Unified analysis of finite-size error for periodic Hartree-Fock and second order M{\o}ller-Plesset perturbation theory

Despite decades of practice, finite-size errors in many widely used electronic structure theories for periodic systems remain poorly understood. For periodic systems using a general Monkhorst-Pack grid, there has been no comprehensive and rigorous analysis of the finite-size error in the Hartree-Fock theory (HF) and the second order M{\o}ller-Plesset perturbation theory (MP2), which are the simplest wavefunction based method, and the simplest post-Hartree-Fock method, respectively. Such calculations can be viewed as a multi-dimensional integral discretized with certain trapezoidal rules. Due to the Coulomb singularity, the integrand has many points of discontinuity in general, and standard error analysis based on the Euler-Maclaurin formula gives overly pessimistic results. The lack of analytic understanding of finite-size errors also impedes the development of effective finite-size correction schemes. We propose a unified analysis to obtain sharp convergence rates of finite-size errors for the periodic HF and MP2 theories. Our main technical advancement is a generalization of the result of [Lyness, 1976] for obtaining sharp convergence rates of the trapezoidal rule for a class of non-smooth integrands. Our result is applicable to three-dimensional bulk systems as well as low dimensional systems (such as nanowires and 2D materials). Our unified analysis also allows us to prove the effectiveness of the Madelung-constant correction to the Fock exchange energy, and the effectiveness of a recently proposed staggered mesh method for periodic MP2 calculations [Xing, Li, Lin, J. Chem. Theory Comput. 2021]. Our analysis connects the effectiveness of the staggered mesh method with integrands with removable singularities, and suggests a new staggered mesh method for reducing finite-size errors of periodic HF calculations

QTL identification and candidate gene identification for monoterpene content in grape (Vitis vinifera L.) berries

Great progress has been made during the last decade in clarifying the molecular details of aroma accumulation in grape berries. However, the multigene complex controlling monoterpene accumulation in grape is not well understood. To shed light on this issue, the grapes of 149 F1 progenies from the cross 87-1 (Vitis vinifera L.) × 9-22 (Vitis vinifera L.) were characterized at the mature stage for three representative free monoterpenes during five growing seasons. A total of 202, 184 and 255 polymorphic SSR (simple sequence repeat) markers were contracted on the maternal 87-1, paternal 9-22 and consensus genetic maps, respectively. On the consensus map, we confirmed a major QTL (quantitative trait locus) for free linalool, nerol and α-terpineol content on linkage group (LG) 5, and a stable QTL for free linalool and α-terpineol was detected on LG 10. In addition, two new stable QTLs for free monoterpene (linalool, nerol and α-terpineol) contents were identified on LG 11 and LG 18 that explained up to 42.5 % of the total variance. Eleven promising candidate genes related to pentatricopeptide repeat (PPR)-containing proteins, seed maturation protein, RING finger protein, and AP2/ERF transcription factors might be potentially involved in monoterpene accumulation. The stable QTLs and candidate genes identified in this study provide new insights into free monoterpene accumulation in grape

Overall PSD and Fractal Characteristics of Tight Oil Reservoirs: A Case Study of Lucaogou Formation in Junggar Basin, China

Lucaogou tight oil reservoir, located in the Junggar Basin, Northwest of China, is one of the typical tight oil reservoirs. Complex lithology leads to a wide pore size distribution (PSD), ranging from several nanometers to hundreds of micrometers. To better understand PSD and fractal features of Lucaogou tight oil reservoir, the experiment methods including scanning electron microscope (SEM), rate-controlled mercury injection (RMI) and pressure-controlled mercury injection (PMI) were performed on the six samples with different lithology. The results indicate that four types of pores exist in Lucaogou tight oil reservoir, including dissolution pores, clay dominated pores, microfractures and inter-granular pores. A combination of PMI and RMI was proposed to calculate the overall PSD of tight oil reservoirs, the overall pore radius of Lucaogou tight oil reservoir ranges from 3.6 nm to 500µm. The fractal analysis was carried out based on the PMI data. Fractal dimension (Fd) values varied between 2.843 and 2.913 with a mean value of 2.88. Fd increases with a decrease of quartz content and an increase of clay mineral content. Samples from tight oil reservoirs with smaller average pore radius have stronger complexity of pore structure. Fractal dimension shows negative correlations with porosity and permeability. In addition, fractal characteristics of different tight reservoirs were compared and analyzed

A routing strategy for spatial networks based on harmonic centrality

With the rapid development of networks, the traffic in the networks has increased sharply, resulting in frequent congestion, especially in spatial networks, such as the railway network, aviation network, and sensor network, and congestion not only affects the user’s experience but also causes serious economic losses. Therefore, in this paper, we effectively identify the high-load nodes in spatial networks by considering harmony centrality and degree. On this basis, we design the HD routing strategy by avoiding these key nodes, which can enhance the traffic throughput of spatial networks efficiently. The results provide new ideas and directions for the design of routing strategies for spatial networks

Dual drug delivery from hydrophobic and hydrophilic intraocular lenses: in-vitro and in-vivo studies

