Universal Model Research Articles

The impact of Ru and Gd dopants on the dielectric and conductive properties of NiO nanoparticles: A comparative study

This comparative study investigates the difference between the transition metal (Ru) and rare earth element (Gd) as dopants in NiO nanoparticles, to unveil their suitability in various applications. Pure, Ru- and Gd-doped NiO nanoparticles have been synthesized by the chemical co-precipitation method, in the presence of polyvinylpyrrolidone, as a capping agent. The EDX pattern revealed the purity, the un-stoichiometric nature of the synthesized nanoparticles, and the influence of the Ru- and Gd-dopants in generating advantageous structural imperfections, including oxygen and nickel vacancies. The dielectric and conductivity measurements have provided valuable insights into the electrical behavior of Ru- and Gd-doped NiO nanoparticles. The influence of the Ru- and Gd-dopants on the Debye model, universal dielectric model, and relaxation mechanism was implemented. The dielectric constant was affected by the dopants, where the Ru-dopants reduced it by a factor of 0.24, however, the Gd-dopants boosted it by a factor of 4. Moreover, the opposing influence of the Ru- and Gd-dopants on the nickel vacancies has tuned the ac conductivity of the NiO lattice, where it was reduced by 75 % with Ru-dopants due to the suppressed nickel vacancies and enhanced by 70 % with the Gd-dopants due to the augmented nickel vacancies. The dc conductivity was examined at ranging temperatures to explore the conduction mechanism and the effect of the dopants. The multi-functional ability of the pure, Ru- and Gd-doped NiO nanoparticles was tested by studying the complex power and temperature coefficient of resistance (TCR), from which the dominating capacitive and resistive nature is identified for the samples. Besides, NiO nanoparticles have attained a maximum TCR value of −10.41 %, rendering it a superior material for bolometric devices, even better than the used conventional materials. The studied dielectric and conductive properties unveiled the potential applicability of the samples in microelectronics, storage capacitor applications, and bolometric devices.

Machine Learning Meets Physics-based Modeling: A Mass-spring System to Predict Protein-ligand Binding Affinity.

Computational assessment of the energetics of protein-ligand complexes is a challenge in the early stages of drug discovery. Previous comparative studies on computational methods to calculate the binding affinity showed that targeted scoring functions outperform universal models. The goal here is to review the application of a simple physics-based model to estimate the binding. The focus is on a mass-spring system developed to predict binding affinity against cyclin-dependent kinase. Publications in PubMed were searched to find mass-spring models to predict binding affinity. Crystal structures of cyclin-dependent kinases found in the protein data bank and two web servers to calculate affinity based on the atomic coordinates were employed. One recent study showed how a simple physics-based scoring function (named Taba) could contribute to the analysis of protein-ligand interactions. Taba methodology outperforms robust physics-based models implemented in docking programs such as AutoDock4 and Molegro Virtual Docker. Predictive metrics of 27 scoring functions and energy terms highlight the superior performance of the Taba scoring function for cyclin- dependent kinase. The recent progress of machine learning methods and the availability of these techniques through free libraries boosted the development of more accurate models to address protein-ligand interactions. Combining a naïve mass-spring system with machine-learning techniques generated a targeted scoring function with superior predictive performance to estimate pKi.

Fixed-time event-triggered adaptive control of manipulator system with input deadzone and model uncertainty

There exist universal model uncertainty and input deadzone in mechatronic motion plant due to unknown model parameters and actuator physical feature, which will degrade the motion performance and stability. In this study, a fixed-time event-triggered adaptive control is proposed in n-DOF manipulator system to address these problems. Firstly, the dynamic model of n-DOF manipulator with joint friction and input deadzone is constructed by Lagrange technique. Then a radial basis function neural network (RBFNN) is designed to approximate uncertain dynamics. Meanwhile, an event-triggered mechanism (ETM) is used to reduce the control update frequency and to save the communication resource. Furthermore, an adaptive estimation law is adopted to compensate unknown input deadzone parameters. Based on backstepping iteration technique, a fixed-time convergent controller is presented to guarantee the system state errors convergence to zero neighborhood in finite time irrelevant to arbitrary initial state. Finally, the effectiveness of the proposed control scheme is verified by sufficient comparative simulation and experimental results.

