Browse Articles

Defect-induced electric field effects direct Fenton-like oxidation pathways towards polymerization for sustainable water treatment

Stochastically evolving graphs via edit semigroups

We investigate a randomly evolving process of subgraphs in an underlying host graph using the spectral theory of semigroups related to the Tsetlin library and hyperplane arrangements. Starting with some initial subgraph, at each iteration, we apply a randomly selected edit to the current subgraph. Such edits vary in nature from simple edits consisting of adding or deleting an edge, or compound edits which can affect several edges at once. This evolving process generates a random walk on the set of all possible subgraphs of the host graph. We show that the eigenvalues of this random walk can be naturally indexed by subsets of edges of the host graph. We also provide, in the case of simple edits, a closed-form formula for the eigenvectors of the transition probability matrix and a sharp bound for the rate of convergence of this random walk. We consider extensions to the case of compound edits; examples of this model include the previously studied Moran forest model and a dynamic random intersection graph model. Evolving graphs arise in a variety of fields ranging from deep learning and graph neural networks to epidemic modeling and social networks. Our random evolving process serves as a general stochastic model for sampling random subgraphs from a given graph.

Green-synthesized superparamagnetic and biocompatible Fe3O4 nanoparticles for memristive and synaptic bioelectronics

Environmental risk of unintentional injuries at home for children aged 0–6 years in the urban area of Mianyang, China: A cross-sectional investigation

To assess the environmental risk levels and factors associated with unintentional injuries in children’s (0–6 years) homes in Mianyang, China, this cross-sectional study used stratified random sampling and surveyed parents through an online questionnaire from April to June 2024. The survey comprised two parts: demographic information and an environmental scale that assessed unintentional injury risks at home. Family unintentional injury environmental scale scores are presented as median (interquartile range) and were analyzed using the Mann–Whitney rank-sum and Kruskal–Wallis H tests. Influencing factors were examined with a multiple linear regression model. Four hundred and five parents participated in the survey. The overall score for the environmental assessment was 92 (71–98.5) points, with 248 families (61.2%) classified as high risk. Significant differences were observed across demographics: families with children aged 1–3 years had the highest scores (93 [70.5, 99] points; H = 6.061, P  < 0.05) compared with families with children <1 and 4–6 years. Families with grandparents as primary caregivers scored higher (94.5 [88.0, 100.0] points; H = 15.194, P  < 0.001) than non-grandparent caregivers; caregivers with primary school education or less had the highest scores (95.0 [91.0, 99.0] points; H = 39.978, P  < 0.001). Children with a history of unintentional injuries also had higher scores (94.0 [85.0, 99.0] points; Z = −3.219, P  < 0.001) than those without. The χ 2 values for risk level comparisons were 20.039, 24.206, 63.092, and 10.424, respectively (all P  < 0.05). Multiple regression identified residence area, child age, caregiver education, training on injury prevention, and history of injuries as independent factors. Overall, home environments in the urban area of Mianyang predominantly pose a high risk of unintentional injury, with scores varying by demographic characteristics. Enhancing safety education for targeted groups and addressing potential hazards are crucial for reducing unintentional injuries among children.

Modeling Long-QT Syndrome Using Gene-Edited Miniature Pigs

China’s intertidal mariculture as an unexpected lifeline sustaining the world’s most threatened shorebird flyway

A machine learning model for predicting 28-day mortality in ICU patients with community-acquired pneumonia and acute kidney injury

Automated cementing quality detection using a domain-specific, multi-scale convolutional neural network

Cementing quality is a key factor in ensuring the long-term safe production of oil and gas wells and preventing defects. Traditional cementing quality evaluation mainly relies on logging interpreters manually analyzing acoustic logging data, such as Variable Density Logging (VDL) images and acoustic amplitude curves. This process is highly dependent on personal experience, labor-intensive, and inefficient. To address these issues, this paper proposes an automated cementing quality detection method, CemQ-CNN, based on a Convolutional Neural Network (CNN). In this context, “intelligent” refers to the model’s ability to perform automatic classification from raw data, thereby increasing efficiency and consistency. This method constructs a multimodal input CNN model that can simultaneously process VDL images and acoustic logging curve data, achieving automatic, fast, and accurate classification of cementing quality. We collected and labeled 5,000 logging samples from 150 different wells across three distinct geological blocks, ensuring dataset diversity, categorizing them into three cementing quality levels: “good,” “medium,” and “poor.” By allocating 70% of the data for training, 15% for validation, and 15% for testing, our model demonstrated Good performance on the test set. Experimental results show that the proposed method achieves an overall classification accuracy of 95.7%, demonstrating robust performance across all three quality classes (‘Good’, ‘Medium’, and ‘Poor’), with a macro-average recall rate of 95.6% and a precision rate of 95.5%. Compared to models using a single data source, this multimodal model performs better. The study demonstrates that an effective intelligent method based on CNN can assist and standardize traditional manual interpretation, providing a reliable and innovative paradigm for cementing quality evaluation.

