Link-Family Safety Mobile Application Design

The safety and health of children are essential issues not only for households but also for society. Missing Child is a severe strike to a family. It causes severe trauma memory and affects family members both psychologically and physically. Incidents like this also spread a feeling of fear among the local community. Many efforts have been made to help with this problem, such as the Amber Alert system, the location-tracking functions in the operating system in smartphones. There are also many safety apps in the app stores trying to prevent this kind of incident. However, there are still things that these apps do not cover: What should the parents do when they lose contact with their children and feel worried? This project explored ways to provide guidance for parents and tried to find a way to balance the privacy-safety tradeoff

Monte Carlo localization algorithm based on particle swarm optimization

In wireless sensor networks, Monte Carlo localization for mobile nodes has a large positioning error and slow convergence speed. To address the challenges of low sampling efficiency and particle impoverishment, a time sequence Monte Carlo localization algorithm based on particle swarm optimization (TSMCL-BPSO) is proposed in this paper. Firstly, the sampling region is constructed according to the overlap of the initial sampling region and the Monte Carlo sampling region. Then, particle swarm optimization (PSO) strategy is adopted to search the optimum position of the target node. The velocity of particle swarm is updated by adaptive step size and the particle impoverishment is improved by distributed estimation and particle replication, which avoids the local optimum caused by the premature convergence of particles. Experiment results indicate that the proposed algorithm improves the particle fitness, increases the particle searching efficiency, and meanwhile the lower positioning error can be obtained at the node\u27s maximum speed of 70 m/s

Effect of in vitro gastrointestinal digestion on the chemical composition and antioxidant properties of Ginkgo biloba leaves decoction and commercial capsules

In this study Ginkgo biloba leaves (GBL) decoction and commercial capsules were digested using an in vitro model. Thirty-six active compounds were identified and quantified by HPLC-ESI-MS analysis based on the MS/MS patterns (precursor ions and product ions) and retention times, in comparison with reference standards. Most compounds in GBL showed a significant decrease during intestinal digestion, with an exception of vanillic acid and biflavonoids. Bioaccessibility values of chemical compositions varied between decoction and capsules samples. Also, significant reductions of total flavonoids and total phenolic content was observed after in vitro digestion. Both, 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid (ABTS) scavenging capacity decreased after gastric digestion, but increased during intestinal digestion. Nevertheless, different behaviour was observed in reducing antioxidant power (FRAP) assay. Compared to the pH of digestion, the influence of digestive enzymes on the chemical composition and antioxidant activity of GBL was relatively minor. Overall, these results may help provide a valid foundation for further investigations on bioactive compounds and the pharmacodynamics of GBL

Determining the Optimal N Input to Improve Grain Yield and Quality in Winter Wheat With Reduced Apparent N Loss in the North China Plain

Excessive or improper nitrogen (N) application rates negatively affect crop production and thereby environmental quality, particularly for winter wheat production in the North China Plain. Therefore, it is very important to optimize N fertilizer input to balance grain yield, environmental risk, and benefits under irrigated conditions. Three long-term stationary field experiments including five N levels, from 0 to 300 kg ha-1 [0 (N0), 90 (N90), 180 (N180), 240 (N240), and 300 (N300) kg ha-1] were carried out to investigate the effects of N regime on wheat yield, photosynthesis, and N balance at different sites. The grain yield and protein content increased quadratically with N rate, and the maximum values were 8087 kg ha-1 and 13.9% at N application rates of 250 and 337 kg N ha-1, respectively. N application increased the photosynthetic fluorescence parameters (Pn, Gs, and Tr) and N metabolism enzyme activities (NR and GS) which then increased grain yield. The leaching of soil nitrate into the deeper soil layers ( > 100 cm) increased with higher N fertilization and experimental years. The partial factor productivity (PFPN) was decreased by N because the apparent N loss increased with N application rate. In order to balance grain yield, N use efficiency (NUE), and N loss, the recommended N rate should be 120–171 kg N ha-1, and the corresponding yields and apparent N loss were 7278–7787 ka ha-1 and 22–37 kg ha-1, respectively

Root Growth, Water and Nitrogen Use Efficiencies in Winter Wheat Under Different Irrigation and Nitrogen Regimes in North China Plain

