Complex-valued K-means clustering of interpolative separable density fitting algorithm for large-scale hybrid functional enabled \textit{ab initio} molecular dynamics simulations within plane waves

K-means clustering, as a classic unsupervised machine learning algorithm, is the key step to select the interpolation sampling points in interpolative separable density fitting (ISDF) decomposition. Real-valued K-means clustering for accelerating the ISDF decomposition has been demonstrated for large-scale hybrid functional enabled \textit{ab initio} molecular dynamics (hybrid AIMD) simulations within plane-wave basis sets where the Kohn-Sham orbitals are real-valued. However, it is unclear whether such K-means clustering works for complex-valued Kohn-Sham orbitals. Here, we apply the K-means clustering into hybrid AIMD simulations for complex-valued Kohn-Sham orbitals and use an improved weight function defined as the sum of the square modulus of complex-valued Kohn-Sham orbitals in K-means clustering. Numerical results demonstrate that this improved weight function in K-means clustering algorithm yields smoother and more delocalized interpolation sampling points, resulting in smoother energy potential, smaller energy drift and longer time steps for hybrid AIMD simulations compared to the previous weight function used in the real-valued K-means algorithm. In particular, we find that this improved algorithm can obtain more accurate oxygen-oxygen radial distribution functions in liquid water molecules and more accurate power spectrum in crystal silicon dioxide compared to the previous K-means algorithm. Finally, we describe a massively parallel implementation of this ISDF decomposition to accelerate large-scale complex-valued hybrid AIMD simulations containing thousands of atoms (2,744 atoms), which can scale up to 5,504 CPU cores on modern supercomputers.Comment: 43 pages, 12 figure

A multiple-time-scale comparative study for the added value of magnetic resonance imaging-based radiomics in predicting pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer

ObjectiveRadiomics based on magnetic resonance imaging (MRI) shows potential for prediction of therapeutic effect to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC); however, thorough comparison between radiomics and traditional models is deficient. We aimed to construct multiple-time-scale (pretreatment, posttreatment, and combined) radiomic models to predict pathological complete response (pCR) and compare their utility to those of traditional clinical models.MethodsIn this research, 165 LARC patients undergoing nCRT followed by surgery were enrolled retrospectively, which were divided into training and testing sets in the ratio of 7:3. Morphological features on pre- and posttreatment MRI, coupled with clinical data, were evaluated by univariable and multivariable logistic regression analysis for constructing clinical models. Radiomic parameters were derived from pre- and posttreatment T2- and diffusion-weighted images to develop the radiomic signatures. The clinical-radiomics models were then generated. All the models were developed in the training set and then tested in the testing set, the performance of which was assessed using the area under the receiver operating characteristic curve (AUC). Radiomic models were compared with the clinical models with the DeLong test.ResultsOne hundred and sixty-five patients (median age, 55 years; age interquartile range, 47–62 years; 116 males) were enrolled in the study. The pretreatment maximum tumor length, posttreatment maximum tumor length, and magnetic resonance tumor regression grade were selected as independent predictors for pCR in the clinical models. In the testing set, the pre- and posttreatment and combined clinical models generated AUCs of 0.625, 0.842, and 0.842 for predicting pCR, respectively. The MRI-based radiomic models performed reasonably well in predicting pCR, but neither the pure radiomic signatures (AUCs, 0.734, 0.817, and 0.801 for the pre- and posttreatment and combined radiomic signatures, respectively) nor the clinical-radiomics models (AUCs, 0.734, 0.860, and 0.801 for the pre- and posttreatment and combined clinical-radiomics models, respectively) showed significant added value compared with the clinical models (all P > 0.05).ConclusionThe MRI-based radiomic models exhibited no definite added value compared with the clinical models for predicting pCR in LARC. Radiomic models can serve as ancillary tools for tailoring adequate treatment strategies

Associations between long-term blood pressure trajectory and all-cause and CVD mortality among old people in China

BackgroundOptimal blood pressure (BP) management strategy among the elderly remains controversial, with insufficient consideration of long-term BP trajectory. This study aimed to identify BP trajectory patterns as well as terminal BP trajectory among the Chinese elderly and to explore the relationships between BP trajectories and all-cause mortality and cardiovascular disease (CVD) mortality.MethodsWe included 11,181 participants older than 60 at baseline (mean age, 80.98 ± 10.71) with 42,871 routine BP measurements from the Chinese Longitudinal Healthy Longevity Survey. Latent class trajectory analysis and Cox proportional hazard model were conducted to identify trajectory patterns and their associations with mortality. Furthermore, we also applied mixed-effects model to identify terminal BP trajectories among the elderly.ResultsCompared with stable at normal high level trajectory, excess systolic BP (SBP) trajectory with decreasing trend was associated with a 34% (HR = 1.34, 95% CI: 1.23–1.45) higher risk of all-cause mortality. Considering the competing risk of non-CVD death, excess BP trajectory with decreasing trend had a more pronounced effect on CVD mortality, in which HR (95% CI) was 1.67 (1.17, 2.37). Similar results were also found in diastolic BP (DBP), pulse pressure (PP), and mean arterial pressure (MAP) trajectories. We further conducted a mixed-effects model and observed that SBP and PP trajectories first increased and began to decline slightly six years before death. In contrast, DBP and MAP showed continuous decline 15 years before death.ConclusionLong-term BP trajectory was associated with all-cause mortality, especially CVD mortality. Keeping a stable BP over time may be an important way for CVD prevention among the elderly

