Vibrational properties of the phononic crystal structural cavity

This paper discusses the construction of a model of phononic crystals and the calculation of the band gap by the finite element method. The physical parameters on the band structure are studied in order to find the proper material suitable for a low frequency vibration. We investigate modal analysis, forbidden band gap characteristics, and the resonance mechanism of the crystal’s cavity. We compare the results of the experiments with those obtained for the phononic crystal cavity, such as the use of crystals on the roof or the floor. This study intends to make phononic crystal cavity applicable for engineers, especially in vehicles

Vibrational properties of the phononic crystal structural cavity

This paper discusses the construction of a model of phononic crystals and the calculation of the band gap by the finite element method. The physical parameters on the band structure are studied in order to find the proper material suitable for a low frequency vibration. We investigate modal analysis, forbidden band gap characteristics, and the resonance mechanism of the crystal’s cavity. We compare the results of the experiments with those obtained for the phononic crystal cavity, such as the use of crystals on the roof or the floor. This study intends to make phononic crystal cavity applicable for engineers, especially in vehicles

PYATB: An Efficient Python Package for Electronic Structure Calculations Using Ab Initio Tight-Binding Model

We present PYATB, a Python package designed for computing band structures and related properties of materials using the ab initio tight-binding Hamiltonian. The Hamiltonian is directly obtained after conducting self-consistent calculations with first-principles packages using numerical atomic orbital (NAO) bases, such as ABACUS. The package comprises three modules: Bands, Geometric, and Optical. In the Bands module, one can calculate essential properties of band structures, including the partial density of states (PDOS), fat bands, Fermi surfaces, and Weyl/Dirac points. The band unfolding method is utilized to obtain the energy band spectra of a supercell by projecting the electronic structure of the supercell onto the Brillouin zone of the primitive cell. With the Geometric module, one can compute the Berry phase and Berry curvature-related quantities, such as electric polarization, Wilson loops, Chern numbers, and anomalous Hall conductivities. The Optical module offers a range of optical property calculations, including optical conductivity and nonlinear optical responses, such as shift current and Berry curvature dipole

Domain Adaptation for Time Series Forecasting via Attention Sharing

Recent years have witnessed deep neural networks gaining increasing popularity in the field of time series forecasting. A primary reason of their success is their ability to effectively capture complex temporal dynamics across multiple related time series. However, the advantages of these deep forecasters only start to emerge in the presence of a sufficient amount of data. This poses a challenge for typical forecasting problems in practice, where one either has a small number of time series, or limited observations per time series, or both. To cope with the issue of data scarcity, we propose a novel domain adaptation framework, Domain Adaptation Forecaster (DAF), that leverages the statistical strengths from another relevant domain with abundant data samples (source) to improve the performance on the domain of interest with limited data (target). In particular, we propose an attention-based shared module with a domain discriminator across domains as well as private modules for individual domains. This allows us to jointly train the source and target domains by generating domain-invariant latent features while retraining domain-specific features. Extensive experiments on various domains demonstrate that our proposed method outperforms state-of-the-art baselines on synthetic and real-world datasets.Comment: 19 pages, 9 figure

ScalAna: Automating Scaling Loss Detection with Graph Analysis

Scaling a parallel program to modern supercomputers is challenging due to inter-process communication, Amdahl's law, and resource contention. Performance analysis tools for finding such scaling bottlenecks either base on profiling or tracing. Profiling incurs low overheads but does not capture detailed dependencies needed for root-cause analysis. Tracing collects all information at prohibitive overheads. In this work, we design ScalAna that uses static analysis techniques to achieve the best of both worlds - it enables the analyzability of traces at a cost similar to profiling. ScalAna first leverages static compiler techniques to build a Program Structure Graph, which records the main computation and communication patterns as well as the program's control structures. At runtime, we adopt lightweight techniques to collect performance data according to the graph structure and generate a Program Performance Graph. With this graph, we propose a novel approach, called backtracking root cause detection, which can automatically and efficiently detect the root cause of scaling loss. We evaluate ScalAna with real applications. Results show that our approach can effectively locate the root cause of scaling loss for real applications and incurs 1.73% overhead on average for up to 2,048 processes. We achieve up to 11.11% performance improvement by fixing the root causes detected by ScalAna on 2,048 processes.Comment: conferenc

PreDiff: Precipitation Nowcasting with Latent Diffusion Models

Earth system forecasting has traditionally relied on complex physical models that are computationally expensive and require significant domain expertise. In the past decade, the unprecedented increase in spatiotemporal Earth observation data has enabled data-driven forecasting models using deep learning techniques. These models have shown promise for diverse Earth system forecasting tasks but either struggle with handling uncertainty or neglect domain-specific prior knowledge, resulting in averaging possible futures to blurred forecasts or generating physically implausible predictions. To address these limitations, we propose a two-stage pipeline for probabilistic spatiotemporal forecasting: 1) We develop PreDiff, a conditional latent diffusion model capable of probabilistic forecasts. 2) We incorporate an explicit knowledge control mechanism to align forecasts with domain-specific physical constraints. This is achieved by estimating the deviation from imposed constraints at each denoising step and adjusting the transition distribution accordingly. We conduct empirical studies on two datasets: N-body MNIST, a synthetic dataset with chaotic behavior, and SEVIR, a real-world precipitation nowcasting dataset. Specifically, we impose the law of conservation of energy in N-body MNIST and anticipated precipitation intensity in SEVIR. Experiments demonstrate the effectiveness of PreDiff in handling uncertainty, incorporating domain-specific prior knowledge, and generating forecasts that exhibit high operational utility.Comment: Technical repor

