Graph Neural Network for spatiotemporal data: methods and applications

In the era of big data, there has been a surge in the availability of data containing rich spatial and temporal information, offering valuable insights into dynamic systems and processes for applications such as weather forecasting, natural disaster management, intelligent transport systems, and precision agriculture. Graph neural networks (GNNs) have emerged as a powerful tool for modeling and understanding data with dependencies to each other such as spatial and temporal dependencies. There is a large amount of existing work that focuses on addressing the complex spatial and temporal dependencies in spatiotemporal data using GNNs. However, the strong interdisciplinary nature of spatiotemporal data has created numerous GNNs variants specifically designed for distinct application domains. Although the techniques are generally applicable across various domains, cross-referencing these methods remains essential yet challenging due to the absence of a comprehensive literature review on GNNs for spatiotemporal data. This article aims to provide a systematic and comprehensive overview of the technologies and applications of GNNs in the spatiotemporal domain. First, the ways of constructing graphs from spatiotemporal data are summarized to help domain experts understand how to generate graphs from various types of spatiotemporal data. Then, a systematic categorization and summary of existing spatiotemporal GNNs are presented to enable domain experts to identify suitable techniques and to support model developers in advancing their research. Moreover, a comprehensive overview of significant applications in the spatiotemporal domain is offered to introduce a broader range of applications to model developers and domain experts, assisting them in exploring potential research topics and enhancing the impact of their work. Finally, open challenges and future directions are discussed

Environmental and economic dispatching strategy for power system with the complementary combination of wind-solar-hydro-thermal-storage multiple sources

The linkage, coordination, and complementary cooperation of energy supply can improve the efficiency of transportation and utilization. At present, the level of new energy consumption needs to be improved, the coordination of the source network load storage link is insufficient, and the insufficient complementarity of various types of power sources in the power system. This article fully explores the differences and complementarities of various types of wind-solar-hydro-thermal-storage power sources, a hierarchical environmental and economic dispatch model for the power system has been established. Among them, the upper level model takes the flexible consumption of new energy as the optimization goal, the middle level model uses a combination of hydropower station and energy storage to minimizes the fluctuation variance and peak-to-valley difference of the net load curve, the lower level model aims to achieve optimal environmental and economic benefits of the power system, comprehensively considers the coal consumption cost, startup and shutdown cost, energy storage operation cost, and pollutant emissions of thermal power units, determines the startup and shutdown mode and output power of thermal power units. Finally, an improved IEEE 6-machine 30-node system is used as an example for simulation analysis, the results show that after applying the proposed hierarchical environmental and economic dispatch strategy of the power system, the fluctuation variance and the peak-to-valley difference of the net load curve have been reduced by 46.3% and 31.5%, respectively, and the environmental and economic benefits of the system is improved by 5.1% compared with the traditional economic dispatch strategy. It can meet the requirements of energy system cleaning and decarbonization while improving the operation economy, which verifies the effectiveness of the proposed environmental economic dispatch model

XkitS：A computational storage framework for high energy physics based on EOS storage system

Large-scale high-energy physics experiments generate scientific data at the scale of petabytes or even exabytes, requiring high-performance data IO for processing. However, in large computing centers, computing and storage devices are typically separated. Large-scale data transfer has become a bottleneck for some data-intensive computing tasks, such as data encoding and decoding, compression, sorting, etc. The time spent on data transfer can account for 50% of the entire computing task. The larger the amount of data accessed, the more significant this cost becomes. One attractive solution to address this problem is to offload a portion of data processing to the storage layer. However, modifying traditional storage systems to support computation offloading is often cumbersome and requires a broad understanding of their internal principles. Therefore, we have designed a flexible software framework called XkitS, which builds a computable storage system by extending the existing storage system EOS. This framework is deployed on the EOS FTS storage server and offloads computational tasks by invoking the computing capabilities (CPU, FPGA, etc.) on FTS. Currently, it has been tested and applied in the data processing of the Large High Altitude Air Shower Observatory (LHAASO), and the results show that the time spent on data decoding using the computable storage technology is half of that using the original method

A Randomized Trial of Hypnosedation Versus General Anesthesia for Women with Breast Cancer Undergoing Surgery

Reshonda Smith and Karen Cleckler-Hughes pictured.https://openworks.mdanderson.org/aprn-week-23/1003/thumbnail.jp

Combined treatment for at-risk drinking and smoking cessation among Puerto Ricans: A randomized clinical trial

Tobacco and alcohol use are linked behaviors that individually and synergistically increase the risk for negative health consequences. This study was a two-group, randomized clinical trial evaluating the efficacy of a behavioral intervention, “Motivation And Problem Solving Plus” (MAPS+), designed to concurrently address smoking cessation and the reduction of at-risk drinking. Targeted interventions may promote coaction, the likelihood that changing one behavior (smoking) increases the probability of changing another behavior (alcohol use). Puerto Ricans (N=202) who were smokers and at-risk drinkers were randomized to standard MAPS treatment focused exclusively on smoking cessation (S-MAPS), or MAPS+, focused on cessation and at-risk drinking reduction. Drinking outcomes included: number of at-risk drinking behaviors, heavy drinking, binge drinking, and drinking and driving. MAPS+ did not have a significant main effect on reducing at-risk drinking relative to S-MAPS. Among individuals who quit smoking, MAPS+ reduced the number of drinking behaviors, the likelihood of meeting criteria for heavy drinking relative to S-MAPS, and appeared promising for reducing binge drinking. MAPS+ did not improve drinking outcomes among individuals who were unsuccessful at quitting smoking. MAPS+ showed promise in reducing at-risk drinking among Puerto Rican smokers who successfully quit smoking, consistent with treatment enhanced coaction. Integrating an alcohol intervention into cessation treatment did not reduce engagement in treatment, or hinder cessation outcomes, and positively impacted at-risk drinking among individuals who quit smoking. Findings of coaction between smoking and drinking speak to the promise of multiple health behavior change interventions for substance use treatment and chronic disease prevention

