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Scientific Reports Jul 2024We present the first machine learning-based autonomous hyperspectral neutron computed tomography experiment performed at the Spallation Neutron Source. Hyperspectral...
We present the first machine learning-based autonomous hyperspectral neutron computed tomography experiment performed at the Spallation Neutron Source. Hyperspectral neutron computed tomography allows the characterization of samples by enabling the reconstruction of crystallographic information and elemental/isotopic composition of objects relevant to materials science. High quality reconstructions using traditional algorithms such as the filtered back projection require a high signal-to-noise ratio across a wide wavelength range combined with a large number of projections. This results in scan times of several days to acquire hundreds of hyperspectral projections, during which end users have minimal feedback. To address these challenges, a golden ratio scanning protocol combined with model-based image reconstruction algorithms have been proposed. This novel approach enables high quality real-time reconstructions from streaming experimental data, thus providing feedback to users, while requiring fewer yet a fixed number of projections compared to the filtered back projection method. In this paper, we propose a novel machine learning criterion that can terminate a streaming neutron tomography scan once sufficient information is obtained based on the current set of measurements. Our decision criterion uses a quality score which combines a reference-free image quality metric computed using a pre-trained deep neural network with a metric that measures differences between consecutive reconstructions. The results show that our method can reduce the measurement time by approximately a factor of five compared to a baseline method based on filtered back projection for the samples we studied while automatically terminating the scans.
PubMed: 38956417
DOI: 10.1038/s41598-024-63931-x -
Scientific Reports Jul 2024Power systems exhibit nonlinearity. causing dynamic instability and complex power oscillations. This research proposes an innovative strategy using the Novel Bat...
Power systems exhibit nonlinearity. causing dynamic instability and complex power oscillations. This research proposes an innovative strategy using the Novel Bat Algorithm (NBA) to achieve ideal Power System Stabilizers (PSSs) in a multimachine power system. The approach shifts electromechanical modes to specific areas in the s-plane. Enhancing the multi-machine power system and establishing stabilizer parameters for dynamic performance. The study examines the designed approach aptitude for standard lead-lag PSSs configurations. In order to elevate the global search problem and transfer some static operators for the optimum optimization process. the chaos mapping. also known as CNBA. is introduced into NBA. Four different forms of chaos maps are compared in experiments to resolve unconstrained mathematical issues in order to illustrate CNBA performance. In any other case. the challenge of designing PSS under a wide range of loading situations is transformed into an optimization challenge with the damping ratio of electromechanical modes with low damping as the target function. The optimal stabilizers' gains are gotten by employing the CNBA algorithm. Second plan. an effective technique is astutely established to delineate the PSS location and quantity using CNBA and another side using participation factor. To examine the efficacy of the proposed CNBA-based PSS on a large system; it is tested on the interconnected of New-England/New-York (16 generators and 68 buses) power grid. and verified by comparative study with NBA through eigenvalue analysis and nonlinear simulation to provide evidence the algorithmic competence of CNBA. The CNBA approach yields a minimum damping ratio of 37%. which is consistent with the its eigenvalue. In contrast, the NBA approach achieves a minimum damping ratio of 31%. The simulation results reveal the fine performance of the proposed CNBA-PSS in a convincing manner and its capacity to provide an excellent damping for inter-area and local oscillations under diverse operating cases compared to NBA-PSS then in the case of PSS location.
PubMed: 38956387
DOI: 10.1038/s41598-024-65101-5 -
Scientific Reports Jul 2024The creep characteristics and potential deformation patterns of gangue backfill material are crucial in backfill mining operations. This study utilizes crushed gangue...
The creep characteristics and potential deformation patterns of gangue backfill material are crucial in backfill mining operations. This study utilizes crushed gangue from the Gangue Yard in Fuxin City as the research material. An in-house designed, large-scale, triaxial gangue compaction test system was used. Triaxial compaction creep tests were conducted on gangue materials with varying particle size distributions. Analysis was performed based on different particle sizes, stresses, and confinement pressures. The study investigates the creep characteristics of the gangue under different conditions and explores the underlying causes. It reveals the relationship between the creep deformation of gangue materials and the passage of time. Mathematical methods are applied to develop a triaxial compaction creep power law model for gangue backfill materials. Finally, the creep results are fitted using an empirical formula approach.
PubMed: 38956294
DOI: 10.1038/s41598-024-66271-y -
Scientific Reports Jul 2024Exploring the factors influencing Food Security and Nutrition (FSN) and understanding its dynamics is crucial for planning and management. This understanding plays a...
