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A recent study reported by Park et al. (Improved community detection using stochastic block models, Springer, Heidelberg, 2025) in Complex Networks and their Applications 2024 showed that clusterings from three Stochastic Block Models (SBMs) in graph-tool, a popular software package, often had internally disconnected clusters when used on large real-world or synthetic networks. To address this issue, Park et al. (Improved community detection using stochastic block models, Springer, Heidelberg, 2025) presented a simple technique, Well-Connected Clusters (WCC), that repeatedly finds and removes small edge cuts of size at most in clusters, where n is the number of nodes in the cluster, and showed that treatment of graph-tool SBM clusterings with WCC improves accuracy. Here we examine the question of cluster connectivity for clusterings computed using other SBM software or nested SBMs within graph-tool. Our study, using a wide range of real-world and synthetic networks ranging up to more than a million nodes, shows that all tested SBM clustering methods frequently produce communities that are disconnected, and that graph-tool improves on PySBM. We provide insight into why graph-tool degree-corrected SBM clustering produces disconnected clusters by examining the description length formula it uses, and explore the impact of modifications to the description length formula. Finally, we show that WCC generally provides an improvement in accuracy for both flat and nested SBMs, except for cases where nearly all nodes in the network are in very sparse ground-truth clusters. We also demonstrate that WCC scales to networks with millions of nodes.

Generating high-quality synthetic networks with realistic community structure is vital to effectively evaluate community detection algorithms. In this study, we propose a new synthetic network generator called the Edge-Connected Stochastic Block Model (EC-SBM). The goal of EC-SBM is to take a given clustered real-world network and produce a synthetic network that resembles the clustered real-world network with respect to both network and community-specific criteria. In particular, we focus on simulating the internal edge connectivity of the clusters in the reference clustered network. Our performance study on large real-world networks shows that EC-SBM is generally more accurate with respect to network and community criteria than currently used approaches for this problem. Furthermore, we demonstrate that EC-SBM can complete analyses on several real-world networks with millions of nodes.

This study presents the design and implementation of a compact data acquisition system for immersive ultrasonic inspection of small-diameter pipelines, targeting applications where conventional systems are impractical due to size constraints. The system integrates the Eclipse Z7 platform with a customized pulser-receiver module and a rotary pipeline inspection gauge (PIG) equipped with a 5 MHz immersion-type ultrasonic transducer. The PIG module is designed to scan pipelines with an 8.18 mm wall thickness and a 200 mm inner diameter. Before deployment, real-time system calibration is performed via a connected computer interface to ensure optimal performance. Once inside the pipeline, the PIG operates autonomously, with ultrasonic data being acquired and stored locally on a Raspberry Pi. Post-inspection, the recorded data is extracted and analyzed on the computer to assess pipeline integrity. The proposed system offers a compact alternative to commercial solutions, particularly in scenarios involving limited access and small-diameter pipelines.

The crystal, electronic and magnetic structures of solid oxygen in the epsilon phase have been investigated using the strongly constrained appropriately normed (SCAN) + rVV10 method and the generalized gradient approximation (GGA) + vdW-D + U method. The spin-polarized SCAN + rVV10 method with an 8-atom primitive unit cell provides lattice parameters consistent with the experimental results over the entire pressure range, including the epsilon-zeta structural phase transition at high pressure, but does not provide accurate values of the intermolecular distances d 1 and d 2 at low pressure. The agreement between the intermolecular distances and the experimental values is greatly improved when a 16-atom conventional unit cell is used. Therefore, the SCAN + rVV10 method with a 16-atom unit cell can be considered the most suitable model for the epsilon phase of solid oxygen. The spin-polarized SCAN + rVV10 model predicts a magnetic phase at low pressure. Since the lattice parameters of the predicted magnetic structure are consistent with the experimental lattice parameters measured at room temperature, our results may suggest that the epsilon phase is magnetic even at room temperature. The GGA + vdW-D + U (with an ad hoc value of U eff  = 2 eV at low pressure instead of the first-principles value of U lr eff ~ 9 eV) and hybrid functional methods provide similar results to the SCAN + rVV10 method; however, they do not provide reasonable values for the intermolecular distances.

Electronic structures of dense solid oxygen have been investigated up to 140 GPa with oxygen K-edge X-ray Raman scattering spectroscopy with the help of ab initio calculations based on density functional theory with semilocal metageneralized gradient approximation and nonlocal van der Waals density functionals. The present study demonstrates that the transition energies (Pi*, Sigma*, and the continuum) increase with compression, and the slopes of the pressure dependences then change at 94 GPa. The change in the slopes indicates that the electronic structure changes at the metallic transition. The change in the Pi* and Sigma* bands implies metallic characteristics of dense solid oxygen not only in the crystal a–b plane but also parallel to the c axis. The pressure evolution of the spectra also changes at ∼40 GPa. The experimental results are qualitatively reproduced in the calculations, indicating that dense solid oxygen transforms from insulator to metal via the semimetallic transition.