Comparative study on in-situ resilient modulus of subgrade estimated using in-situ modulus detector

This study presents a comprehensive evaluation of the resilient modulus of unsaturated subgrade soils using a novel in-situ modulus detector (IMD) along with various laboratory and field-testing methods. The current study employs several testing devices, including bender elements, standard dynamic cone penetrometer, and crosshole-type dynamic cone penetrometer (CDP), to assess the reliability of the IMD in estimating the resilient modulus profiles of small-scale homogeneous subgrade models in unsaturated conditions. The index properties, compaction and strength characteristics of the compacted soils, are evaluated. The resilient modulus values obtained from the IMD are compared with those obtained from repeated load triaxial tests under various stress states. Experimental results show a decrease in the penetration depth per blow with increased dry unit weight. In addition, elastic modulus and shear wave velocity increase with soil density. The resilient modulus estimated from the IMD increase with both depth and soil density. A robust correlation has been established between the resilient modulus values estimated from the IMD and those estimated from the CDP or bender element tests. Furthermore, the resilient moduli estimated from the universal model and the IMD demonstrate linear relationships with a coefficient of determination greater than 0.8. Thus, the IMD may be useful for the in-situ evaluation of resilient modulus in unsaturated subgrade soils, considering influencing factors such as soil density and confining pressure.

Status of Electromagnetically Accelerating Universe.

To describe the dark side of the universe, we adopt a novel approach where dark energy is explained as an electrically charged majority of dark matter. Dark energy, as such, does not exist. The Friedmann equation at the present time coincides with that in a conventional approach, although the cosmological "constant" in the Electromagnetic Accelerating Universe (EAU) Model shares a time dependence with the matter component. Its equation of state is ω ≡ P/ρ ≡ -1 within observational accuracy.

Design of a Universal Model of Parental Education in Preschool Education

Design of a Universal Model of Parental Education in Preschool Education

MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction.

Enzymatic reaction kinetics are central in analyzing enzymatic reaction mechanisms and target-enzyme optimization, and thus in biomanufacturing and other industries. The enzyme turnover number (kcat) and Michaelis constant (Km), key kinetic parameters for measuring enzyme catalytic efficiency, are crucial for analyzing enzymatic reaction mechanisms and the directed evolution of target enzymes. Experimental determination of kcat and Km is costly in terms of time, labor, and cost. To consider the intrinsic connection between kcat and Km and further improve the prediction performance, we propose a universal pretrained multitask deep learning model, MPEK, to predict these parameters simultaneously while considering pH, temperature, and organismal information. Through testing on the same kcat and Km test datasets, MPEK demonstrated superior prediction performance over the previous models. Specifically, MPEK achieved the Pearson coefficient of 0.808 for predicting kcat, improving ca. 14.6% and 7.6% compared to the DLKcat and UniKP models, and it achieved the Pearson coefficient of 0.777 for predicting Km, improving ca. 34.9% and 53.3% compared to the Kroll_model and UniKP models. More importantly, MPEK was able to reveal enzyme promiscuity and was sensitive to slight changes in the mutant enzyme sequence. In addition, in three case studies, it was shown that MPEK has the potential for assisted enzyme mining and directed evolution. To facilitate in silico evaluation of enzyme catalytic efficiency, we have established a web server implementing this model, which can be accessed at http://mathtc.nscc-tj.cn/mpek.

The Pristine survey. XXIV. The Galactic underdogs: Dynamic tales of a Milky Way metal-poor population