Exploring the factors influencing Food Security and Nutrition (FSN) and understanding its dynamics is crucial for planning and management. This understanding plays a pivotal role in supporting Africa's food security efforts to achieve various Sustainable Development Goals (SDGs). Utilizing Principal Component Analysis (PCA) on data from the FAO website, spanning from 2000 to 2019, informative components are derived for dynamic spatio-temporal modeling of Africa's FSN Given the dynamic and evolving nature of the factors impacting FSN, despite numerous efforts to understand and mitigate food insecurity, existing models often fail to capture this dynamic nature. This study employs a Bayesian dynamic spatio-temporal approach to explore the interconnected dynamics of food security and its components in Africa. The results reveal a consistent pattern of elevated FSN levels, showcasing notable stability in the initial and middle-to-late stages, followed by a significant acceleration in the late stage of the study period. The Democratic Republic of Congo and Ethiopia exhibited particularly noteworthy high levels of FSN dynamicity. In particular, child care factors and undernourishment factors showed significant dynamicity on FSN. This insight suggests establishing regional task forces or forums for coordinated responses to FSN challenges based on dynamicity patterns to prevent or mitigate the impact of potential food security crises.
Topics: Bayes Theorem; Food Security; Humans; Africa; Spatio-Temporal Analysis; Food Supply; Principal Component Analysis; Nutritional Status
PubMed: 38956274
DOI: 10.1038/s41598-024-65989-z -
Scientific Reports Jul 2024A complex Polytopic fuzzy set (CPoFS) extends a Polytopic fuzzy set (PoFS) by handling vagueness with degrees that range from real numbers to complex numbers within the...
A complex Polytopic fuzzy set (CPoFS) extends a Polytopic fuzzy set (PoFS) by handling vagueness with degrees that range from real numbers to complex numbers within the unit disc. This extension allows for a more nuanced representation of uncertainty. In this research, we develop Complex Polytopic Fuzzy Sets (CPoFS) and establish basic operational laws of CPoFS. Leveraging these laws, we introduce new operators under a confidence level, including the confidence complex Polytopic fuzzy Einstein weighted geometric aggregation (CCPoFEWGA) operator, the confidence complex Polytopic fuzzy Einstein ordered weighted geometric aggregation (CCPoFEOWGA) operator, the confidence complex Polytopic fuzzy Einstein hybrid geometric aggregation (CCPoFEHGA) operator, the induced confidence complex Polytopic fuzzy Einstein ordered weighted geometric aggregation (I-CCPoFEOWGA) operator and the induced confidence complex Polytopic fuzzy Einstein hybrid geometric aggregation (I-CCPoFEHGA) operator, enhancing decision-making precision in uncertain environments. We also investigate key properties of these operators, including monotonicity, boundedness, and idempotency. With these operators, we create an algorithm designed to solve multiattribute decision-making problems in a Polytopic fuzzy environment. To demonstrate the effectiveness of our proposed method, we apply it to a numerical example and compare its flexibility with existing methods. This comparison will underscore the advantages and enhancements of our approach, showing its efficiency in managing complex decision-making scenarios. Through this, we aim to demonstrate how our method provides superior performance and adaptability across different situations.
PubMed: 38956256
DOI: 10.1038/s41598-024-65679-w -
Scientific Reports Jul 2024The 2D:4D digit ratio is commonly used as a surrogate possibly reflecting prenatal testosterone levels. Indirect evidence comes from studies investigating the...
The 2D:4D digit ratio is commonly used as a surrogate possibly reflecting prenatal testosterone levels. Indirect evidence comes from studies investigating the association between 2D:4D and human characteristics that likely relate to prenatal testosterone. In children, sex-typed play reveals large sex differences early in development and an influence of prenatal testosterone is likely. Findings on the association between 2D:4D and children's sex-typed play are heterogeneous and other influences on the development of sex-typed play have been suggested, most of all social influences like siblings, their sex and birth order. The current study examined the association between right and left 2D:4D, a proposed surrogate for prenatal testosterone exposure, which was assessed in right and left hands of N = 505 6-month-old children, and sex-typed play behavior, which was evaluated 3.5 years later using the Pre-School Activities Inventory (PSAI), and the influence of siblings. To capture differential effects of siblings' sex and birth order, dummy-coded variables were used reflecting having no siblings as well as older or younger sisters or brothers. Multiple regression models were used to investigate the association between PSAI scores and sex, right and left 2D:4D, being a singleton as well as having an older or younger sister or brother. It was shown that sex and having an older brother were significant predictors for sex-typed play. Effects were further disentangled by conducting separate regression analyses in boys and girls. In boys, a significant association between PSAI scores and having an older brother was revealed, in girls, no significant associations were found. Results are discussed highlighting the non-significant association between 2D:4D and children's sex-typed play, which weakens the applicability of 2D:4D as a surrogate reflecting influences of prenatal T. Further, the importance of social factors like siblings on children's sex-typed play is discussed.
Topics: Humans; Female; Male; Siblings; Fingers; Play and Playthings; Infant; Testosterone; Child, Preschool; Sex Characteristics; Pregnancy; Child; Prenatal Exposure Delayed Effects
PubMed: 38956189
DOI: 10.1038/s41598-024-65739-1 -
Scientific Reports Jul 2024Across vertebrates, adaptive behaviors, like feeding and avoiding predators, are linked to lateralized brain function. The presence of the behavioral manifestations of...
