bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transparent and high-porosity aluminum alkoxide network-forming glasses
Authors: Zhang Zihui , Zhao Yingbo 
Published: August 2024

AbstractMetal-organic network-forming glasses are an emerging type of material capable of combining the modular design and high porosity of metal-organic frameworks and the high processability and optical transparency of glasses. However, a generalizable strategy for achieving both high porosity and high glass-forming ability in modularly designed metal-organic networks has yet to be developed. Herein, we develop a series of aluminum alkoxide glasses and monoliths by linking aluminum-oxo clusters with alcohol linkers. A bulky monodentate alcohol modulator is introduced during synthesis and act as both network plasticizer and pore template, which can be removed by the subsequent solvent exchange to give gas accessible pores. Glasses synthesized with the modulator template exhibit well-defined glass transitions in their as-synthesized form and high surface areas up to 500 m2/g after activation, making them among the most porous glassy materials. The aluminum alkoxide glasses also have optical transparency and fluorescent properties, and their structures are elucidated by pair-distribution functions, spectroscopic and compositional analysis. These findings could significantly expand the library of microporous metal-organic network-forming glasses and enable their future applications.
A novel stacking ensemble learner for predicting residual strength of corroded pipelines
Authors: Wang Qiankun , Lu Hongfang 
Published: August 2024

AbstractAccurately assessing the residual strength of corroded oil and gas pipelines is crucial for ensuring their safe and stable operation. Machine learning techniques have shown promise in addressing this challenge due to their ability to handle complex, non-linear relationships in data. Unlike previous studies that primarily focused on enhancing prediction accuracy through the optimization of single models, this work shifts the emphasis to a different approach: stacking ensemble learning. This study applies a stacking model composed of seven base learners and three meta-learners to predict the residual strength of pipelines using a dataset of 453 instances. Automated hyperparameter tuning libraries are utilized to search for optimal hyperparameters. By evaluating various combinations of base learners and meta-learners, the optimal stacking configuration was determined. The results demonstrate that the stacking model, using k-nearest neighbors as the meta-learner alongside seven base learners, delivers the best predictive performance, with a coefficient of determination of 0.959. Compared to individual models, the stacking model also significantly improves generalization performance. However, the stacking model’s effectiveness on low-strength pipelines is limited due to the small sample size. Furthermore, incorporating original features into the second-layer model did not significantly enhance performance, likely because the first-layer model had already extracted most of the critical features. Given the marginal contribution of model optimization to prediction accuracy, this work offers a novel perspective for improving model performance. The findings have important practical implications for the integrity assessment of corroded pipelines.
The genetic architecture of repeated local adaptation to climate in distantly related plants
Authors: Whiting James R. , Booker Tom R. , Rougeux Clément , Lind Brandon M. , Singh Pooja , Lu Mengmeng , Huang Kaichi , Whitlock Michael C. , Aitken Sally N. , Andrew Rose L. , Borevitz Justin O. , Bruhl Jeremy J. , Collins Timothy L. , Fischer Martin C. , Hodgins Kathryn A. , Holliday Jason A. , Ingvarsson Pär K. , Janes Jasmine K. , Khandaker Momena , Koenig Daniel , Kreiner Julia M. , Kremer Antoine , Lascoux Martin , Leroy Thibault , Milesi Pascal , Murray Kevin D. , Pyhäjärvi Tanja , Rellstab Christian , Rieseberg Loren H. , Roux Fabrice , Stinchcombe John R. , Telford Ian R. H. , Todesco Marco , Tyrmi Jaakko S. , Wang Baosheng , Weigel Detlef , Willi Yvonne , Wright Stephen I. , Zhou Lecong , Yeaman Sam 
Published: August 2024