Metal-poor stars hold key information on the early Milky Way. Through the identification and characterisation of substructures, one can understand internal mechanisms (including merger and accretion events), which are indispensable to reconstruct the formation history of the Galaxy. To allow an investigation of a population of very metal-poor stars ( Fe/H < -1.7) with disc-like orbits (planar and prograde), high angular momenta ($L_z$/$J_ tot $ > 0.5) and rotational velocities ($V_ $) proposed in the literature, we used a sample of sim 3M giant stars with Gaia DR3 BP/RP information and Pristine-Gaia metallicities down to -4.0 dex that we aimed to decontaminate. To achieve this, we constructed a sample as free as possible from spurious photometric estimates, an issue commonly encountered for high $V_ metal-poor stars. We created a statistically robust sample of sim 36 000 Pristine-Gaia very metal-poor ( Fe/H < -1.7) giant stars, using APOGEE and LAMOST data (adding GALAH and GSP-spec for verification) to estimate and remove contamination. We investigated the spatial and kinematic properties of the decontaminated sample, making use of $V_ as well as the action space, which are both powerful tools to disentangle stellar populations. The global distribution of very metal-poor stars in our sample shows the typical kinematics, orbital properties, and spatial distributions of a halo; however, as in previous works, we found a pronounced asymmetry in the $L_z$ and $V_ distributions, in favour of prograde stars. We showed that this excess is predominantly due to prograde-planar stars (10 $<!PCT!>$ of the very metal-poor population), which can be detected down to Fe/H = -2.9 at a 2sigma confidence level. This prograde-planar population contains stars with $V_ $ and $Z_ max $ < 1.5\,kpc. While the overall orbital configurations max $ - $R_ max $ or action space distributions) of our sample match that of a halo, the highly prograde and planar subset (2 $<!PCT!>$ of the very metal-poor population) also bears characteristics classically associated with a thick disc: (i) a spatial distribution compatible with a short-scaled thick disc, (ii) a $Z_ max $ - $R_ max $ distribution similar to the one expected from the thick disc prediction of the Gaia Universe Model Snapshot, and (iii) a challenge to erase its signature assuming a stationary or prograde halo with $ V_ phi $ sim 30-40 km.s$^ $. Altogether, these results seem to rule out that these highly prograde and planar stars are part of a thin disc population and, instead, support a contribution from a metal-weak thick disc. Higher resolution spectra are needed to fully disentangle the origin(s) of the population.

Optimization-Based Pairwise Interaction Point Process (O-PIPP): A Precise and Universal Retinal Mosaic Modeling Approach.

A retinal mosaic, the spatial organization of a population of homotypic neurons, is thought to sample a specific visual feature into the feedforward visual pathway. The purpose of this study was to propose a universal modeling approach for precisely generating retinal mosaics and overcoming the limitations of previous models, especially in modeling abnormal mosaic patterns under disease conditions. Here, we developed the optimization-based pairwise interaction point process (O-PIPP). It incorporates optimization techniques into previous simulation approaches, enabling directional control of the simulation process according to the user-designed optimization target. For the convenience of the community, we implemented the O-PIPP approach into a Python package and a website application. We showed that the O-PIPP can generate more precise neural spatial patterns of healthy and diseased mosaics compared to previous phenomenological approaches. Notably, through modeling the retinal neural circuitry with O-PIPP-simulated retinitis pigmentosa cone mosaics, we elucidated how the cone mosaic rearrangement impacted the information processing of ganglion cells. The O-PIPP provides a precise and universal tool to simulate realistic mosaics, which could help to investigate the function of retinal mosaics in vision.

Generalized Brans-Dicke cosmology, noether symmetries and quantization

A generalized Brans-Dicke theory of gravitation is applied to investigate a cosmological model for a primordial universe that includes a self-interacting bosonic field as source. This model permits, initially, to consider an arbitrary ω function, not usual for BD formulations. The imposition of Noether symmetries conditions identifies a reduced space for the ω function. This model is shown to present a variety of behaviors consistent with a primordial era, depending on the parameters chosen. We find that the bosonic field is capable of promoting a transition from an initially accelerated to a decelerated regime. The self-interaction potential is also restricted by the Noether conditions, and can be interpreted as a vacuum energy contribution. The model also permits a quantization process, going through a hamiltonian formulation and recognizing the presence of cyclic variables. The Bohm approach shows as result a consistent model with a smooth classical limit as the Universe leaves the Planck era.

Overcoming data scarcity in biomedical imaging with a foundational multi-task model.

Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more specialized datasets common in biomedical imaging. Here we propose a multi-task learning strategy that decouples the number of training tasks from memory requirements. We trained a universal biomedical pretrained model (UMedPT) on a multi-task database including tomographic, microscopic and X-ray images, with various labeling strategies such as classification, segmentation and object detection. The UMedPT foundational model outperformed ImageNet pretraining and previous state-of-the-art models. For classification tasks related to the pretraining database, it maintained its performance with only 1% of the original training data and without fine-tuning. For out-of-domain tasks it required only 50% of the original training data. In an external independent validation, imaging features extracted using UMedPT proved to set a new standard for cross-center transferability.

