Regions are Who Walk Them: a Large Pre-trained Spatiotemporal Model Based on Human Mobility for Ubiquitous Urban Sensing

User profiling and region analysis are two tasks of significant commercial value. However, in practical applications, modeling different features typically involves four main steps: data preparation, data processing, model establishment, evaluation, and optimization. This process is time-consuming and labor-intensive. Repeating this workflow for each feature results in abundant development time for tasks and a reduced overall volume of task development. Indeed, human mobility data contains a wealth of information. Several successful cases suggest that conducting in-depth analysis of population movement data could potentially yield meaningful profiles about users and areas. Nonetheless, most related works have not thoroughly utilized the semantic information within human mobility data and trained on a fixed number of the regions. To tap into the rich information within population movement, based on the perspective that Regions Are Who walk them, we propose a large spatiotemporal model based on trajectories (RAW). It possesses the following characteristics: 1) Tailored for trajectory data, introducing a GPT-like structure with a parameter count of up to 1B; 2) Introducing a spatiotemporal fine-tuning module, interpreting trajectories as collection of users to derive arbitrary region embedding. This framework allows rapid task development based on the large spatiotemporal model. We conducted extensive experiments to validate the effectiveness of our proposed large spatiotemporal model. It's evident that our proposed method, relying solely on human mobility data without additional features, exhibits a certain level of relevance in user profiling and region analysis. Moreover, our model showcases promising predictive capabilities in trajectory generation tasks based on the current state, offering the potential for further innovative work utilizing this large spatiotemporal model.Comment: 8 page

MLFLHMDA: predicting human microbe-disease association based on multi-view latent feature learning

IntroductionA growing body of research indicates that microorganisms play a crucial role in human health. Imbalances in microbial communities are closely linked to human diseases, and identifying potential relationships between microbes and diseases can help elucidate the pathogenesis of diseases. However, traditional methods based on biological or clinical experiments are costly, so the use of computational models to predict potential microbe-disease associations is of great importance.MethodsIn this paper, we present a novel computational model called MLFLHMDA, which is based on a Multi-View Latent Feature Learning approach to predict Human potential Microbe-Disease Associations. Specifically, we compute Gaussian interaction profile kernel similarity between diseases and microbes based on the known microbe-disease associations from the Human Microbe-Disease Association Database and perform a preprocessing step on the resulting microbe-disease association matrix, namely, weighting K nearest known neighbors (WKNKN) to reduce the sparsity of the microbe-disease association matrix. To obtain unobserved associations in the microbe and disease views, we extract different latent features based on the geometrical structure of microbes and diseases, and project multi-modal latent features into a common subspace. Next, we introduce graph regularization to preserve the local manifold structure of Gaussian interaction profile kernel similarity and add Lp,q-norms to the projection matrix to ensure the interpretability and sparsity of the model.ResultsThe AUC values for global leave-one-out cross-validation and 5-fold cross validation implemented by MLFLHMDA are 0.9165 and 0.8942+/−0.0041, respectively, which perform better than other existing methods. In addition, case studies of different diseases have demonstrated the superiority of the predictive power of MLFLHMDA. The source code of our model and the data are available on https://github.com/LiangzheZhang/MLFLHMDA_master

Overexpression of the Gene Encoding Solanum lycopersicum Carotenoid Cleavage Dioxygenase 1A (SlCCD1A) Regulates Tomato Flavor Quality

SlCCD1A-OE lines of cherry tomato CI1005 and large-fruited tomato AC were selected to investigate the effect of SlCCD1A on tomato flavor quality. The transgenic plants were identified by real-time polymerase chain reaction (PCR), and the volatile components and major quality traits of the transgenic lines were determined. The results showed that SlCCD1A-OE mainly cleaved lycopene and β-carotene in tomato fruit, and increased the contents of 11 volatile isoprene compounds, including 6-methyl-5-heptene-2-one, compared with the wild type (WT). The total amount of these isoprene compounds was the highest in strain OE-3 from CI1005, which was 5.68 times of that in WT, and it increased 1.88 times in OE-8 from AC compared with WT, significantly enhancing the floral, fruity, and sweet aroma. The soluble solid content, total sugar content, and sugar/acid ratio in strain OE-3 from CI1005 increased to 6.2%, 37.34 mg/g, and 10.37, while the contents of total acid and vitamin C (VC) decreased to 0.36 mg/L and 42.10 mg/L, respectively. The soluble solid content, total sugar content, and sugar/acid ratio in strain OE-7 from AC increased to 4.77%, 20.03 mg/g, and 5.23, while the total acid and VC contents decreased to 0.38 mg/L and 37.10 mg/L, respectively, resulting in high-sweet and low-sour taste. The SlCCD1A gene is beneficial for enhancing the content and richness of carotenoid-derived volatiles in tomato fruit and increasing the contents of soluble solids and total sugars, thereby improving the flavor quality

Parallelized Hybrid Monte Carlo Simulation of Stress-Induced Texture Evolution

A parallelized hybrid Monte Carlo (HMC) methodology is devised to quantify the microstructural evolution of polycrystalline material under elastic loading. The approach combines a time explicit material point method (MPM) for the mechanical stresses with a calibrated Monte Carlo (cMC) model for grain boundary kinetics. The computed elastic stress generates an additional driving force for grain boundary migration. The paradigm is developed, tested, and subsequently used to quantify the effect of elastic stress on the evolution of texture in nickel polycrystals. As expected, elastic loading favors grains which appear softer with respect to the loading direction. The rate of texture evolution is also quantified, and an internal variable rate equation is constructed which predicts the time evolution of the distribution of orientations.Comment: 20 pages, 8 figure