AbstractClosely related species often use the same genes to adapt to similar environments. However, we know little about why such genes possess increased adaptive potential and whether this is conserved across deeper evolutionary lineages. Adaptation to climate presents a natural laboratory to test these ideas, as even distantly related species must contend with similar stresses. Here, we re-analyse genomic data from thousands of individuals from 25 plant species as diverged as lodgepole pine and Arabidopsis (~300 Myr). We test for genetic repeatability based on within-species associations between allele frequencies in genes and variation in 21 climate variables. Our results demonstrate significant statistical evidence for genetic repeatability across deep time that is not expected under randomness, identifying a suite of 108 gene families (orthogroups) and gene functions that repeatedly drive local adaptation to climate. This set includes many orthogroups with well-known functions in abiotic stress response. Using gene co-expression networks to quantify pleiotropy, we find that orthogroups with stronger evidence for repeatability exhibit greater network centrality and broader expression across tissues (higher pleiotropy), contrary to the ‘cost of complexity’ theory. These gene families may be important in helping wild and crop species cope with future climate change, representing important candidates for future study.
A 27-country test of communicating the scientific consensus on climate change
Authors: Većkalov Bojana , Geiger Sandra J. , Bartoš František , White Mathew P. , Rutjens Bastiaan T. , van Harreveld Frenk , Stablum Federica , Akın Berkan , Aldoh Alaa , Bai Jinhao , Berglund Frida , Bratina Zimic Aleša , Broyles Margaret , Catania Andrea , Chen Airu , Chorzępa Magdalena , Farahat Eman , Götz Jakob , Hoter-Ishay Bat , Jordan Gesine , Joustra Siri , Klingebiel Jonas , Krajnc Živa , Krug Antonia , Andersen Thomas Lind , Löloff Johanna , Natarajan Divya , Newman-Oktan Sasha , Niehoff Elena , Paerels Celeste , Papirmeister Rachel , Peregrina Steven , Pohl Felicia , Remsö Amanda , Roh Abigail , Rusyidi Binahayati , Schmidt Justus , Shavgulidze Mariam , Vellinho Nardin Valentina , Wang Ruixiang , Warner Kelly , Wattier Miranda , Wong Chloe Y. , Younssi Mariem , Ruggeri Kai , van der Linden Sander 
Published: August 2024

AbstractCommunicating the scientific consensus that human-caused climate change is real increases climate change beliefs, worry and support for public action in the United States. In this preregistered experiment, we tested two scientific consensus messages, a classic message on the reality of human-caused climate change and an updated message additionally emphasizing scientific agreement that climate change is a crisis. Across online convenience samples from 27 countries (n = 10,527), the classic message substantially reduces misperceptions (d = 0.47, 95% CI (0.41, 0.52)) and slightly increases climate change beliefs (from d = 0.06, 95% CI (0.01, 0.11) to d = 0.10, 95% CI (0.04, 0.15)) and worry (d = 0.05, 95% CI (−0.01, 0.10)) but not support for public action directly. The updated message is equally effective but provides no added value. Both messages are more effective for audiences with lower message familiarity and higher misperceptions, including those with lower trust in climate scientists and right-leaning ideologies. Overall, scientific consensus messaging is an effective, non-polarizing tool for changing misperceptions, beliefs and worry across different audiences.
Continuous spectral and coupling-strength encoding with dual-gradient metasurfaces
Authors: Aigner Andreas , Weber Thomas , Wester Alwin , Maier Stefan A. , Tittl Andreas 
Published: August 2024

AbstractTo control and enhance light–matter interactions at the nanoscale, two parameters are central: the spectral overlap between an optical cavity mode and the material’s spectral features (for example, excitonic or molecular absorption lines), and the quality factor of the cavity. Controlling both parameters simultaneously would enable the investigation of systems with complex spectral features, such as multicomponent molecular mixtures or heterogeneous solid-state materials. So far, it has been possible only to sample a limited set of data points within this two-dimensional parameter space. Here we introduce a nanophotonic approach that can simultaneously and continuously encode the spectral and quality-factor parameter space within a compact spatial area. We use a dual-gradient metasurface design composed of a two-dimensional array of smoothly varying subwavelength nanoresonators, each supporting a unique mode based on symmetry-protected bound states in the continuum. This results in 27,500 distinct modes and a mode density approaching the theoretical upper limit for metasurfaces. By applying our platform to surface-enhanced molecular spectroscopy, we find that the optimal quality factor for maximum sensitivity depends on the amount of analyte, enabling effective molecular detection regardless of analyte concentration within a single dual-gradient metasurface. Our design provides a method to analyse the complete spectral and coupling-strength parameter space of complex material systems for applications such as photocatalysis, chemical sensing and entangled photon generation.
Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics
Authors: Almet Axel A. , Tsai Yuan-Chen , Watanabe Momoko , Nie Qing 
Published: August 2024