Response by Zheng and Chen to Letter Regarding Article, “METTL4-Mediated Mitochondrial DNA N6-Methyldeoxyadenosine Promoting Macrophage Inflammation and Atherosclerosis”

Dual-photon–driven hydrogen evolution in copper-based photocatalysts under near-infrared and visible light

Correlation analysis between RAS gene mutations and pathological morphological features in colorectal cancer

Tongue-tie diagnosis using the Lingual frenulum in newborn infants (LINNE) -scoring: A validation study

Background Ankyloglossia may restrict newborn infant’s tongue movements, complicating breastfeeding. However, due to lacking evidence-based guidelines, patient selection for early frenotomy has remained a challenge. The present prospective, observation study validated a scoring aimed to detect infants who required early tongue-tie treatment. It is a part of the Lingual Frenulum in Newborn Infants (LINNE) project, investigating tongue-tie epidemiology along with a randomized treatment trial. Methods Healthy mother-infant-dyads were assessed by independent and mutually blinded study physicians and midwives. They used the LINNE scoring, including: 1. Infant examination using a picture assessment tool for tongue-tie in breastfed babies (TABBY); 2. Maternal breastfeeding experience scoring (MBES), a psychometric testing on acute breastfeeding symptoms; 3. Three anamnestic factors associated with tongue-ties and breastfeeding problems. Validation tests were conducted using the Cronbach alpha for the whole LINNE scoring, and for the subscorings: 1. TABBY: areas under the curve (AUC), multi-rater Fleiss kappa; 2. MBES: content and construct validity and responsiveness tests; 3. Anamnesis: accuracy studies for diagnostic tests. Results Of the studied 556 mother-infant-dyads, 72 (12.9%) infants fulfilled the early tongue-tie treatment criteria. The overall LINNE scoring’s internal consistency was acceptable (alpha = 0.723). The pooled TABBY scoring (n = 1094) accurately detected the need for treatment (AUC = 0.914, 95%CI 0.891–0.938) with moderate inter-rater agreement (kappa = 0.441, 95%CI 0.406–0.477). MBES responsiveness, content, and construct validities reached the required levels. Anamnestic factors had high specificity but fair sensibility for the tongue-tie causing breastfeeding symptoms. Conclusion The LINNE scoring demonstrated moderate inter-rater agreement; however, the accuracy of the TABBY tool was very good with AUC 0.9 for ankyloglossia needing early treatment.

Letter by Mo Regarding Article, “METTL4-Mediated Mitochondrial DNA N6-Methyldeoxyadenosine Promoting Macrophage Inflammation and Atherosclerosis”

Lessons from niche-specific fitness of Staphylococcus aureus at the wound edge

Postpartum anemia and associated factors among caesarean section women in West Gojjam zone Ethiopia 2023

Modeling the integration of ethics, social responsibility, and sustainability in university contexts

The integration of ethics, social responsibility, and sustainability (ERS) into higher education curricula is essential for fostering competencies aligned with the Sustainable Development Goals. This study aimed to develop and validate a structural model to evaluate ERS competencies in higher education. Data were collected from a sample of 418 engineering students at a public university in Colombia, using a Likert-type instrument comprising items distributed across three dimensions. Exploratory factor analysis identified a well-defined three-factor structure, while confirmatory factor analysis confirmed the model’s validity. The structural equation model demonstrated a good fit (CFI = 0.946, TLI = 0.940, RMSEA = 0.047, SRMR = 0.042), and reliability analysis indicated excellent internal consistency (α = 0.914; ω = 0.920 for the higher-order ERS construct). Results showed that ERS significantly explained the first-order dimensions of Ethics (0.620), Sustainability (0.884), and Social Responsibility (0.847). Moreover, ERS predicted academic performance in transversal courses, with standardized coefficients of 0.698 in Science, Technology, and Society (STS) and 0.574 in Environmental Management (p < .001). These findings provide strong empirical support for the hierarchical structure of ERS competencies, confirm their positive association with academic achievement, and highlight the model’s value as a tool to guide curriculum development and policy in higher education.