Generic and Dynamic Graph Representation Learning for Crowd Flow Modeling

Many deep spatio-temporal learning methods have been proposed for crowd flow modeling in recent years. However, most of them focus on designing a spatial and temporal convolution mechanism to aggregate information from nearby nodes and historical observations for a pre-defined prediction task. Different from the existing research, this paper aims to provide a generic and dynamic representation learning method for crowd flow modeling. The main idea of our method is to maintain a continuous-time representation for each node, and update the representations of all nodes continuously according to the streaming observed data. Along this line, a particular encoder-decoder architecture is proposed, where the encoder converts the newly happened transactions into a timestamped message, and then the representations of related nodes are updated according to the generated message. The role of the decoder is to guide the representation learning process by reconstructing the observed transactions based on the most recent node representations. Moreover, a number of virtual nodes are added to discover macro-level spatial patterns and also share the representations among spatially-interacted stations. Experiments have been conducted on two real-world datasets for four popular prediction tasks in crowd flow modeling. The result demonstrates that our method could achieve better prediction performance for all the tasks than baseline methods

Template-Free Preparation of α-Ni(OH)₂ Nanosphere as High-Performance Electrode Material for Advanced Supercapacitor

Although it is one of the promising candidates for pseudocapacitance materials, Ni(OH)2 is confronted with poor specific capacitance and inferior cycling stability. The design and construction of three-dimensional (3D) nanosphere structures turns out to be a valid strategy to combat these disadvantages and has attracted tremendous attention. In this paper, a 3D α-Ni(OH)2 nanosphere is prepared via a facile and template-free dynamic refluxing approach. Significantly, the α-Ni(OH)2 nanosphere possesses a high specific surface area (119.4 m2/g) and an abundant porous structure. In addition, the as-obtained α-Ni(OH)2 electrodes are investigated by electrochemical measurements, which exhibit a high specific capacitance of 1243 F/g at 1 A/g in 6 M KOH electrolyte and an acceptable capacitive retention of 40.0% after 1500 charge/discharge cycles at 10 A/g, which can be attributed to the sphere’s unique nanostructure. Furthermore, the as-assembled Ni(OH)2-36//AC asymmetric supercapacitor (ASC) yields a remarkable energy density of 26.50 Wh/kg, with a power density of 0.82 kW/kg. Notably, two ASCs in series can light a 2.5 V red lamp sustainably for more than 60 min, as well as power an LED band with a rated power of 25 W. Hence, this 3D α-Ni(OH)2 nanosphere may raise great potential applications for next-generation energy storage devices

Data_Sheet_1_MLFLHMDA: predicting human microbe-disease association based on multi-view latent feature learning.ZIP

IntroductionA growing body of research indicates that microorganisms play a crucial role in human health. Imbalances in microbial communities are closely linked to human diseases, and identifying potential relationships between microbes and diseases can help elucidate the pathogenesis of diseases. However, traditional methods based on biological or clinical experiments are costly, so the use of computational models to predict potential microbe-disease associations is of great importance.MethodsIn this paper, we present a novel computational model called MLFLHMDA, which is based on a Multi-View Latent Feature Learning approach to predict Human potential Microbe-Disease Associations. Specifically, we compute Gaussian interaction profile kernel similarity between diseases and microbes based on the known microbe-disease associations from the Human Microbe-Disease Association Database and perform a preprocessing step on the resulting microbe-disease association matrix, namely, weighting K nearest known neighbors (WKNKN) to reduce the sparsity of the microbe-disease association matrix. To obtain unobserved associations in the microbe and disease views, we extract different latent features based on the geometrical structure of microbes and diseases, and project multi-modal latent features into a common subspace. Next, we introduce graph regularization to preserve the local manifold structure of Gaussian interaction profile kernel similarity and add Lp,q-norms to the projection matrix to ensure the interpretability and sparsity of the model.ResultsThe AUC values for global leave-one-out cross-validation and 5-fold cross validation implemented by MLFLHMDA are 0.9165 and 0.8942+/−0.0041, respectively, which perform better than other existing methods. In addition, case studies of different diseases have demonstrated the superiority of the predictive power of MLFLHMDA. The source code of our model and the data are available on https://github.com/LiangzheZhang/MLFLHMDA_master.</p

VO₂(B)/Graphene Composite-Based Symmetrical Supercapacitor Electrode via Screen Printing for Intelligent Packaging

More multipurpose and convenient demand driven by Radio Frequency Identification (RFID) and intelligent packaging require flexible power sources. A VO2(B)/graphene (VO2(B)/GN) core-shell composite was successfully synthesized by the hydrothermal treatment with V2O5 and graphite. The as-obtained sample was characterized by XRD, FT-IR, SEM, TEM, and XPS measurements. In addition, the electrochemical properties of VO2(B)/GN were tested. Due to its great electrochemical performance and mechanical properties, graphene could increase the electrochemical performance and strengthen the structural stability of the material at the same time. With increasing loading amount of GN, the specific capacitance of VO2(B)/GN increased correspondingly. With 20% GN loading, the initial discharge specific capacity could reach 197 F g−1 at 0.5 A g−1, and 160 F g−1 at 1 A g−1 in 0.5 M Na2SO4 electrolyte, which is better than that of pure rod-like VO2(B). The capacitance of the VO2(B)/GN (20%) composite electrode retains 95.49% after 1000 cycles, which is higher than that of a pure VO2(B) electrode (85.43%), indicating that the VO2(B)/GN composite possesses better cycling stability. Moreover, a symmetrical solid-state supercapacitor (SCs) using VO2(B)/GN(20%) as the anode was assembled. Four printed SCs were connected in series to light up a 1.5 V red LED. This demonstrates its potential application in intelligent packaging to trace food safety