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Observing formation and evolution of dislocation cells during plastic deformation
Abstract During plastic deformation of metals and alloys, dislocations self-organise in cells, which subsequently continuously decrease in size. How and when these processes take place has remained elusive, because observations of the structural dynamics in the bulk have not been feasible. We here present X-ray diffraction microscopy sequences of the structural evolution during tensile deformation of a mm-sized aluminium (111) single crystal. The formation and subsequent development of 40,000 cells are visualised. The cells form in a stochastic, isotropic and uncorrelated manner already at 1% strain. We reveal that the cell size and dislocation density distributions are log-normal and bi-modal distributions, respectively, exhibiting scaling and maintaining a fixed volume ratio between cell interior and cell boundary. This insight leads to an interpretation of the formation and evolution steps in terms of universal stochastic multiplicative processes. This work will guide dislocation dynamics modelling, as it provides unique dynamic data and understanding.
An efficient parallelization technique for the coupled problems of fluid, gas and plasma mechanics in the grid environment
A multi model deep net with an explainable AI based framework for diabetic retinopathy segmentation and classification
Transcranial direct current stimulation associated with physical exercise can help smokers to quit smoking: a randomized controlled trial
Author Correction: CTLA4 blockade abrogates KEAP1/STK11-related resistance to PD-(L)1 inhibitors
Unraveling carbonate fault dynamics, from friction to decarbonation, through the 1959 Mw 7.2 earthquake in Montana
Abstract Seismic rupture in carbonate rocks influences fault friction behavior through thermal evolution and mineral reactions. Focusing on the 1959 Mw 7.2 Hebgen Lake event in western Yellowstone, Montana, the largest earthquake on a normal fault in the United States, we analyze fault rock microstructures and mineralogical changes to constrain frictional heating on the fault plane. We combine thermal maturity of organic matter, magnetic fabric, and thermomagnetic methods with scanning electron microscopy to unravel variations in peak frictional temperature along the fault slip surface. The mineral changes caused by coseismic heating (e.g., nanocalcite formation or goethite to hematite reaction) occur in patches along the fault mirror, hence reflecting considerable differences in frictional heat. While coseismic thermal heterogeneities have been reported in other rock types, this is the first time they are documented and quantified specifically in carbonates. Furthermore, these results provide new mineralogical criteria to quantify coseismic frictional heat in natural faults at temperatures lower than that of decarbonation and highlight the need to consider coseismic friction processes at a scale larger than most deformation experiments. For example, we document the critical role played by fault plane attitude (dip) at the scale of a few tens of centimeters in production of frictional heat. Our results emphasize that while coseismic decarbonation dynamically weakens carbonate-hosted faults, it may generally not occur along an entire fault plane.
Global impact of the COVID-19 lockdown on biodiversity data collection
Abstract The COVID-19 pandemic triggered different governmental responses across borders, with cascading effects on people’s movements and on biodiversity data collection. We quantified changes in the number of species occurrence records collected during the first global lockdown (March 15th to May 1st 2020) relative to pre-pandemic levels using data from the Global Biodiversity Information Facility (GBIF). We modelled how such changes relate to the stringency of governmental policy responses, changes in human mobility, and countries’ population size and economic class across 129 countries. We further focused on data from the community science project eBird, which constitutes the largest dataset in GBIF, to investigate changes in participation and activity patterns of individual observers (eBirders) during the lockdown. We found that the decreases in GBIF records correlated with declines in numbers of visitors to parks and outdoor areas, and were significantly larger in developing countries compared to developed ones. While the activity ranges of eBirders shrunk across all countries analysed, the number of eBirders in developing and least developed countries declined more than in developed countries, as the lockdown disrupted the influx of international visitors. Our results suggest that community-based, local monitoring programmes are essential to reduce biases in global biodiversity monitoring.
Non-invasive vestibular nerve stimulation (VeNS) reduces visceral adipose tissue: results of a randomised controlled trial
Abstract Across multiple species, chronic vestibular stimulation activates hypothalamic regions involved in energy homeostasis and reduces body fat. This first-in-human randomised controlled trial evaluated the efficacy and safety of electrical vestibular nerve stimulation (VeNS) as a means of reducing excess body weight and fat. Overweight and obese adults were randomised 1:1 to receive 60 min of daily VeNS (n = 117) or sham stimulation (n = 124) for 6 months, together with a hypocaloric diet. The primary endpoints were weight loss based. Secondary endpoints included reduction in visceral adipose tissue (VAT). It is VAT, more than subcutaneous fat depots, which is particularly associated with the risks associated with obesity. The weight loss based primary endpoints were not met. However, mean change in VAT was significantly greater in the active (− 12.6%) versus the sham (− 4.7%) group (p = 0.03). This suggests that regular VeNS may cause a clinically meaningful reduction in VAT.