Designing bi-layer electrode-electrolyte interfaces with an asymmetric ether to enable wide-temperature lithium metal batteries

Comprehensive analysis of structure–property–shielding relationships in Pr2O3-modified tellurite glass systems

Abstract This work investigated the structural, thermal, mechanical, and shielding characteristics of a tellurite-based glass series with the composition 60TeO 2 –12.5Nb 2 O 5 –12.5ZnO–(15– x )LiF– x Pr 2 O 3 , where x ranged from 0.5 to 5.0 mol%. The novelty of this study lay in a comprehensive analysis that combined multiple techniques. The research involved using the radial distribution function (RDF) with Gaussian fitting to determine structural parameters and the fraction of trigonal bipyramidal (TeO 4 ), N 4 (X-ray) representation for each glass sample. The study also utilized deconvoluted FTIR spectra to further characterize the glass structure. Results showed the transformation of trigonal pyramidal (TeO 3 ) tp units to trigonal bipyramidal (TeO 4 ) tbp units and consequently enhanced the rigidity of the glass structure with addition of Pr 2 O 3 . Additionally, it established a logarithmic correlation across the entire glass series, linking the molar volume (V m ) with the experimental bulk modulus (K exp ​), $$\:{K}_{exp}={V}_{m}^{-\alpha\:}$$ , and explored the correlations between these properties and the power (α). The ring deformation model was applied to calculate the average atomic ring diameter. Increased Pr 2 ​O 3 ​ content enhanced the glass’s thermal stability. This was evidenced by a significant rise in its glass transition temperature and onset crystallization temperature, indicating a more rigid and stable structure. In addition, various shielding parameters were determined. The results of this investigation highlight the excellent gamma photon shielding capabilities of these glasses.

Development of a mechatronic weft selector to enhance patterning capacity in Rapier looms

Rapier weaving machines are widely used in the weaving industry; however, the design of the current weft selectors limits their capability to produce fabrics incorporating a large variety of weft yarns. Nevertheless, increasing the number of wefts with the existing selectors can impose challenges for the rapier in the efficient gripping of the weft. This study presents the design and development of a novel mechatronic weft selection system aimed at significantly enhancing the patterning capabilities of rapier looms. The proposed system features a circular arrangement of up to 20 weft yarn feeders and a single programmable selector module capable of handling the weft yarns. The selector integrates stepper motors, servo motors, solenoid valves, and an Arduino Mega-based control unit to execute user-defined weft patterns with high precision. The device was successfully operated at 9 picks per minute (PPM) across various yarn types, including polyester, viscose, and elastomeric yarns. A total of 11 different yarns were tested, each subjected to 5 trials using 20 feeders, resulting in the insertion of 1100 wefts. The system achieved a 100% selection and insertion success rate after the trials. The system also maintains a constant yarn-to-rapier angle, mitigating pick errors. The developed system offers a solution for expanding weft selection in rapier looms, enabling more intricate fabric designs and increased product versatility. To the best of our knowledge, this type of design has not been tried before in weft selection.

Coccolith clumped isotopes reveal modest rather than extreme northern high latitude amplification during the Miocene

Abstract Accurate predictions of the future climate response to CO 2 depend on the ability of climate models to simulate past analog warmer climates, like the Miocene. However, one key unresolved issue in paleoclimate modeling is reproducing the pronounced high-latitude warmth and relatively flat latitudinal temperature gradients inferred from proxy records. Here, we use clumped isotope thermometry—a method that sidesteps limitations of conventional proxies—on pure coccolith calcite from a high-latitude North Atlantic site, extending from the Mid Miocene to the Quaternary. Coccolith-derived clumped isotope temperatures are on average ~9°C lower than alkenone estimates, representing the first proxy dataset to align with Miocene model outputs and calling into question the prevailing paradigm of pronounced high latitude amplification. This record highlights the need to continuously reevaluate proxy interpretations to achieve both reliable trends and absolute temperature values, while providing a more optimistic perspective of future high latitude climate response to CO 2 emissions.