Intermittent-Hypoxia-Induced Autophagy Activation Through the ER-Stress-Related PERK/eIF2α/ATF4 Pathway is a Protective Response to Pancreatic β-Cell Apoptosis

Background/Aims: Intermittent hypoxia (IH) causes apoptosis in pancreatic β-cells, but the potential mechanisms remain unclear. Endoplasmic reticulum (ER) stress, autophagy, and apoptosis are interlocked in an extensive crosstalk. Thus, this study aimed to investigate the contributions of ER stress and autophagy to IH-induced pancreatic β-cell apoptosis. Methods: We established animal and cell models of IH, and then inhibited autophagy and ER stress by pharmacology and small interfering RNA (siRNA) in INS-1 cells and rats. The levels of biomarkers for autophagy, ER stress, and apoptosis were evaluated by immunoblotting and immunofluorescence. The number of autophagic vacuoles was observed by transmission electron microscopy. Results: IH induced autophagy activation both in vivo and in vitro, as evidenced by increased autophagic vacuole formation and LC3 turnover, and decreased SQSTM1 level. The levels of ER-stress-related proteins, including GRP78, CHOP, caspase 12, phosphorylated (p)-protein kinase RNA-like ER kinase (PERK), p-eIF2α, and activating transcription factor 4 (ATF4) were increased under IH conditions. Inhibition of ER stress with tauroursodeoxycholic acid or 4-phenylbutyrate partially blocked IH-induced autophagy in INS-1 cells. Furthermore, inhibition of PERK with GSK2606414 or siRNA blocked the ERstress-related PERK/eIF2α/ATF4 signaling pathway and inhibited autophagy induced by IH, which indicates that IH-induced autophagy activation is dependent on this signaling pathway. Promoting autophagy with rapamycin alleviated IH-induced apoptosis, whereas inhibition of autophagy with chloroquine or autophagy-related gene (Atg5 and Atg7) siRNA aggravated pancreatic β-cell apoptosis caused by IH. Conclusion: IH induces autophagy activation through the ER-stress-related PERK/eIF2α/ATF4 signaling pathway, which is a protective response to pancreatic β-cell apoptosis caused by IH

MicroRNA-483 amelioration of experimental pulmonary hypertension.

Endothelial dysfunction is critically involved in the pathogenesis of pulmonary arterial hypertension (PAH) and that exogenously administered microRNA may be of therapeutic benefit. Lower levels of miR-483 were found in serum from patients with idiopathic pulmonary arterial hypertension (IPAH), particularly those with more severe disease. RNA-seq and bioinformatics analyses showed that miR-483 targets several PAH-related genes, including transforming growth factor-β (TGF-β), TGF-β receptor 2 (TGFBR2), β-catenin, connective tissue growth factor (CTGF), interleukin-1β (IL-1β), and endothelin-1 (ET-1). Overexpression of miR-483 in ECs inhibited inflammatory and fibrogenic responses, revealed by the decreased expression of TGF-β, TGFBR2, β-catenin, CTGF, IL-1β, and ET-1. In contrast, inhibition of miR-483 increased these genes in ECs. Rats with EC-specific miR-483 overexpression exhibited ameliorated pulmonary hypertension (PH) and reduced right ventricular hypertrophy on challenge with monocrotaline (MCT) or Sugen + hypoxia. A reversal effect was observed in rats that received MCT with inhaled lentivirus overexpressing miR-483. These results indicate that PAH is associated with a reduced level of miR-483 and that miR-483 might reduce experimental PH by inhibition of multiple adverse responses

Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction

Protein structure-based property prediction has emerged as a promising approach for various biological tasks, such as protein function prediction and sub-cellular location estimation. The existing methods highly rely on experimental protein structure data and fail in scenarios where these data are unavailable. Predicted protein structures from AI tools (e.g., AlphaFold2) were utilized as alternatives. However, we observed that current practices, which simply employ accurately predicted structures during inference, suffer from notable degradation in prediction accuracy. While similar phenomena have been extensively studied in general fields (e.g., Computer Vision) as model robustness, their impact on protein property prediction remains unexplored. In this paper, we first investigate the reason behind the performance decrease when utilizing predicted structures, attributing it to the structure embedding bias from the perspective of structure representation learning. To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures. Extensive experiments have shown that our framework is model-agnostic and effective in improving the property prediction of both predicted structures and experimental structures. The benchmark datasets and codes will be released to benefit the community