A machine learning framework for predicting shear strength properties of rock materials
Abstract The shear strength characteristics of rock materials, specifically internal friction angle and cohesion, are critical parameters for the design of rock structures. Accurate strength prediction can significantly reduce design time and costs while minimizing material waste associated with extensive physical testing. This paper utilizes experimental data from rock samples in the Himalayas to develop a novel machine learning model that combines the improved sparrow search algorithm (ISSA) with Extreme Gradient Boosting (XGBoost), referred to as the ISSA-XGBoost model, for predicting the shear strength characteristics of rock materials. To train and validate the proposed model, a dataset comprising 199 rock measurements and six input variables was employed. The ISSA-XGBoost model was benchmarked against other models, and feature importance analysis was conducted. The results demonstrate that the ISSA-XGBoost model outperforms the alternatives in both training and test datasets, showcasing superior predictive accuracy (R² = 0.982 for cohesion and R² = 0.932 for internal friction angle). Feature importance analysis revealed that uniaxial compressive strength has the greatest influence on cohesion, followed by P-wave velocity, while density exerts the most significant impact on internal friction angle, also followed by P-wave velocity.
Force overestimation during vascular occlusion is triggered by motor system inhibition
A data driven approach to urban area delineation using multi source geospatial data
Abstract This study introduces a data-driven, bottom-up approach to urban delineation, integrating feature engineering with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which represents a significant improvement in precision and methodology compared to traditional approaches that rely on simplistic OpenStreetMap (OSM) road node data aggregations. By employing a broad array of OSM categories and refining data selection through feature engineering, our research significantly enhances the precision and relevance of urban clustering. Using Bavaria, Germany, as a case study, we demonstrate that feature engineering effectively reduces noise and mitigates common DBSCAN clustering pitfalls by filtering out irrelevant and autocorrelated data. The robustness of the proposed method is validated through a comprehensive assessment involving three key elements: (1) a 5% improvement in average accuracy, (2) optimal clustering selections based on entropy values that eliminate the need for prior knowledge, and (3) validation through nighttime light data and Zipf’s law, where a high p-value of 0.99 confirms a good fit, supporting the power law. This study contributes to urban studies by providing a scalable, replicable model that incorporates advanced data processing techniques and multidimensional data sources, supporting improved urban planning and policy-making while effectively delineating urban areas in varied settings.
How blowing the whistle on the Theranos scandal transformed Erika Cheung’s career
Enhanced probabilistic prediction of pavement deterioration using Bayesian neural networks and cuckoo search optimization
Abstract The predictive performance of probabilistic pavement condition deterioration is critical for effective maintenance and rehabilitation decisions. Currently, numerous improved models exist, but few rely on probabilistic models to improve pavement deterioration prediction. Therefore, this study proposed an improved probabilistic model for pavement deterioration prediction based on the coupling of Bayesian neural network (BNN) and cuckoo search (CS) algorithm. The model prediction performance is evaluated against two metrics: determination coefficient (R2) and standard deviation (stability). Finally, based on the data from the pavement management system in Shanxi Province, it was verified that the CS-BNN model outperforms the genetic algorithm-BNN, particle swarm optimization-BNN, and BNN models in terms of the two metrics. Sensitivity analysis further confirms the robustness of the CS-BNN model. The findings indicate that the CS-BNN model provides more reliable predictions with lower uncertainty, aiding road engineers in optimizing maintenance schedules and costs.
A dual branch model for predicting microseismic magnitude time series named DTFNet
Enhanced electrochemical detection of dopamine and uric acid using Au@Ni-MOF and employing 2D structure DFT simulation
Strength and microstructural characteristics of sand soils stabilized with paper sludge Ash-Based geopolymer
Sustainable groundwater management through water quality index and geochemical insights in Valsad India
Author Correction: Partner relationships, hopelessness, and health status strongly predict maternal well-being: an approach using light gradient boosting machine
Flax domestication processes as inferred from genome-wide SNP data
Abstract Flax (Linum usitatissimum L.) is one of the founder crops domesticated for oil and fiber uses in the Near-Eastern Fertile Crescent, but its domestication history remains largely elusive. Genetic inferences so far have expanded our knowledge in several aspects of flax domestication such as the wild progenitor, the first use of domesticated flax, and domestication events. However, little is known about flax domestication processes involving multiple domestication events. This study applied genotyping-by-sequencing to infer flax domestication processes. Ninety-three Linum samples representing four flax domestication groups (oilseed, fiber, winter and capsular dehiscence) and its wild progenitor (or pale flax; L. bienne Mill.) were sequenced. SNP calling identified 16,998 SNPs that were widely distributed across 15 flax chromosomes. Diversity analysis found that pale flax had the largest nucleotide diversity, followed by indehiscent, winter, oilseed and fiber cultivated flax. Pale flax seemed to be under population contraction, while the other four domestication groups were under population expansion after bottleneck. Demographic inferences showed that five Linum groups carried clear genetic signals of multiple mixture events that were associated largely with oilseed flax. Phylogenetic analysis revealed that oilseed, fiber and winter flax formed two separate phylogenetic subclades. One subclade had abundant winter flax, along with some oilseed and fiber flax, mainly originating in the Near East and nearby regions. The other subclade mainly had oilseed and fiber flax originating from Europe and other parts of the world. Dating genetic divergences with an assumption of 10,000 years before present (BP) of flax domestication revealed that oilseed and fiber flax spread to Europe 5800 years BP and domestication for winter hardiness occurred in the Near East 5100 years BP. These findings provide new significant insights into flax domestication processes.