Across vertebrates, adaptive behaviors, like feeding and avoiding predators, are linked to lateralized brain function. The presence of the behavioral manifestations of these biases are associated with increased task success. Additionally, when an individual's direction of bias aligns with the majority of the population, it is linked to social advantages. However, it remains unclear if behavioral biases in humans correlate with the same advantages. This large-scale study (N = 313-1661, analyses dependent) examines whether the strength and alignment of behavioral biases associate with cognitive and social benefits respectively in humans. To remain aligned with the animal literature, we evaluate motor-sensory biases linked to motor-sequencing and emotion detection to assess lateralization. Results reveal that moderate hand lateralization is positively associated with task success and task success is, in turn, associated with language fluency, possibly representing a cascade effect. Additionally, like other vertebrates, the majority of our human sample possess a 'standard' laterality profile (right hand bias, left visual bias). A 'reversed' profile is rare by comparison, and associates higher self-reported social difficulties and increased rate of autism and/or attention deficit hyperactivity disorder. We highlight the importance of employing a comparative theoretical framing to illuminate how and why different laterization profiles associate with diverging social and cognitive phenotypes.
Topics: Humans; Cognition; Male; Female; Functional Laterality; Adult; Young Adult; Adolescent; Social Skills; Middle Aged; Emotions
PubMed: 38956070
DOI: 10.1038/s41598-024-64372-2 -
Nature Communications Jul 2024Hybrid improper ferroelectricity can effectively avoid the intrinsic chemical incompatibility of electronic mechanism for multiferroics. Perovskite superlattices, as...
Hybrid improper ferroelectricity can effectively avoid the intrinsic chemical incompatibility of electronic mechanism for multiferroics. Perovskite superlattices, as theoretically proposed hybrid improper ferroelectrics with simple structure and high technological compatibility, are conducive to device integration and miniaturization, but the experimental realization remains elusive. Here, we report a strain-driven oxygen octahedral distortion strategy for hybrid improper ferroelectricity in LaNiMnO/LaCoMnO double-perovskite superlattices. The epitaxial growth mode with mixed crystalline orientations maintains a large strain transfer distance more than 90 nm in the superlattice films with lattice mismatch less than 1%. Such epitaxial strain permits sustainable long-range modulation of oxygen octahedral rotation and tilting, thereby inducing and regulating hybrid improper ferroelectricity. A robust room-temperature ferroelectricity with remnant polarization of ~ 0.16 μC cm and piezoelectric coefficient of 2.0 pm V is obtained, and the density functional theory calculations and Landau-Ginsburg-Devonshire theory reveal the constitutive correlations between ferroelectricity, octahedral distortions, and strain. This work addresses the gap in experimental studies of hybrid improper ferroelectricity for perovskite superlattices and provides a promising research platform and idea for designing and exploring hybrid improper ferroelectricity.
PubMed: 38956065
DOI: 10.1038/s41467-024-49707-x -
Nature Communications Jul 2024The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we...
The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and produces the best results in a large-scale protein dynamics dataset; this model can accurately infer the dynamic information of a protein in only a few seconds. By learning effectively from experimental protein structure data and cryo-electron microscopy (cryo-EM) data integration, our approach is able to accurately identify the interactive bidirectional constraints and supervision between cryo-EM maps and PDB models in maximizing the dynamic prediction efficacy. Rigorous 5-fold cross-validation on the dataset demonstrates that RMSF-net achieves test correlation coefficients of 0.746 ± 0.127 at the voxel level and 0.765 ± 0.109 at the residue level, showcasing its ability to deliver dynamic predictions closely approximating molecular dynamics simulations. Additionally, it offers real-time dynamic inference with minimal storage overhead on the order of megabytes. RMSF-net is a freely accessible tool and is anticipated to play an essential role in the study of protein dynamics.
Topics: Cryoelectron Microscopy; Deep Learning; Proteins; Protein Conformation; Molecular Dynamics Simulation; Neural Networks, Computer; Databases, Protein; Computational Biology
PubMed: 38956032
DOI: 10.1038/s41467-024-49858-x -
Nature Communications Jul 2024Wavefront-shaping is a promising approach for imaging fluorescent targets deep inside scattering tissue despite strong aberrations. It enables focusing an incoming...
Wavefront-shaping is a promising approach for imaging fluorescent targets deep inside scattering tissue despite strong aberrations. It enables focusing an incoming illumination into a single spot inside tissue, as well as correcting the outgoing light scattered from the tissue. Previously, wavefront shaping modulations have been successively estimated using feedback from strong fluorescent beads, which have been manually added to a sample. However, such algorithms do not generalize to neurons whose emission is orders of magnitude weaker. We suggest a wavefront shaping approach that works with a confocal modulation of both the illumination and imaging arms. Since the aberrations are corrected in the optics before the detector, the low photon budget is directed into a single sensor spot and detected with high signal-noise ratio. We derive a score function for modulation evaluation from mathematical principles, and successfully use it to image fluorescence neurons, despite scattering through thick tissue.
PubMed: 38956030
DOI: 10.1038/s41467-024-49697-w