National Center for High-Performance Computing

Covalent organic frameworks (COFs) with tailored functional groups represented promising electrode materials for aqueous zinc-ion batteries (AZIBs). However, while prevailing research on linker and vertex chemistries─especially enhancing redox-active sites and electron conduction─the potential of linkage chemistry in regulating Zn2+/H+ transport kinetics remained fundamentally underexplored. Herein, a covalent organic framework (HPP-COF) featuring hexaazatriphenylene as redox-active vertex center and electron-positive pyridinamine linkage was fabricated as the electrode material for AZIBs. The high-density redox-active sites, coupled with effective electron delocalization and intermolecular π–π interactions, collectively ensure substantial and stable H+/Zn2+ storage. Furthermore, the electron-positive nature of the pyridinamine linkage modified the local electronic environment to accelerate redox kinetics at the electron-negative hexaazatriphenylene center. Consequently, HPP-COF achieved a high reversible capacity (309 mAh g–1 at 0.025 A g–1), outstanding rate performance (57 mAh g–1 at 5 A g–1), and exceptional cycling stability with 80.1% capacity retention after 6000 cycles at 2 A g–1. Mechanistic studies further revealed a multistep co-storage mechanism of Zn2+ and H+ with the C═N groups, involving the initial coordination of Zn2+ followed by the binding of H+. This work provides new insights into the rational linkage design of COF electrodes for enhancing the electrochemical performance.

Primary aldosteronism (PA) has been linked with reduced health-related quality of life (QoL) and increased prevalence of anxiety and depression. While most studies compared patients with PA to healthy subjects, this study focused on comparison with hypertensive patients without PA to identify PA-specific effects. Furthermore, the impact of targeted therapy on QoL and mental health was evaluated. Mental and physical health-related QoL, anxiety and presence of depressive and panic symptoms were assessed using the SF-12, GAD-7 and PHQ questionnaires in 925 patients with arterial hypertension tested for PA in a prospective, multi-center international prospective cohort study. Questionnaires were assessed at baseline as well as 3-6 months after initiation of targeted therapy. Amongst 833 of 925 patients who completed the necessary diagnostic procedures, 211 were diagnosed with PA. 98 of 116 patients with unilateral PA received unilateral adrenalectomy. At baseline, QoL, anxiety and mental health scores did not differ in patients with PA vs patients with hypertension without PA. At outcome assessment, SF-12 mental and physical component summaries as well as GAD-7 specifically improved in patients with unilateral PA after adrenalectomy (mean Δ = 4.2, p = 0.018, Δ = 3.6, p = 0.038 and Δ = -1.9, p = 0.006, respectively) in contrast to no improvement in patients with PA on MRA treatment. There was no difference in health-related QoL, anxiety and mental health in patients with hypertension with versus without PA. Unilateral adrenalectomy but not medical therapy improved health-related QoL and decreased anxiety in patients with PA.

To protect the services and data of the main servers, Rapidly Deployed Cyber Attack Bait is proposed for the unpredictable cybersecurity attack in this research. Based on Software Defined Network, the data packet and operation of a cybersecurity attack could be redirected to the on demand deployed bait container. By suitable artificial intelligence strategy, the idle bait containers corresponding to the type of cybersecurity attack can be on demand deployed automatically and rapidly. Verification shows the feasibility of the virtualized bait container could be about 1.16 times to 2 times enough for the future coming attacker and also record the all operations of the cybersecurity attack individually.

Gastric cancer, with disproportionately higher incidence in East Asia, arises from complex host-microbiome-environment interactions beyond Helicobacter pylori (HP) infection. However, the molecular architecture linking environmental carcinogens, microbial succession and host response remains unclear. To delineate multifactorial aetiologies and clinically actionable subtypes/biomarkers of gastric cancer through integrative proteogenomic, microbial and environmental exposure profiling. We established a multiomics atlas of paired tumour, adjacent mucosa tissues and blood from 154 treatment-naïve Taiwanese patients, integrating whole-exome sequencing, RNA-seq, proteome and phosphoproteome profiling with carcinogen signatures, HP status, microbiome composition and refined anatomical mapping. Cell-based functional assays tested carcinogen effects. Microbial subtype was assessed in an independent cohort. A polycyclic-aromatic-hydrocarbon signature, dibenz[a,h]acridine, emerged as a high-risk exposure promoting invasion, immune suppression and poor survival, significantly exceeding nitrosamine-linked risk in this cohort. Multilayer integration defined three initiation ecologies: HP-driven inflammatory, non-HP microbiome-enriched immune-silent and HP-free microbially depleted states. Among HP-negative tumours, a Streptococcus-enriched subtype associated with tight-junction (CLDN18.2/ZO-1/OCLN) disruption and epithelial-mesenchymal transition, whereas a subset of clinically aggressive cases retained CLDN18.2-high epithelial-stable subtype for therapeutic accessibility. An independent cohort revealed gastric juice-derived Streptococcus anginosus abundance inversely correlated with tight-junction proteins. Anatomical mapping reveals location-specific, sex-specific, subtype-specific oncogenic networks and kinase activity, including CDK4 activation in clinical biomarker-negative tumours. Decision-tree models combining exposure and proteome-immune states refined recurrence and survival prediction beyond stage. This proteogenomic framework defines exposure-informed and microbiome-informed gastric cancer subtypes, providing a molecular schema for patient stratification, prevention and actionable therapeutic vulnerabilities.