China's Investments in Germany and the Impact of the COVID-19 Pandemic

This paper analyses how China's investments in Germany have developed over time and the potential impact of the COVID-19 pandemic in this regard, based on four different datasets, including our own survey in mid-2020. Our analysis shows that Germany is currently one of the most attractive investment destinations for Chinese investors. Chinese state-owned enterprises have played an important role as investors in Germany – particularly in large-scale projects. The COVID-19 pandemic has had some negative but rather temporary effects on Chinese investments in Germany. Germany is expected to stay attractive to Chinese investors who seek to gain access to advanced technologies and know-how in the future

Mechanism of Rake Frame Shear Drainage during Gravity Dewatering of Ultrafine Unclassified Tailings for Paste Preparation

To study the mechanism of reverse percolation and drainage of unclassified tailings, improve the disposal concentration of tailings and solve the bottleneck in the development of filling technology, this study performed semi-industrial flocculation and sedimentation tests using macroscopic continuous thickener tests and a self-developed continuous thickener test platform to observe the evolution pattern and formation mechanism of unclassified tailings flocs. Then, in situ sampling was performed on the compressed thickener zone of tailings at the bottom of the bed with the help of industrial CT scanning tests and 3D images. Avizo software was used to establish the seepage channels and construct an evolutionary model to analyze the effect of tailings dewatering and concentration on tailings concentration from a microscopic perspective. The study shows that the distribution of seepage channels is closely related to the height of the bed. As the bed height increases, the bed concentration increases; shear has a significant effect on the water flow inside the pore space. After shear, the water between the sample pores has been discharged. Therefore, the flow rate is relatively slow. Shear produces pressure and tension effects, breaking the static equilibrium between flocs and water forming seepage channels. Shear can effectively break the floc structure and release the water so that the mutual position between flocs and water constantly changes, The concentration of the tailings bed is increased

Mechanism of Rake Frame Shear Drainage during Gravity Dewatering of Ultrafine Unclassified Tailings for Paste Preparation

To study the mechanism of reverse percolation and drainage of unclassified tailings, improve the disposal concentration of tailings and solve the bottleneck in the development of filling technology, this study performed semi-industrial flocculation and sedimentation tests using macroscopic continuous thickener tests and a self-developed continuous thickener test platform to observe the evolution pattern and formation mechanism of unclassified tailings flocs. Then, in situ sampling was performed on the compressed thickener zone of tailings at the bottom of the bed with the help of industrial CT scanning tests and 3D images. Avizo software was used to establish the seepage channels and construct an evolutionary model to analyze the effect of tailings dewatering and concentration on tailings concentration from a microscopic perspective. The study shows that the distribution of seepage channels is closely related to the height of the bed. As the bed height increases, the bed concentration increases; shear has a significant effect on the water flow inside the pore space. After shear, the water between the sample pores has been discharged. Therefore, the flow rate is relatively slow. Shear produces pressure and tension effects, breaking the static equilibrium between flocs and water forming seepage channels. Shear can effectively break the floc structure and release the water so that the mutual position between flocs and water constantly changes, The concentration of the tailings bed is increased

STATIC AND DYNAMIC MULTI-OBJECTIVE RELIABILITY TOPOLOGICAL OPTIMIZATION FOR TIPPING SHAFT STRUCTURE OF DUMP TRUCK

In view of the effect of random uncertain factors such as the material properties of the turning shaft and different load conditions on its performance, and reduce its weight to improve fuel economy.Through the finite element modeling of the dump truck, the load analysis of the turning shaft is carried out according to the limit force under the actual working conditions, the first-order second moment method is used for reliability analysis, and the reliability index is used to reflect the influence of uncertain factors. The stiffness and dynamic characteristic values under static multi-conditions are set as objective functions, and the volume fraction and reliability index are used as constraints. A comprehensive objective function is established based on the normalized sub-objectives of the compromise programming method, the weight coefficients of the sub-objectives are determined by the analytic hierarchy process, and the multi-objective topology optimization design based on reliability constraints is carried out on the flip-axis structure. The results show that, compared with the deterministic topology optimization, the stiffness of the turning axis and the natural frequencies of each order obtained by the reliability topology optimization are more significantly improved, and the mass is reduced by 28.96% under the condition of satisfying the reliability. The experimental test and theoretical analysis results are basically consistent, verifying the feasibility of multi-objective reliability topology optimization design

Proposed Quantum Twisting Scanning Probe Microscope over Twisted Bilayer Graphene

Twisted bilayer graphene (TBG) has the natural merits of tunable flat bands and localized states distributed as a triangular lattice. However, the application of this state remains obscure. By density functional theory (DFT) and pz orbital tight-binding model calculations, we investigate the tip-shaped electrostatic potential of top valence electrons of TBG at half filling. Adsorption energy scanning of molecules above the TBG reveals that this tip efficiently attracts molecules selectively to AA-stacked or AB-stacked regions. Tip shapes can be controlled by their underlying electronic structure, with electrons of low bandwidth exhibiting a more localized feature. Our results indicate that TBG tips offer applications in noninvasive and nonpolluting measurements in scanning probe microscopy and theoretical guidance for 2D material-based probes

Reliability-Aware Multipath Routing of Time-Triggered Traffic in Time-Sensitive Networks

With the development of industrial networks, the demands for strict timing requirements and high reliability in transmission become more essential, which promote the establishment of a Time-Sensitive Network (TSN). TSN is a set of standards with the intention of extending Ethernet for safety-critical and real-time applications. In general, frame replication is used to achieve fault-tolerance, while the increased load has a negative effect on the schedule synthesis phase. It is necessary to consider schedulability and reliability jointly. In this paper, a heuristic-based routing method is proposed to achieve fault tolerance by spatial redundancy for TSNs containing unreliable links. A cost function is presented to evaluate each routing set, and a heuristic algorithm is applied to find the solution with higher schedulability. Compared to the shortest path routing, our method can improve the reliability and the success rate of no-wait scheduling by 5–15% depending on the scale of topology