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A NiAl-layered double hydroxides memristor with artificial synapse function and its Boolean logic applications
In the era of artificial intelligence, there has been a rise in novel computing methods due to the increased demand for rapid and effective data processing. It is of great significance to develop memristor devices capable of emulating the computational neural network of the brain, especially in the realm of artificial intelligence applications. In this work, a memristor based on NiAl-layered double hydroxides is presented with excellent electrical performance, including analog resistive conversion characteristics and the effect of multi-level conductivity modulation. In addition, the device's conductance can be continuously adjusted by varying pulse width, interval, and amplitude. The successful replication of synaptic features has been achieved. In order to implement the functions of “NOT,” “AND,” and “OR,” a logic gate is constructed using two synaptic devices. The confirmation of the potential use of synaptic devices in brain-like computing was demonstrated. In addition, it demonstrates the potential of these devices in supporting computing models beyond von Neumann architecture.
Displacement MMG-based estimation of dynamic muscle viscoelasticity in the quadriceps during passive pedaling
Entropy-based methods for formulating bottom-up ultra-coarse-grained models
Bottom-up coarse-grained (CG) modeling is an effective means of bypassing the limited spatiotemporal scales of conventional atomistic molecular dynamics while retaining essential information from the atomistic model. A central challenge in CG modeling is the trade-off between accuracy and efficiency, as the inclusion of often pivotal many-body interaction terms in the CG force-field renders simulation markedly slower than simple pairwise models. The Ultra Coarse-Graining (UCG) method incorporates many-body terms through discrete internal state variables that modulate the CG force-field according to, e.g., changes in local environment when substantial chemical heterogeneities exist. However, assigning optimal internal states systematically from atomistic simulation data, as well as the practical application of bottom-up UCG theory to biomolecular systems, remain open problems. We develop two synergistic methods to aid in the development of UCG models that can capture inhomogeneities in atomistic systems such as those induced by phase coexistence. The first method establishes the systematic construction of UCG force-fields from a relative entropy minimization principle, while the second method utilizes machine-learning to obtain optimal local order parameters for enhanced model efficiency and transferability. We apply these methods to a methanol liquid–vapor interface and the ripple phase of a 1,2-dipalmitoyl-sn-glycero-3-phosphocholine lipid bilayer and demonstrate that UCG modeling alone recapitulates aspects of phase coexistence that are otherwise not observed in CG modeling.
Clinical characteristics and predictive biomarkers of intensive care unit-acquired weakness in patients with cardiogenic shock requiring mechanical circulatory support
Optimizing oil–water separation using fractal surfaces
Oil has become a prevalent global pollutant, stimulating the research to improve the techniques to separate oil from water. Materials with special wetting properties—primarily those that repel water while attracting oil—have been proposed as suitable candidates for this task. However, one limitation in developing efficient substrates is the limited available volume for oil absorption. In this study, we investigate the efficacy of disordered fractal materials in addressing this challenge, leveraging their unique wetting properties. Using a combination of a continuous model and Monte Carlo simulations, we characterize the hydrophobicity and oleophilicity of substrates created through ballistic deposition (BD). Our results demonstrate that these materials exhibit high contact angles for water, confirming their hydrophobic nature while allowing significant oil penetration, indicative of oleophilic behavior. The available free volume within the substrates varies from 60% to 90% of the total volume of the substrate depending on some parameters of the BD. By combining their water and oil wetting properties with a high availability of volume, the fractal substrates analyzed in this work achieve an efficiency in separating oil from water of nearly 98%, which is significantly higher compared to micro-pillared surfaces made from the same material but lacking a fractal design.