Wide bandgap (WBG) semiconductors such as 4H-SiC MOSFETs are key enablers for next-generation power electronics due to their superior efficiency and high-temperature capability. However, their electrical performance is strongly affected by process variations, and conventional Technology Computer-Aided Design (TCAD) simulations are computationally demanding and difficult to scale. This work presents a novel explainable machine learning framework that integrates artificial neural networks (ANNs) with explainable artificial intelligence (XAI) to enable accurate and interpretable device modeling. The ANN is trained on comprehensive TCAD-generated datasets covering a broad range of structural and doping parameters, while SHapley Additive exPlanations (SHAP) are employed to quantify the influence of each design parameter on electrical characteristics. The proposed model achieves a Pearson correlation coefficient exceeding 0.99 for on-state current prediction, and SHAP analysis reveals physically consistent trends such as the inverse dependence of drain current on oxide thickness and channel length. This study is among the first to apply XAI for interpreting design-performance correlations in SiC MOSFETs, establishing a transparent and data-driven framework for device understanding and optimization. The methodology can be readily extended to other semiconductor technologies where both modeling accuracy and interpretability are essential.

Abstract Within this study, we delve into novel optical solitons concerning the perturbed nonlinear Schrödinger equation, which governs the propagation of electromagnetic waves in magneto-optic waveguides. These components play vital roles in optical transmission lines and lasers, serving critical functions within integrated optical circuits as circulators, modulators, and isolators. Employing the simplest equation method, the 1 φ ( δ ) , φ ′ ( δ ) φ ( δ ) \left(\phantom{\rule[-0.75em]{}{0ex}},\frac{1}{\varphi \left(\delta )},\frac{\varphi ^{\prime} \left(\delta )}{\varphi \left(\delta )}\right) method, and the generalized Riccati equation mapping method, we derive single and multi-optical soliton solutions. Our analysis yields a diverse array of solutions, including multi-soliton, singular, combo bright-dark, periodic singular, bright, and dark optical solitons. Additionally, we discuss the parametric conditions crucial for both the existence and shaping of these solitons. The visual representation of our findings through three-dimensional, two-dimensional, and contour plots elucidates the dynamic phenomena and interprets the physical implications of these solutions using various parameter values. This research demonstrates the efficacy of rational analytical methods in elucidating the intricate dynamics of soliton solutions in nonlinear optical systems, thereby offering valuable insights for further exploration in the fields of physics, mathematics, sciences, and engineering.

The field of autonomous driving has seen continuous advances, yet achieving higher levels of automation in real-world applications remains challenging. A critical requirement for autonomous navigation is accurate map construction, particularly in novel and unstructured environments. In recent years, Simultaneous Localization and Mapping (SLAM) has evolved to support diverse sensor modalities, with some implementations incorporating machine learning to improve performance. However, these approaches often demand substantial computational resources. The key challenge lies in achieving efficiency within resource-constrained environments while minimizing errors that could degrade downstream tasks. This paper presents an enhanced ORB-SLAM2 (Oriented FAST and Rotated BRIEF Simultaneous Localization and Mapping, version 2) algorithm implemented on a Raspberry Pi 3 (ARM A53 CPU) to improve mapping performance under limited computational resources. ORB-SLAM2 comprises four main stages: Tracking, Local Mapping, Loop Closing, and Full Bundle Adjustment (BA). The proposed improvements include employing a more efficient feature descriptor to increase stereo feature-matching rates and optimizing loop-closing parameters to reduce accumulated errors. Experimental results demonstrate that the proposed system achieves notable improvements on the Raspberry Pi 3 platform. For monocular SLAM, RMSE is reduced by 18.11%, mean error by 22.97%, median error by 29.41%, and maximum error by 17.18%. For stereo SLAM, RMSE decreases by 0.30% and mean error by 0.38%. Furthermore, the ROS topic frequency stabilizes at 10 Hz, with quad-core CPU utilization averaging approximately 90%. These results indicate that the system satisfies real-time requirements while maintaining a balanced trade-off between accuracy and computational efficiency under resource constraints.

In this work, we introduce the Quantum Adaptive Excitation Network (QAE-Net), a hybrid quantum-classical framework designed to enhance channel attention mechanisms in Convolutional Neural Networks (CNNs). QAE-Net replaces the classical excitation block of Squeeze-and-Excitation modules with a shallow Variational Quantum Circuit (VQC), leveraging quantum superposition and entanglement to capture higher-order inter-channel dependencies that are challenging to model with purely classical approaches. We evaluate QAE-Net on benchmark image classification tasks, including MNIST, FashionMNIST, and CIFAR-10, and observe consistent performance improvements across all datasets, with particularly notable gains on tasks involving three-channel inputs. Furthermore, experimental results demonstrate that increasing the number of variational layers in the quantum circuit leads to progressively higher classification accuracy, underscoring the expressivity benefits of deeper quantum models. These findings highlight the potential of integrating VQCs into CNN architectures to improve representational capacity while maintaining compatibility with nearterm quantum devices. The proposed approach is tailored for the Noisy Intermediate-Scale Quantum (NISQ) era, offering a scalable and feasible pathway for deploying quantum-enhanced attention mechanisms in practical deep learning workflows.

This paper demonstrates the cross-site heterogeneous P4-INT testbeds between NCHC of Taiwan and NICT of Japan. NCHC and NICT have constructed Tofino-based and BMv2-based testbeds, respectively. To explore P4 technology together, we interconnect the testbeds via the multiple submarine cables through Los Angeles and Singapore. To monitor the statistics of the international testbed, we apply the INT technology for collecting real-time data. The proposed mechanism can help us explore various routing optimization algorithms to improve network performance in the future.