the size of fine annotation of Cityscapes. We have developed a novel DeepLabT model, which combines CNN and Transformer backbones, to provide a reliable baseline for semantic segmentation in drone imagery. Our experiments indicate that DeepLabT performs admirably on VDD and other drone datasets. We expect that our dataset will generate considerable interest in drone image segmentation and serve as a foundation for other drone vision tasks. VDD is freely available on our website at https://vddvdd.com

A Generalizability Analysis of the Mobile Phone Addiction Tendency Scale for Chinese College Students

College students’ mobile phone addiction is negatively associated with physical and mental health and academic performance. Many self-made questionnaires are currently being administered to Chinese college students to evaluate the mobile phone addiction tendency. Using the univariate generalizability theory and multivariate generalizability theory, this study investigated the psychometric properties and the internal structure of the Mobile Phone Addiction Tendency Scale (MPATS), the most widely used survey questionnaire assessing the status of Chinese college students’ mobile addiction. Data were a sample of 1,253 college students from the southwest of China. Primary analytic approaches included the generalizability design of univariate random measurement mode p × (i:h) and multivariate random measurement mode p˙ × i°. Results showed that the variance component of the participants and the variation related to the participants explained most of the variation of the scale, while the variance component of the items was small, and the generalizability coefficient and dependability index of the scale were 0.88 and 0.85. In the multivariate generalizability analysis, the variance component of the participants and the variation related to the participants accounted for most of the variation of the scale and the variance component of the items was small. The generalizability coefficients of withdrawal symptoms, salience, social comfort, and mood changes were 0.64–0.80, and the dependability indexes were 0.63–0.77. However, the generalizability coefficient and reliability index of universe score were 0.91 and 0.90. In addition, the contribution ratio of the four dimensions to the universe score variance was different from the assignment intention of the initial scale. Recommendations were discussed on the improvement of the test reliability for each dimension

Selection of Suitable Reference Genes for Quantitative Real-time PCR in Sapium sebiferum

Chinese tallow (Sapium sebiferum L.) is a promising landscape and bioenergy plant. Measuring gene expression by quantitative real-time polymerase chain reaction (qRT-PCR) can provide valuable information on gene function. Stably expressed reference genes for normalization are a prerequisite for ensuring the accuracy of the target gene expression level among different samples. However, the reference genes in Chinese tallow have not been systematically validated. In this study, 12 candidate reference genes (18S, GAPDH, UBQ, RPS15, SAND, TIP41, 60S, ACT7, PDF2, APT, TBP, and TUB) were investigated with qRT-PCR in 18 samples, including those from different tissues, from plants treated with sucrose and cold stresses. The data were calculated with four common algorithms, geNorm, BestKeeper, NormFinder, and the delta cycle threshold (ΔCt). TIP41 and GAPDH were the most stable for the tissue-specific experiment, GAPDH and 60S for cold treatment, and GAPDH and UBQ for sucrose stresses, while the least stable genes were 60S, TIP41, and 18S respectively. The comprehensive results showed APT, GAPDH, and UBQ to be the top-ranked stable genes across all the samples. The stability of 60S was the lowest during all experiments. These selected reference genes were further validated by comparing the expression profiles of the chalcone synthase gene in Chinese tallow in different samples. The results will help to improve the accuracy of gene expression studies in Chinese tallow

A compendium of genetic regulatory effects across pig tissues

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.</p

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Cow Rump Identification Based on Lightweight Convolutional Neural Networks

Individual identification of dairy cows based on computer vision technology shows strong performance and practicality. Accurate identification of each dairy cow is the prerequisite of artificial intelligence technology applied in smart animal husbandry. While the rump of each dairy cow also has lots of important features, so do the back and head, which are also important for individual recognition. In this paper, we propose a non-contact cow rump identification method based on convolutional neural networks. First, the rump image sequences of the cows while feeding were collected. Then, an object detection model was applied to detect the cow rump object in each frame of image. Finally, a fine-tuned convolutional neural network model was trained to identify cow rumps. An image dataset containing 195 different cows was created to validate the proposed method. The method achieved an identification accuracy of 99.76%, which showed a better performance compared to other related methods and a good potential in the actual production environment of cow husbandry, and the model is light enough to be deployed in an edge-computing device

Should Live streaming be adopted for agricultural supply chain considering platform’s quality improvement and blockchain support?

Platforms empower farmers to address the quality and safety issues of agricultural products by offering blockchain technology and improving product quality. Recently, live streaming becomes a new sales channel to sell products, and plays a key role in enhancing the visibility of agricultural products and introducing detailed information of agricultural products in real time. This disrupted the long-term cooperative relationship between the platforms and farmers. Based on this, our paper explores that whether and how the agricultural supply chain members should adopt the live streaming channel. We build a game model that consists of a farmer, a platform, and an influencer, and investigate the live streaming introduction strategies in the agricultural supply chain in the context of the platform assisting the farmer (the improvement of quality level and adoption of blockchain). We capture two characteristics of the live streaming channel, i.e., the live streaming channel’s impact on market size (live streaming value) and additional profit per unit product derived from the influencer’s personal influence (influencer value). We interestingly find that when the live streaming value is low, introducing live streaming negatively impacts the farmer’s profit at the low influencer value in the farmer’s live streaming. When the live streaming value is low (high), introducing live streaming damages the platform’s profit at the moderate (low) influencer value in the platform’s live streaming. Therefore, adopting the live streaming channel is not necessarily profitable for agricultural supply chain members. We also extend our model to check the robustness of our findings. This study is the first to explore live streaming introduction strategies in the agricultural supply chain in the context of the platform assisting the farmer, and significantly contributes to the literature and provides valuable guidance for farmers and platforms on when to adopt the live streaming channel in the agricultural supply chain