AbstractFrom single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), one can extract high-dimensional gene expression patterns that can be described by intercellular communication networks or decoupled gene modules. These two descriptions of information flow are often assumed to occur independently. However, intercellular communication drives directed flows of information that are mediated by intracellular gene modules, in turn triggering outflows of other signals. Methodologies to describe such intercellular flows are lacking. We present FlowSig, a method that infers communication-driven intercellular flows from scRNA-seq or ST data using graphical causal modeling and conditional independence. We benchmark FlowSig using newly generated experimental cortical organoid data and synthetic data generated from mathematical modeling. We demonstrate FlowSig’s utility by applying it to various studies, showing that FlowSig can capture stimulation-induced changes to paracrine signaling in pancreatic islets, demonstrate shifts in intercellular flows due to increasing COVID-19 severity and reconstruct morphogen-driven activator–inhibitor patterns in mouse embryogenesis.
A multi-disciplinary approach for uranium exploration using remote sensing and airborne gamma-ray spectrometry data in the Gebel Duwi area, Central Eastern Desert, Egypt
Authors: Hegab Mahmoud Abd El-Rahman 
Published: August 2024

AbstractUranium exploration plays a pivotal role in meeting global energy demands and advancing nuclear technology. This study presents a comprehensive approach to uranium exploration in the Gebel Duwi area of the Central Eastern Desert of Egypt, utilizing remote sensing and airborne gamma-ray spectrometric data. Multispectral remote sensing techniques, including Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Band Ratioing (BR), are employed to identify lithological units and hydrothermal alteration zones associated with uranium deposition, such as iron oxides, argillic, propylitic, and phyllic alterations. Additionally, airborne gamma-ray spectrometry data provide insights into the spatial distribution of radioelements, including uranium (eU), thorium (eTh), and potassium (K), as well as radioelement ratios (eU/eTh, eU/K, and eTh/K). The uranium migration index map (eU-(eTh/3.5)) and the F-parameter map (K*(eU/eTh)) have been generated to investigate the movement of uranium within various geological zones and characterize anomalous uranium concentrations. Statistical analyses, including mean (X), standard deviation (S), and coefficient of variability (C.V.), are conducted to identify uranium-rich zones. The integration of these datasets enables the generation of a uranium potential map highlighting areas of elevated concentrations indicative of uranium mineralization. Field observations and mineralogical analyses of collected samples validate our findings, confirming the presence of minerals associated with uranium mineralization in mapped high-potential areas. The significance of minerals like Fe-Chlorite, Fe-Mg-Chlorite, ferrihydrite, goethite, calcite, muscovite, dolomite, actinolite, vermiculite, and gypsum in indicating potential uranium mineralization processes underscores the importance of our results.
Distribution and associated factors of keratometry and corneal astigmatism in an elderly population
Authors: Hashemi Hassan , Aghamirsalim Mohamadreza , Hashemi Alireza , Khabazkhoob Mehdi 
Published: August 2024

