Browse Articles

Low-overload internal ballistics in UAV ejection using multiple time-sequenced compressed-air chambers

Growth mechanism and formaldehyde sensing of mixed phase cobalt oxide nanowalls

Streptococcus gordonii type VII secretion system substrate EsxA induces neutrophil extracellular trap formation in infective endocarditis

Native CFTR codon bias controls translation rate to balance off-pathway aggregation and channel function by conformational imprinting

Complexity of parental number talk predicts preschoolers’ gains in cardinal knowledge

Convergence analysis and application for high-order neural networks based on gradient descent learning algorithm via smooth regularization

Identification and removal of contamination in palaeoproteomic analysis of dental enamel

A qualitative exploration of deterrents to COVID-19 vaccination uptake among adults in post-war Tigray, Northern Ethiopia

Abstract Coronavirus disease 2019 (COVID-19) has had a profound impact on developing countries and continues to pose a serious global threat. Vaccination is essential for protecting public health, yet vaccine hesitancy remains a significant barrier to uptake. Evidence on the deterrents to COVID-19 vaccination in post-war contexts, particularly in Ethiopia, is limited. This study explored the factors hindering COVID-19 vaccine uptake in post-war Tigray, Northern Ethiopia. An exploratory qualitative study was conducted in Tigray from August 1 to 30, 2023. Six focus group discussions (FGDs) and seven in-depth interviews (IDIs) were held in host communities and internally displaced persons (IDP) centers, along with seven key informant interviews (KIIs) with public health experts. Participants were purposively selected, and data were collected using semi-structured guides refined through immediate debriefing. All sessions were audio-recorded, transcribed verbatim, and analyzed inductively using Atlas.ti (version 9.0). Five themes emerged as deterrents to COVID-19 vaccination uptake in post-war Tigray: perceived risk of COVID-19, reliance on cultural practices, post-war-related hindrances, infodemic-related barriers, and limited vaccine accessibility. The most frequently reported deterrents were low perceived risk, post-war challenges, fear of side effects, and accessibility issues, whereas reliance on cultural practices was mentioned less often. Low COVID-19 vaccine uptake in post-war Tigray is driven by individual perceptions, sociocultural beliefs, logistical barriers, and post-war challenges. Strengthening uptake requires engaging local religious leaders, implementing gender-sensitive campaigns addressing fertility concerns, and integrating vaccination with humanitarian food aid or health outreach programs.

Predicting myopia risk using a machine learning model based on fundus imageomics

Quantum dynamics and squeezing in a noiseless cavity mode driven by a coherent three-level atom

Evaluation of myocardial fibrosis and cardiac function in fibrotic lung diseases: a prospective study

Relationship between anthropometric indices and macular optical coherence tomography angiography in healthy adults from the PERSIAN cohort study

Enhanced small object detection in UAV aerial imagery through attention gated backbone and context aware fusion

Features of formation and neutralization of environmentally hazardous CrO₃ in chromium pyrometallurgy

A U-shaped association between blood selenium levels and prostate cancer: findings of a case-control study among Nigerian men

Exploring end-to-end earthquake early warning performance in large earthquakes using the February 2023 Kahramanmaraş, Türkiye sequence

Abstract Earthquake early warning systems (EEWS) aim to warn end-users of impending ground shaking. They can be most impactful in large earthquakes occurring close to large population centers, where exposure to strong ground shaking is extensive. However, such earthquakes are rare, and EEWS performance expectations remain uncertain. The February 2023 Kahramanmaraş, Türkiye sequence, including the M7.8 Pazarcık and M7.5 Elbistan events, exposed millions to strong ground shaking and produced a rich waveform dataset, offering a test case. We use this data to produce a realistic simulation of warning times. We use the EPIC point source algorithm for real-time earthquake characterization, and incorporate alert delivery latency using a statistical model driven by real-world alert delivery data from California, collected by the MyShake smartphone app. We show EPIC would produce solutions very quickly (4 s for Pazarcık, 10 s for Elbistan). Despite EPIC’s expected magnitude underestimation (peak M6.7 for Pazarcık, M7.2 for Elbistan), we show that its magnitude estimate grows large quickly enough to provide areas of MMI 6+ shaking with up to 20 s of warning time, even with alert delivery latencies included, provided that low alerting thresholds of MMI 3 or 4 are used.

Dipole instability after an ultrashort XUV pulse in N2: population inversion and timescales

Abstract We study the response of N 2 to a range of XUV laser pulses using real-time time-dependent density functional theory on real-space grids in order to better understand and quantify the internal processes that lead to dipole instabilities. These instabilities, documented in a series of recent papers, develop following the generation of a population inversion in the molecule, in this case induced by the laser pulse. In the current paper, a series of laser pulses having durations of one femtosecond are considered and we explore how the growth rate, total ionization and amount of population inversion changes. This reveals a new aspect to the instability in which the growth rate starts decreasing with increasing pulse intensity. In addition, by using a range of different pseudopotential descriptions of the electron-ion interactions, we find that the observed behaviour is qualitatively independent of the pseudopotential used.

A federated transformer-enhanced double Q-network for collaborative intrusion detection

Physiological responses of pak choi (Brassica Rapa subsp. Chinensis (L.) Hanelt) to cerium and yttrium in two acidic soils with contrasting textures

BlueEdge neural network approach and its application to automated data type classification in mobile edge computing

Abstract Owing to the increasing number of IoT gadgets and the growth of big data, we are now facing massive amounts of diverse data that require proper preprocessing before they can be analyzed. Conventional methods involve sending data directly to the cloud, where it is cleaned and sorted, resulting in a more crowded network, increased latency, and a potential threat to users’ privacy. This paper presents an enhanced version of the BlueEdge framework—a neural network solution designed for the automated classification of data types on edge devices. We achieve this by utilizing a feed-forward neural network and optimized features to identify the presence of 14 distinct data types. Because of this, input data can be preprocessed near its source, and not in the cloud. We utilized a comprehensive dataset comprising 1400 samples, encompassing various data formats from around the world. Compared with rule-based methods, experimental assessment achieves better performance, and results in reduced data transmission (reduced by 62%) and processing latency (78 times faster than cloud-based systems), with resource efficiency comparable to low-end mobile devices. Additionally, our strategy demonstrates strong performance under various data conditions, achieving accuracy levels of over 85% on datasets that may include variations and a noise level as high as 20%. The approach used here is capable of processing data for IoT devices used in education, which can lead to more efficient connections with the cloud and better privacy preservation.