Deep-neural-network solution of the ab initio nuclear structure

Predicting the structure of quantum many-body systems from the first principles of quantum mechanics is a common challenge in physics, chemistry, and material science. Deep machine learning has proven to be a powerful tool for solving condensed matter and chemistry problems, while for atomic nuclei, it is still quite challenging because of the complicated nucleon-nucleon interactions, which strongly couples the spatial, spin, and isospin degrees of freedom. By combining essential physics of the nuclear wave functions and the strong expressive power of artificial neural networks, we develop FeynmanNet, a novel deep-learning variational quantum Monte Carlo approach for \emph{ab initio} nuclear structure. We show that FeynmanNet can provide very accurate ground-state energies and wave functions for $^4$He, $^6$Li, and even up to $^{16}$O as emerging from the leading-order and next-to-leading-order Hamiltonians of pionless effective field theory. Compared to the conventional diffusion Monte Carlo approaches, which suffer from the severe inherent fermion-sign problem, FeynmanNet reaches such a high accuracy in a variational way and scales polynomially with the number of nucleons. Therefore, it paves the way to a highly accurate and efficient \emph{ab initio} method for predicting nuclear properties based on the realistic interactions between nucleons.Comment: 13 pages, 3 figure

Number of Repetitions in Re-randomization Tests

In covariate-adaptive or response-adaptive randomization, the treatment assignment and outcome can be correlated. Under this situation, re-randomization tests are a straightforward and attractive method to provide valid statistical inference. In this paper, we investigate the number of repetitions in the re-randomization tests. This is motivated by the group sequential design in clinical trials, where the nominal significance bound can be very small at an interim analysis. Accordingly, re-randomization tests lead to a very large number of required repetitions, which may be computationally intractable. To reduce the number of repetitions, we propose an adaptive procedure and compare it with multiple approaches under pre-defined criteria. Monte Carlo simulations are conducted to show the performance of different approaches in a limited sample size. We also suggest strategies to reduce total computation time and provide practical guidance in preparing, executing and reporting before and after data are unblinded at an interim analysis, so one can complete the computation within a reasonable time frame

Sectional variable frequency and voltage regulation control strategy for energy saving in beam pumping motor systems

Despite the fact that the energy losses in the beam pumping motor systems (BPMS) utilized in oil fields represent a monumental challenge industrially, very few studies discussed the feasibility and applicability of a universal energy saving technology for such industry. This study proposes a sectional control strategy integrating variable frequency (VF) with voltage regulation (VR) based on the mechanical load characteristics of the BPMS. Main merits of the proposed strategy are as follows: 1) controlling horse-head acceleration through VF, and indirectly weakening the inertia torque of polished rod load, thereby reducing the power consumption during the up-stroke; and 2) based on monitoring load conditions in real time, auto-tracking VR is adopted to optimize the online efficiency of the system. The proposed strategy utilized the adaptive fuzzy logic control to alternate between VF and VR modes. The proposed energy saving strategy was applied to a CYJ10 BPMS driven via a 37-kW induction motor in simulation and experimental environments. Results revealed that the effectiveness of the proposed strategy to improve the load balance effects through better utilization of the counterbalance during the heavy-loading conditions in up-stroke. Furthermore, the energy consumption is reduced via the auto-tracking of VR under light-loading conditions during the down-stroke. Moreover, the energy saving ratio is more than 10% under different dynamic liquid levels and counter weights. The effectiveness of the proposed strategy is verified through comparing the calculated results with the measured data for a standard oil rig, and the generality is verified as well