Excessive nitrogen (N) application combined with water shortage has a negative effect on crop production, particularly wheat (Triticum aestivum L.) production in the North China Plain. This study examined root growth and water and nitrogen use efficiencies in wheat grown on loam soil in the North China Plain, from 2012 to 2014 using a fixed-position experiment initiated in 2010. The experiment followed a completely randomized split-plot design with four replications, taking irrigation [no irrigation (W0) versus irrigation at jointing plus flowering (W2)] as the main plot and N treatment (0, 180, 240, and 300 kg N ha-1) as the subplot. Compared with W0, W2 increased grain yield and root weight density (RWD) by up to 91.3 and 57.7% in 2012–2013, and 15.5 and 43.0% in 2013–2014, respectively, across all N application rates. Irrigation had no effect on grain water use efficiency (WUEY), but caused a decrease in biomass WUE at vegetative growth stage (WUEF) and at grain-filling stage (WUEM). Significant improvements in grain yield and biomass WUE during vegetative growth stage, and reductions in nitrogen-use efficiency (NUE) and RWD, were observed with increasing N application. Compared with non-N treatment, N treatment increased yield by up to 98.9 and 93.7% in 2012–2013 and 2013–2014, respectively, decreasing RWD by 12.0 and 16.9%. Correlation analysis further revealed that RWD was positively correlated with grain yield, evapotranspiration (ET) and NUE. NUE was also positively correlated with nitrogen uptake efficiency (UPE). Overall, the findings suggest that optimal N application improves NUE by increasing above–ground nitrogen uptake as a result of optimized RWD and a synchronous increase in WUE, thus increasing yield. Under the experimental conditions, an N application rate of 240 kg N ha-1 plus irrigation at jointing and flowering is recommended

Search for light dark matter from atmosphere in PandaX-4T

We report a search for light dark matter produced through the cascading decay of $\eta$ mesons, which are created as a result of inelastic collisions between cosmic rays and Earth's atmosphere. We introduce a new and general framework, publicly accessible, designed to address boosted dark matter specifically, with which a full and dedicated simulation including both elastic and quasi-elastic processes of Earth attenuation effect on the dark matter particles arriving at the detector is performed. In the PandaX-4T commissioning data of 0.63 tonne$\cdot$year exposure, no significant excess over background is observed. The first constraints on the interaction between light dark matter generated in the atmosphere and nucleus through a light scalar mediator are obtained. The lowest excluded cross-section is set at $5.9 \times 10^{-37}{\rm cm^2}$ for dark matter mass of $0.1$ MeV$/c^2$ and mediator mass of 300 MeV$/c^2$. The lowest upper limit of $\eta$ to dark matter decay branching ratio is $1.6 \times 10^{-7}$

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis

Motor imagery (MI) electroencephalography (EEG) is natural and comfortable for controllers, and has become a research hotspot in the field of the brain–computer interface (BCI). Exploring the inter-subject MI-BCI performance variation is one of the fundamental problems in MI-BCI application. EEG microstates with high spatiotemporal resolution and multichannel information can represent brain cognitive function. In this paper, four EEG microstates (MS1, MS2, MS3, MS4) were used in the analysis of the differences in the subjects’ MI-BCI performance, and the four microstate feature parameters (the mean duration, the occurrences per second, the time coverage ratio, and the transition probability) were calculated. The correlation between the resting-state EEG microstate feature parameters and the subjects’ MI-BCI performance was measured. Based on the negative correlation of the occurrence of MS1 and the positive correlation of the mean duration of MS3, a resting-state microstate predictor was proposed. Twenty-eight subjects were recruited to participate in our MI experiments to assess the performance of our resting-state microstate predictor. The experimental results show that the average area under curve (AUC) value of our resting-state microstate predictor was 0.83, and increased by 17.9% compared with the spectral entropy predictor, representing that the microstate feature parameters can better fit the subjects’ MI-BCI performance than spectral entropy predictor. Moreover, the AUC of microstate predictor is higher than that of spectral entropy predictor at both the single-session level and average level. Overall, our resting-state microstate predictor can help MI-BCI researchers better select subjects, save time, and promote MI-BCI development

External Memory Sort On CGM1 Clusters

External memory sort has been widely accepted as an overall benchmark to evaluate the processing performance of computers. Lots of algorithms have been developed to sort large scalable data in different environments. The bottlenecks of external memory sort are the I/O operation and communication cost. In this paper, we adapted HPVM MinuteSort[4], and borrowed the THsort[5] idea to develop our external memory sort algorithm, which minimizes the I/O and communication costs. We hope that we will get better performance than other algorithms.