AbstractTo determine the distribution of keratometry and corneal astigmatism (CA) and their association with demographic factors, systemic parameters, anthropometric measures, ocular biometric indices, and refractive errors in people aged 60 years and above. In this cross-sectional study, 160 clusters were randomly selected from Tehran city (Iran) using the multi-stage cluster sampling method. All participants underwent optometric examinations including testing uncorrected and best-corrected distance visual acuity, non-cycloplegic autorefraction, and subjective refraction. Pentacam imaging for all participants was carried out using Pentacam AXL. Keratometry and CA were reported based on Pentacam’s data. The average, standard deviation (SD) and 95% confidence interval (CI) of flat keratometry (Kf), steep keratometry (Ks), mean keratometry (mean K), and CA were 44.02 ± 1.58 D (95% CI 43.94–44.1), 44.86 ± 1.67 D (95% CI 44.78–44.94), 44.44 ± 1.58 D (95% CI 44.36–44.52), and 0.84 ± 0.74 D (95% CI 0.81–0.87), respectively. The 95% and 99% percentiles of mean K were 47.1 and 48.6 D, respectively. According to the multiple generalized estimating equation model, the mean K was significantly higher in males, in myopes, and in those with higher systolic blood pressure. Moreover, the mean K was inversely related to the axial length, height, anterior chamber depth (ACD), corneal diameter, and central corneal thickness (CCT). The prevalence of various types of CA based on a cut-off > 0.50 D was as follows; with-the-rule: 32.5% (95% CI 30.6–34.4), against-the-rule: 18.2% (95% CI 16.7–19.7), and oblique: 10.0% (95% CI 9.1–11.0). The present study investigated the normal distribution of keratometry and CA in individuals ≥ 60 years, and results can be used in clinical matters, especially in intraocular lens power calculation. Sex, systolic blood pressure, height, and some biometric components such as ACD, corneal diameter, and CCT were significantly related to keratometry and should be considered.
Single-cell analysis of chromatin and expression reveals age- and sex-associated alterations in the human heart
Authors: Read David F. , Booth Gregory T. , Daza Riza M. , Jackson Dana L. , Gladden Rula Green , Srivatsan Sanjay R. , Ewing Brent , Franks Jennifer M. , Spurrell Cailyn H. , Gomes Anne Roshella , O’Day Diana , Gogate Aishwarya A. , Martin Beth K. , Larson Haleigh , Pfleger Christian , Starita Lea , Lin Yiing , Shendure Jay , Lin Shin , Trapnell Cole 
Published: August 2024

AbstractSex differences and age-related changes in the human heart at the tissue, cell, and molecular level have been well-documented and many may be relevant for cardiovascular disease. However, how molecular programs within individual cell types vary across individuals by age and sex remains poorly characterized. To better understand this variation, we performed single-nucleus combinatorial indexing (sci) ATAC- and RNA-Seq in human heart samples from nine donors. We identify hundreds of differentially expressed genes by age and sex and find epigenetic signatures of variation in ATAC-Seq data in this discovery cohort. We then scale up our single-cell RNA-Seq analysis by combining our data with five recently published single nucleus RNA-Seq datasets of healthy adult hearts. We find variation such as metabolic alterations by sex and immune changes by age in differential expression tests, as well as alterations in abundance of cardiomyocytes by sex and neurons with age. In addition, we compare our adult-derived ATAC-Seq profiles to analogous fetal cell types to identify putative developmental-stage-specific regulatory factors. Finally, we train predictive models of cell-type-specific RNA expression levels utilizing ATAC-Seq profiles to link distal regulatory sequences to promoters, quantifying the predictive value of a simple TF-to-expression regulatory grammar and identifying cell-type-specific TFs. Our analysis represents the largest single-cell analysis of cardiac variation by age and sex to date and provides a resource for further study of healthy cardiac variation and transcriptional regulation at single-cell resolution.
A translationally informed approach to vital signs for psychiatry: a preliminary proof of concept
Authors: Wallace Meredith L. , Frank Ellen , McClung Colleen A. , Cote Sarah E. , Kendrick Jeremy , Payne Skylar , Frost-Pineda Kimberly , Leach Jeremy , Matthews Mark J. , Choudhury Tanzeem , Kupfer David J. 
Published: August 2024