Mobile Metaverse: A Road Map from Metaverse to Metavehicles

With the rapid development of communication technologies and extended reality (XR), the services and applications of the Metaverse are gradually entering our lives. However, the current development of the Metaverse provides users with services that are homogeneous with the user experience that the Internet has brought in the past, making them more like an extension of the Internet. In addition, as a mobile application carrier for the Metaverse, it is also worth considering how vehicles with diverse onboard components can develop in synergy with the Metaverse. In this article, we focus on the core of the Metaverse, namely user experience, and provide a road map from Metaverse to Metaverse vehicles (Metavehicles). Specifically, we first elaborate on six features of the Metaverse from the perspective of user experience and propose a hierarchical framework for the Metaverse based on the evolutionary logic of the features. Under the guidance of this framework, we discuss the empowerment of onboard components of Metavehicles on the development of the Metaverse, and analyze the service experience that Metavehicles can bring to two types of users, namely drivers and passengers. Finally, considering the differentiated development levels of Metaverse and autonomous driving, we further establish a hierarchical framework for Metavehicles from three aspects (i.e., enhance Metaverse, enhance driving experience, and enhance entertainment experience), providing an evolutionary path for the development of Metavehicles.Comment: 7 pages, 5 figure

Deep learning for in vitro prediction of pharmaceutical formulations

Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error approach by individual experiences of pharmaceutical scientists, which is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of this research is to use deep learning to predict pharmaceutical formulations. In this paper, two different types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assessing the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. The result shows the accuracies of both two deep neural networks were above 80% and higher than other machine learning models, which showed good prediction in pharmaceutical formulations. In summary, deep learning with the automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was firstly developed for the prediction of pharmaceutical formulations. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical researches from experience-dependent studies to data-driven methodologies

Cubic ZrW1.75Mo0.25O8 from a Rietveld refinement based on neutron powder diffraction data

The solid solution in the system Zr–Mo–W–O with composition ZrW1.75Mo0.25O8 (zirconium tungsten molybdenum octa­oxide) was prepared by solid-state reactions as a polycrystalline material. Its structure has cubic symmetry (space group P213) at room temperature. The structure contains a network of corner-sharing ZrO6 octa­hedra (.3. symmetry) and MO4 (M = W, Mo) tetra­hedra (.3. symmetry). Along the main threefold axis of the cubic unit cell, the MO4 tetra­hedra are arranged in pairs forming M 2O8 units in which the M1O4 tetra­hedra have larger distortions in terms of bond distances and angles than the M2O4 tetra­hedra. These units are disordered over two possible orientations, with the M—Oterminal vectors pointing to the [111] or [ ] directions. The reversal of the orientations of the M 2O8 units results from the concerted flips of these units. The time-averaged proportions of flipped and unflipped M 2O8 units were determined and the fraction of unflipped M 2O8 units is about 0.95. The order degree of the M 2O8 unit orientation is about 0.9. During the reversal process, the M-atom site has a migration about 0.93 Å, one of the O-atom sites has a 0.25 Å migration distance, whereas two other O-atom sites migrate marginally (≃ 0.08 Å). The results prove the constraint strategy to be a reasonable approach based on the ratcheting mechanism

Study on overburden damage and prevention of runoff disaster in multiseam mining of gully region

Multi-seam mining in gully region has resulted in serious and complex chain disasters, including fissure development, mountain landslides, river blockage, and intensified water inflow. To prevent and control landslides and water inrush disasters, it is crucial to explore the characteristics and laws of overlying strata failure under the coupling effect of gully terrain and repeated mining in coal seams. This study focuses on the mining of multiseam in the gully terrain of Xiqu Coal Mine. The comprehensive analysis method, integrating surface exploration, InSAR dynamic observation, rainfall-runoff analysis, and numerical simulation, is used to analyze the entire process of spatial expansion of overlying strata failure and surface subsidence caused by downward mining of multiseam in the gully region. The results reveal that after the critical mining of the lower coal seam in the gully region, the lower strata beneath the key stratum in interlayered formations are prone to develop cutting failure and vertical fissure, with tensile cracking being the dominant mode of failure. The proportion of shear fractures in the overburden above the key stratum increases significantly, and the gully slope is prone to shear slip under the effects of mining subsidence and gravity. The connection phenomenon between the downward fractures of the slope and the upward fractures of the overburden can even occur. In addition, if the accumulation formed by mountain landslides due to repeated mining blocks the river channel and forms a barrier lake during the flood season, there is a risk of underground water inflow. To prevent such disasters, high-precision terrain synthesized by UAV tilt photogrammetry is used to simulate the rainfall inundation range and time percentage of different durations in Fanshigou watershed during the “100-year return period” rainstorm in Shanxi Province. The research proposes a comprehensive prevention and control method of surface runoff water disaster based on fissure development and surface inundation range, which provides support for gully water disaster prevention and risk assessment in Fanshigou small watershed. This study can serve as a useful reference for the prevention and control of surface geological disasters and the protection of water resources under the condition of multiseam mining in gully regions

Is there a correlation between socioeconomic disparity and functional outcome after acute ischemic stroke?