Growth Pattern Analysis of Murine Lung Neoplasms by Advanced Semi-Automated Quantification of Micro-CT Images

Computed tomography (CT) is a non-invasive imaging modality used to monitor human lung cancers. Typically, tumor volumes are calculated using manual or semi-automated methods that require substantial user input, and an exponential growth model is used to predict tumor growth. However, these measurement methodologies are time-consuming and can lack consistency. In addition, the availability of datasets with sequential images of the same tumor that are needed to characterize in vivo growth patterns for human lung cancers is limited due to treatment interventions and radiation exposure associated with multiple scans. In this paper, we performed micro-CT imaging of mouse lung cancers induced by overexpression of ribonucleotide reductase, a key enzyme in nucleotide biosynthesis, and developed an advanced semi-automated algorithm for efficient and accurate tumor volume measurement. Tumor volumes determined by the algorithm were first validated by comparison with results from manual methods for volume determination as well as direct physical measurements. A longitudinal study was then performed to investigate in vivo murine lung tumor growth patterns. Individual mice were imaged at least three times, with at least three weeks between scans. The tumors analyzed exhibited an exponential growth pattern, with an average doubling time of 57.08 days. The accuracy of the algorithm in the longitudinal study was also confirmed by comparing its output with manual measurements. These results suggest an exponential growth model for lung neoplasms and establish a new advanced semi-automated algorithm to measure lung tumor volume in mice that can aid efforts to improve lung cancer diagnosis and the evaluation of therapeutic responses

Effects Of Mammalian Ribonucleotide Reductase Deregulation On Redox Homeostasis And Genomic Integrity

Ribonucleotide reductase catalyzes the rate-limiting step in de novo deoxyribonucleoside triphosphate (dNTPs) biosynthesis and is essential for providing balanced dNTP pools for nuclear and mitochondrial genome maintenance. RNR contains two components R1 and R2. R2 generates free radicals that are transferred to R1 and used for catalysis. RNR is tightly regulated through several mechanisms, including the control of R2 expression levels. Broad overexpression of R2 in transgenic mice causes lung neoplasms through a mutagenic mechanism. Because R2 produces free radicals, I hypothesized that R2 deregulation results in mutagenic perturbations of cellular redox status which could contribute to R2-induced tumorigenesis. This dissertation aims to (A) elucidate the effect of RNR deregulation on redox homeostasis and dissect the molecular mechanism of RNR-induced mutagenesis and tumorigenesis, and (B) use RNR mice as a lung tumor model in imaging studies to assess lung tumor growth patterns. For the first aim, we generated cells that overexpress R2 and showed that this overexpression leads to increased reactive oxygen species (ROS) production. By generating a series of R2 mutants, we subsequently identified the source of R2-induced ROS production. In addition, some R2 mutants showed dominant negative effects by interfering with endogenous RNR, leading to mitochondrial DNA depletion and mitochondrial redox imbalance. These findings indicate the importance of RNR regulation in maintaining cellular ROS levels and suggest the possibility that R2-induced ROS may play a role in mutagenesis. For the second aim, we adapted an automated algorithm for the measurement of pulmonary nodules on human chest CT scans and used it to measure mouse lung tumors. Euthanized mice were first imaged to optimize scan parameters and refine computational algorithms for tumor volume measurement. Lung tumor-bearing mice were then scanned sequentially for tumor growth rate determination. Findings from this study establish new automated algorithms to measure lung tumor volume in mice and confirm an exponential growth model for murine lung neoplasms. Together, these studies demonstrate the importance of RNR regulation in maintaining cellular redox homeostasis and genome integrity, and that RNR mice serve as an authentic model of human lung cancer in translational studies

Principle of multi-critical-points in the ALP-Higgs model and the corresponding phase transition

The principle of multi-critical-points (PMCP) may be a convincing approach to determine the emerging parameter values in different kinds of beyond-standard-model (BSM) models. This could certainly be applied to solve the problem of undetermined new parameters in the ALP-Higgs interaction models. In this paper, we apply this principle to such model and investigate whether there are suitable solutions. Then, using the 1-loop effective potential, we study the phase transition property of this model under the PMCP requirement. It is gratifying to find that under the requirement of PMCP, the phase transition can be not only first-order, but also strong enough to serve as a solution for electroweak baryongenesis (EWBG). Finally, we show the parameter space of ALP and provide the parameter range that leads to the first-order phase transition

Robust State Estimation for Uncertain Discrete Linear Systems with Delayed Measurements

Measurement delays and model parametric uncertainties are meaningful issues in actual systems. Addressing the simultaneous existence of random model parametric uncertainties and constant measurement delay in the discrete-time linear systems, this study proposes a novel robust estimation method based on the combination of Kalman filter regularized least-squares (RLS) framework and state augmentation. The state augmentation method is elaborately designed, and the cost function is improved by considering the influence of modelling errors. A recursive program similar to the Kalman filter is derived. Meanwhile, the asymptotic stability conditions of the proposed estimator and the boundedness conditions of its error covariance are analyzed theoretically. Numerical simulation results show that the proposed method has a better processing capability for measurement delay and better robustness to model parametric uncertainties than the Kalman filter based on nominal parameters