AbstractThe nature of data obtainable from the commercial smartphone – bolstered by a translational model emphasizing the impact of social and physical zeitgebers on circadian rhythms and mood – offers the possibility of scalable and objective vital signs for major depression. Our objective was to explore associations between passively sensed behavioral smartphone data and repeatedly measured depressive symptoms to suggest which features could eventually lead towards vital signs for depression. We collected continuous behavioral data and bi-weekly depressive symptoms (PHQ-8) from 131 psychiatric outpatients with a lifetime DSM-5 diagnosis of depression and/or anxiety over a 16-week period. Using linear mixed-effects models, we related depressive symptoms to concurrent passively sensed behavioral summary features (mean and variability of sleep, activity, and social engagement metrics), considering both between- and within-person associations. Individuals with more variable wake-up times across the study reported higher depressive symptoms relative to individuals with less variable wake-up times (B [95% CI] = 1.53 [0.13, 2.93]). On a given week, having a lower step count (−0.16 [−0.32, −0.01]), slower walking rate (−1.46 [−2.60, −0.32]), lower normalized location entropy (−3.01 [−5.51, −0.52]), more time at home (0.05 [0.00, 0.10]), and lower distances traveled (−0.97 [−1.72, −0.22]), relative to one’s own typical levels, were each associated with higher depressive symptoms. With replication in larger samples and a clear understanding of how these components are best combined, a behavioral composite measure of depression could potentially offer the kinds of vital signs for psychiatric medicine that have proven invaluable to assessment and decision-making in physical medicine. Clinical Trials Registration: The data that form the basis of this report were collected as part of clinical trial number NCT03152864.
Accurate design, simulation and implementation of AC/DC inductors for power electronic converters
Authors: Martínez‐Salgado Elean Jim , Mejia‐Ruiz Gabriel E. , Ramos‐Guerrero José Manuel , Paternina Mario R. Arrieta , de la Cruz Javier , Chamorro Harold R. 
Published: August 2024

AbstractThis article proposes a holistic and improved methodology for the inductors' design in power electronic circuits operating at high frequencies. This methodology includes an analytical design, a finite element modeling of the electromagnetic behavior with JMAG software, a simulation analysis in a Simulink environment, and experimental verification of the effectiveness of inductor design in a laboratory‐scale step‐up boost power converter prototype. The holistic approach allowed us to achieve all target specifications such as maximum power (300 W), minimum voltage (60 V), inductance (1.77 mH), and maximum current ripple (1 A). It guarantees the losses' minimization in core and copper (21.47 W), and marginal errors below 4% between the analytical, simulated, and experimental values.
Resilient robust model predictive load frequency control for smart grids with air conditioning loads
Authors: Jike Shiluo , Liu Guobao , Li Feng , Zhang Changyu , Wang Qi , Zhou Mengxia , Qian Haiya 
Published: August 2024

AbstractThis paper investigates the robust model predictive load frequency control problem for smart grids with wind power under cyber attacks. To accommodate intermittent power generation, the demand response of the system is considered by involving the air conditioning loads in the frequency regulation. In addition, the system uncertainties produced by the air conditioning load users and wind turbines when replacing traditional generator sets are considered. By using the cone complementary linearization algorithm and the linear matrix inequality technique, a resilience robust model predictive control strategy with mixed performance indexes is proposed. Furthermore, a rigorous derivation of the recursive feasibility of robust model predictive control is given. Finally, the simulation results of the two‐area load frequency control scheme show that the proposed model predictive control strategy is capable of realizing the load frequency control of the multi‐area smart grid and is robust to the parameter uncertainties and frequency regulation of the system. The results also show that the proposed model predictive control strategy has some resistance to cyber attacks and external disturbances.
A robust must‐run capacity identifying approach in power system dispatch considering interdependence with natural gas system
Authors: Wu Wenqi , Xiao Yunpeng , Hu Tao , Wang Xiuli 
Published: August 2024

AbstractIdentifying the must‐run capacity (MRC) could help to accelerate the unit commitment and assess market power, thus critical for achieving an efficient power system dispatch. However, the existing approaches identify the MRC without considering the ever‐strengthening interdependence between power system and natural gas (NG) system, as well as neglecting either the uncertainties of renewables or the constraints on start‐up/shut‐down states for thermal units. To fill the gaps, this article proposes a novel model and methodology for identifying the MRC in power system considering the interdependence with NG system, with adopting a distributionally robust (DR) approach to consider the uncertainties of renewables. A constraint‐generation based algorithm is devised to solve the model, with a relax‐round‐polish based Alternating Direction Method of Multipliers (ADMM) algorithm to address the interaction between the two energy systems. Comparative studies show the effectiveness in MRC assessment over the existing approaches.