Background To investigate the impact of low socioeconomic status (SES), indicated by low level of education, occupation and income, on 3 months functional outcome after ischemic stroke. Methods We analyzed data from the China National Stroke Registry (CNSR), a multicenter and prospective registry of consecutive patients with acute cerebrovascular events occurred between September 2007 and August 2008. 11226 patients with ischemic stroke had SES and clinical characteristics data collected at baseline and mRS measured as indicator of functional outcome in 3 months follow up. Multinomial and ordinal logistic regression models were performed to examine associations between SES and the functional outcome. Results At 3 months after stroke, 5.3% of total patients had mRS scored at 5, 11.3% at score 4, 11.1% at score 3, 14.4% at score 2, 34.2% at score 1 and 23.7% at score 0. Compared to patients with educational level of ≥ 6 years and non-manual laboring, those < 6 years and manual laboring tended to have higher mRS score (P<0.001). Multinomial adjusted odds ratios (ORs) of outcome in manual workers were significantly increased (ORs from1.38 to 1.87), but OR in patients with less income was not significant. There were similar patterns of association The impact may be stronger in patients aged <65 years (P = 0.003, P<0.001 respectively) and being male (P = 0.001, P<0.001 respectively). Conclusions Our study provides evidence that people who are relatively more deprived in socioeconomic status suffer poorer outcome after ischemic stroke. The influence of low educational level and manual laboring can be more intensive than low income level on 3-month outcome. Health policy and service should target the deprived populations to reduce the public health burden in the society.This study is supported by grants from the Ministry of Science and Technology of the People’s Republic of China (2006BAI01A11, 2011BAI08B01, 2011BAI08B02, 2012ZX09303-005-001, and 2013BAI09B03), a grant from the Beijing Biobank of Cerebral Vascular Disease (D131100005313003) and a grant from Beijing Institute for Brain Disorders (BIBD-PXM2013_014226_07_000084

Socioeconomic Status and the Quality of Acute Stroke Care

Background and Purpose—The association of socioeconomic status (SES) with quality of stroke care is not well understood, and few studies have examined the association with different indicators of SES simultaneously. We assessed the impacts of low levels of education, occupation, and income on the quality of stroke care. Methods—We examined data from the China National Stroke Registry recording consecutive stroke patients between September 2007 and August 2008. Baseline low SES was measured using educational level <6 years, occupation as manual workers or no job, and average family income per capita at ≤¥1000 per month. Compliance with 11 performances was summarized in a composite score defined as the proportion of all needed care given. Poor quality of care was defined as having a composite score of 0.71 or less. Results—Among 12 270 patients with ischemic stroke, 38.6% had <6 educational years, 37.6% had manual workers/no job, and 34.7% had income ≤¥1000 per month. There was an increased chance of receiving poor quality of care in patients with low education (adjusted odds ratio 1.15, 95% confidence interval 1.03–1.28), low occupation (adjusted odds ratio 1.16, 95% confidence interval 1.01–1.32), and low income (adjusted odds ratio 1.18, 95% confidence interval 1.06–1.30), respectively. People with low SES had poor performances on some aspects of care quality. Combined effects existed among these SES indicators; those with low SES from all 3 indicators had the poorest quality of care. Conclusions—There was a social gradient in the quality of stroke care. Continuous efforts of socioeconomic improvement will increase the quality of acute stroke care.The Ministry of Science and Technology of the People’s Republic of China (2006BAI01A11, 2011BAI08B01, 2011BAI08B02, 2012ZX09303-005-001, and 2013BAI09B03), The Beijing Biobank of Cerebral Vascular Disease (D131100005313003), Beijing Institute for Brain Disorders (BIBD-PXM2013_014226_07_000084