ObjectivesThis study aimed to determine doctors’ level of adherence to the natriuretic peptide testing guideline and to identify the factors influencing their adherence.DesignCross-sectional study.SettingWeb-based survey.ParticipantsFull-time doctors involved in heart failure (HF) treatment in Vietnam.Primary and secondary outcome measuresDoctors using natriuretic peptide testing for diagnosis, differential diagnosis, treatment assessment and prognosis of patients with HF were classified as adhering to the guidelines. We assessed the practice of natriuretic peptide testing and stratified the doctors based on their professional qualifications. Univariate and multivariate logistic regression analyses were used to estimate the Odds Ratio (ORs) and 95% CIs for associations between guideline adherence and selected covariates.ResultsOver half of the participants adhered to the natriuretic peptide testing guidelines (57.4%). Cardiologists adhered more closely to the guidelines than other professionals; they had approximately four times higher odds of adherence than other doctors (univariate model, OR: 3.88, 95% CI: 2.56 to 5.89, p<0.001; multivariate model, OR: 4.24, 95% CI: 2.64 to 6.82, p<0.001). Cardiologists also had significantly higher rates of using natriuretic peptide testing for diagnosis (93.8% vs 84.1%, p<0.002), differential diagnosis (71.4% vs 53.5%, p<0.001), treatment assessment (87% vs 64.2%, p<0.001) and prognosis (68.2% vs 50.4%, p<0.001) than other professionals. More years of professional experience correlated with higher guideline adherence (<2 years was used as a reference point; >5 to <10 years, OR: 2.59, 95% CI: 1.45 to 4.60, p<0.001; ≥10 years, OR: 2.30, 95% CI: 1.30 to 4.09, p<0.004).ConclusionThe level of adherence to natriuretic peptide testing guidelines among doctors treating patients with HF varies across Vietnam. Targeted interventions are needed to enhance understanding and proficiency, especially among non-cardiologists and those with limited experience. A dedicated fact sheet focusing on natriuretic peptide testing in HF management, separate from the existing guidelines, could bridge this gap.

The impact of dopant atoms in transistor functionality has significantly changed over the past few decades. In downscaled transistors, discrete dopants with uncontrolled positions and number induce fluctuations in device operation. On the other hand, by gaining access to tunneling through individual dopants, a new type of devices is developed: dopant-atom-based transistors. So far, most studies report transport through dopants randomly located in the channel. However, for practical applications, it is critical to control the location of the donors with simple techniques. Here, we fabricate silicon transistors with selectively nanoscale-doped channels using nano-lithography and thermal-diffusion doping processes. Coupled phosphorus donors form a quantum dot with the ground state split into a number of levels practically equal to the number of coupled donors, when the number of donors is small. Tunneling-transport spectroscopy reveals fine features which can be correlated with the different numbers of donors inside the quantum dot, as also suggested by first-principles simulation results.

Following the rapid development of the electronics industry and technology, it is expected that future electronic devices will operate based on functional units at the level of electrically active molecules or even atoms. One pathway to observe and characterize such fundamental operation is to focus on identifying isolated or coupled dopants in nanoscale silicon transistors, the building blocks of present electronics. Here, we review some of the recent progress in the research along this direction, with a focus on devices fabricated with simple and CMOS-compatible-processing technology. We present results from a scanning probe method (Kelvin probe force microscopy) which show direct observation of dopant-induced potential modulations. We also discuss tunneling transport behavior based on the analysis of low-temperature I-V characteristics for devices representative for different regimes of doping concentration, i.e., different inter-dopant coupling strengths. This overview outlines the present status of the field, opening also directions toward practical implementation of dopant-atom devices.

Multi-document summarization is a task aimed to extract the most salient information from a set of input documents. One of the main challenges in this task is the long-term dependency problem. When we deal with texts written in Vietnamese, it is also accompanied by the specific syllable-based text representation and lack of labeled datasets. Recent advances in machine translation have resulted in significant growth in the use of a related architecture known as the Transformer. Being pretrained on large amounts of raw texts, Transformers allow to capture a deep knowledge of the texts. In this paper, we survey the findings of language model applications for text summarization problems, including important Vietnamese text summarization models. According to the latter, we select LongT5 to pretrain and then fine-tune it for the Vietnamese multi-document text summarization problem from scratch. We analyze the resulting model and experiment with multi-document Vietnamese datasets, including ViMs, VMDS, and VLSP2022. We conclude that using a Transformer-based model pretrained on a large amount of unlabeled Vietnamese texts allows us to achieve promising results, with further enhancement via fine-tuning within a small amount of manually summarized texts. The pretrained model utilized in the experiment section has been made available online at https://github.com/nicolay-r/ViLongT5 .

Since true communities within real-world networks are rarely known, synthetic networks with planted ground truths are valuable for evaluating the performance of community detection methods. Of the synthetic network generation tools available, Stochastic Block Models (SBMs) produce networks with ground truth clusters that well approximate input parameters from real-world networks and clusterings. However, we show that SBMs can produce disconnected ground truth clusters, even when given parameters from clusterings where all clusters are connected. Here we describe the REalistic Cluster Connectivity Simulator (RECCS), a technique that modifies an SBM synthetic network to improve the fit to a given clustered real-world network with respect to edge connectivity within clusters, while maintaining the good fit with respect to other network and cluster statistics. Using real-world networks up to 13.9 million nodes in size, we show that RECCS, applied to stochastic block models, results in synthetic networks that have a better fit to cluster edge connectivity than unmodified SBMs, while providing roughly the same quality fit for other network and clustering parameters as unmodified SBMs.