Proton response and neutron spectrum unfolding by solution-grown trans-stilbene scintillator
Excited electronic states of Na2 and K2: The potential for long-lived “reservoir” states leading to collision induced population inversions
Potential energy curves (PECs) for the spin-free (ΛS) and spin–orbit (Ω) states associated with the four lowest-lying dissociation channels of Na2 and K2 were calculated at the SA-CASSCF/SO-CASPT2/aug-cc-pwCVQZ-DK level. The PECs of Na2 were consistent with the experimental data and with the FS-CCSD (2,0) calculations, reproducing the double-well and the “shelf” character for some of the potentials of the excited states. For K2, the PECs behaved in a similar way and the spectroscopic parameters for the ground and the excited states are in good agreement with the available experimental values. The dissociation energy of K2 was predicted to be De = 4454 cm−1, within an agreement of 5 cm−1 with the experiments. For Na2, De = 5789 cm−1 compared to the experimental value of 6022 cm−1. The inclusion of spin–orbit coupling effects resulted in avoided crossings, which affect the PECs. Spin–orbit changes the predicted curves for some excited Ω states arising from ΛS states that overlap each other, affecting their associated vibrational frequencies and bond distances. The current studies of the low-lying states in K2 reveal a similar structure to those of Na2, which suggests the accessibility of long-lived energy storing reservoir states and possible population inversions in K2 following prior experimental work on the reaction of halogen atoms with Na3 to produce excited states of Na2.
Programmed cell death-related gene IL20RA facilitates tumor progression and remodels tumor microenvironment in thyroid cancer
DRAM1 enhances the proliferation and metastasis of gastric cancer through the PI3K/AKT/mTOR signaling pathway and energy metabolism
OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning
Abstract Glaucoma poses a growing health challenge projected to escalate in the coming decades. However, current automated diagnostic approaches on Glaucoma diagnosis solely rely on black-box deep learning models, lacking explainability and trustworthiness. To address the issue, this study uses optical coherence tomography (OCT) images to develop an explainable artificial intelligence (XAI) tool for diagnosing and staging glaucoma, with a focus on its clinical applicability. A total of 334 normal and 268 glaucomatous eyes (86 early, 72 moderate, 110 advanced) were included, signal processing theory was employed, and model interpretability was rigorously evaluated. Leveraging SHapley Additive exPlanations (SHAP)-based global feature ranking and partial dependency analysis (PDA) estimated decision boundary cut-offs on machine learning (ML) models, a novel algorithm was developed to implement an XAI tool. Using the selected features, ML models produce an AUC of 0.96 (95% CI: 0.95–0.98), 0.98 (95% CI: 0.96–1.00) and 1.00 (95% CI: 1.00–1.00) respectively on differentiating early, moderate and advanced glaucoma patients. Overall, machine outperformed clinicians in the early stage and overall glaucoma diagnosis with 10.4 –11.2% higher accuracy. The developed user-friendly XAI software tool shows potential as a valuable tool for eye care practitioners, offering transparent and interpretable insights to improve decision-making.
Nano urea’s environmental edge and economic efficacy in boosting wheat grain yield across diverse Indian agro-climates
Research on calculation of transverse bending moment of deck of box girder with long-span and long cantilever
Lymphatic vessel network injury reduces local tumor control despite preservation of the tumor-draining lymph node
Author Correction: AI based predictive acceptability model for effective vaccine delivery in healthcare systems
Allometric equations for estimating above and belowground biomass of Colophospermum mopane in Mozambique
Calcium to magnesium ratio as a superior biomarker for nephrolithiasis detection in primary hyperparathyroidism
Retraction Note: Synthesis of silver nanoparticles using Plantago lanceolata extract and assessing their antibacterial and antioxidant activities
Childhood BMI trajectories and sociodemographic factors in an Italian pediatric population
Analysis of the spatial distribution of metabolites in Aloe vera leaves by mass spectrometry imaging and UHPLC-UHRMS
Abstract This study presents an investigation of the chemical composition of Aloe vera leaf tissue with a focus on the spatial distribution of compounds. The composition was studied using two mass spectrometry imaging techniques: silver-109 nanoparticles assisted laser desorption/ionization mass spectrometry imaging (109AgNPs-LDI-MSI) and laser ablation-remote atmospheric pressure photoionization/chemical ionization mass spectrometry imaging (LARAPPI/CI-MSI) and the identification was aided by ultra-high-performance liquid chromatography and ultra-high-resolution mass spectrometry (UHPLC-UHRMS) analysis. The results showed an abundance of phenolic compounds with antioxidant, antimicrobial, and anti-inflammatory properties, making it a beneficial food additive and food packaging material. Analysis of the results of mass spectrometry imaging provided information about the potential changes in metabolic pathway expression in different regions of the leaf.