Posterior capsule opacification (PCO) still remains the most frequent long term complication after cataract surgery, while endophthalmitis is rare but severe and should be prevented at all cost. Intraocular lenses (IOLs) with different designs (eg. edge and body-haptics angle) and materials (acrylic hydrophobic and acrylic hydrophilic surfaces) have been studied to reduce PCO. For the prevention of endophthalmitis, intracameral injection followed or not by topical treatment with antibiotics and anti-inflammatories are usually prescribed. The objective of this work was to investigate the use of IOLs as controlled release platforms of two drugs, the antibiotic moxifloxacin (MXF) and the anti-inflammatory ketorolac (KTL) that could advantageously substitute the usual treatment. Two types of IOLs were chosen, hydrophobic and hydrophilic. Hydrophobic IOLs have shown better results in the prevention of PCO because they adhere better to the posterior capsular bag, while hydrophilic IOLs are advised in the case of patients with uveitis, glaucoma or diabetes. The IOLs were loaded with MXF + KTL and sterilized by high hydrostatic pressure. Both IOLs reduced the tendency for adhesion of LECs. In vivo tests were done to compare the concentration of the drugs in the aqueous humor obtained after eye drops administration and drug-loaded IOLs implantation. The developed IOLs were able to release MXF and KTL at therapeutic levels, in a sustained way, which contrasts with the eye drops prophylaxis. No PCO signs were detected and histological analyses demonstrated biocompatibility of these devices.publishe

Laminated Ti-Al composites: Processing, structure and strength

Laminated Ti-Al composite sheets with different layer thickness ratios have been fabricated through hot pressing followed by multi-pass hot rolling at 500 °C.The laminated sheets show strong bonding with intermetallic interface layers of nanoscale thickness between the layers of Ti and Al. The mechanical properties of the composites with different volume fractions of Al from 10% to 67% show a good combination of strength and ductility. A constraint strain in the hot-rolled laminated structure between the hard and soft phases introduces an elastic-plastic deformation stage, which becomes more pronounced as the volume fraction of Al decreases. Moreover, the thin intermetallic interface layer may also contribute to the strength of the composites, and this effect increases with increasing volume fraction of the interface layer

Prediction of high-Tc superconductivity in ternary lanthanum borohydrides

The study of superconductivity in compressed hydrides is of great interest due to measurements of high critical temperatures (Tc) in the vicinity of room temperature, beginning with the observations of LaH10 at 170-190 GPa. However, the pressures required for synthesis of these high Tc superconducting hydrides currently remain extremely high. Here we show the investigation of crystal structures and superconductivity in the La-B-H system under pressure with particle-swarm intelligence structure searches methods in combination with first-principles calculations. Structures with six stoichiometries, LaBH, LaBH3, LaBH4, LaBH6, LaBH7 and LaBH8, were predicted to become stable under pressure. Remarkably, the hydrogen atoms in LaBH8 were found to bond with B atoms in a manner that is similar to that in H3S. Lattice dynamics calculations indicate that LaBH7 and LaBH8 become dynamically stable at pressures as low as 109.2 and 48.3 GPa, respectively. Moreover, the two phases were predicted to be superconducting with a critical temperature (Tc) of 93 K and 156 K at 110 GPa and 55 GPa, respectively. Our results provide guidance for future experiments targeting new hydride superconductors with both low synthesis pressures and high Tc.Comment: 16 pages, 5 figures

Stigmatized Stroke? A Qualitative Study of Perception of Stroke Among Community Residents With Hypertension

Objectives: To understand the perception of stroke in the hypertensive population. Hypertension is the primary risk factor for stroke, and current approaches to stroke prevention are inadequate and often fragmented. Understanding the perception of stroke among individuals with hypertension is crucial for a targeted approach. However, empirical evidence on this perception is limited.Methods: A qualitative design involved thematic analysis of focus groups and interview data from urban China with hypertension. Audio recordings were transcribed and subjected to thematic analysis.Results: Three themes were identified. Hypertensive participants first identified stroke patients by their obvious physical disability, and then identified the disease as a negative thing. Finally, they wanted to stay away from stroke, but paradoxically, there is a contradictory approach to avoidance and prevention, such as being willing to prevent the disease or simply avoiding socializing with stroke patients.Conclusion: Hypertensive patients hold complex and diverse perceptions of stroke, including a certain stigma. Future public health education should prioritize improving media promotion and fostering interaction between patients with hypertension and stroke in the community

Single-image based deep learning for precise atomic defects identification

Defect engineering has been profoundly employed to confer desirable functionality to materials that pristine lattices inherently lack. Although single atomic-resolution scanning transmission electron microscopy (STEM) images are widely accessible for defect engineering, harnessing atomic-scale images containing various defects through traditional image analysis methods is hindered by random noise and human bias. Yet the rise of deep learning (DL) offering an alternative approach, its widespread application is primarily restricted by the need for large amounts of training data with labeled ground truth. In this study, we propose a two-stage method to address the problems of high annotation cost and image noise in the detection of atomic defects in monolayer 2D materials. In the first stage, to tackle the issue of data scarcity, we employ a two-state transformation network based on U-GAT-IT for adding realistic noise to simulated images with pre-located ground truth labels, thereby infinitely expanding the training dataset. In the second stage, atomic defects in monolayer 2D materials are effectively detected with high accuracy using U-Net models trained with the data generated in the first stage, avoiding random noise and human bias issues. In both stages, we utilize segmented unit-cell-level images to simplify the model's task and enhance its accuracy. Our results demonstrate that not only sulfur vacancies, we are also able to visualize oxygen dopants in monolayer MoS2, which are usually overwhelmed by random background noise. As the training was based on a few segmented unit-cell-level realistic images, this method can be readily extended to other 2D materials. Therefore, our results outline novel ways to train the model with minimized datasets, offering great opportunities to fully exploit the power of machine learning (ML) applicable to a broad materials science community