Generalization of interfacial thermal conductance based on interfacial phonon localization

Interfacial energy transport is of great engineering and scientific importance. Traditional theoretical treatment based on phonon reflection and transmission only provides qualitative understanding of the interfacial thermal conductance (G). In the interface region, the material has gradual (covalent) or abrupt (van de Waals) physical structure transition, each transition features interface-region atomic interactions that are different from those of both adjoining sides. This difference makes the interface-region phonons extremely localized. Here, by constructing an “equivalent interfacial medium” (EIM) that accounts for the extremely localized phonon region, G can be described by a universal physical model that is characterized by an “interface characteristic temperature” (Θint) and energy carrier transfer time. The EIM model fits widely reported G ∼ T (T: temperature) data with high accuracy and provides remarkable prediction of G at different temperatures based on 2–3 experimental data points. Under normalized temperature (T/Θint) and interfacial thermal conductance (G/Gmax), all literature data of G can be universally grouped to a single curve. The EIM model provides a solid correlation between G and interfacial structure and is expected to significantly advance the physical understanding and design of interfacial energy transport toward high-efficiency energy conversion, transport, and micro/nanoelectronics.

Modeling of fluid‐rigid body interaction in an electrically conducting fluid

AbstractWe derive a mathematical model for the motion of several insulating rigid bodies through an electrically conducting fluid. Starting from a universal model describing this phenomenon in generality, we elaborate (simplifying) physical assumptions under which a mathematical analysis of the model becomes feasible. Our main focus lies on the derivation of the boundary and interface conditions for the electromagnetic fields as well as the derivation of the magnetohydrodynamic approximation carried out via a nondimensionalization of the system.

Background gravitational waves as signature of the accelerated cosmic expansion

We propose a toy model of a spherical universe made up of an exotic dark gas with temperature T in thermal equilibrium with a black-body in adiabatic expansion. Each particle of this exotic gas mimics a kind of particle of dark energy. So, each dark particle occupies a very small area of space so-called Planck area [Formula: see text], which represents the minimum area of the whole space-time given by the spherical surface with area [Formula: see text], where [Formula: see text] is the Hubble radius. We realize that such spherical surface is the surface of the black-body for representing the dark universe as if it were the surface of an expanding balloon. Thus, we are able to derive the law of universal gravitation, thus leading us to understand the cosmological anti-gravity. We estimate the tiny order of magnitude of the cosmological constant and the acceleration of expansion of the dark sphere. In this toy model, as the dark universe can be thought of as a large black body, when we obtain its power and frequency of emission of radiation, we find very low values. We conclude that such radiation and frequency of the black body made up of dark energy is a background gravitational wave with very low frequency in the order of [Formula: see text] due to the slight stretching of the fabric of space-time.

Rural Settlement Optimization for Ecologically Sensitive Area Evaluations Based on Geo-Proximity and the Soil‒Water Conservation Capacity

Rural communities in ecologically sensitive areas are confronted with environmental challenges and land waste. The hollowing out of villages because of the steady loss of young people has brought these problems to the fore. Research on the remediation of rural settlements in ecologically sensitive regions is scarce. This paper focuses on Tong Yu County, a region located in the agricultural and pastoral intertwined zones of western Jilin, which is known for its ecological sensitivity. On the basis of the results of the geographical assessment of the area via hierarchical analysis (AHP method)and soil and water conservation capacity evaluation via the revised universal soil loss equation model(RUSLE) and the net primary production quantitative index model(NPP model), we propose a rural development program that divides the study area into three different zones with different development orientations, namely, potential enhancement, agglomeration development, and expansion restrictions. Moreover, we conceived a rural settlement remediation scheme using the kernel density estimation method and provided remediation strategies for rural settlements of various density levels. The rural settlement development program and the rural settlement remediation scheme led to the optimization of rural settlements. Notably, with increasing kernel density, the area of rural settlements tends to increase and then decrease. Our research helps save 5.059 km2 of land resources in the study area and offers guidance for improving the rural settlement layout in Tong Yu County.

An approach for full space inverse materials design by combining universal machine learning potential, universal property model, and optimization algorithm

We present a full space inverse materials design (FSIMD) approach that fully automates the materials design for target physical properties without the need to provide the atomic composition, chemical stoichiometry, and crystal structure in advance. Here, we used density functional theory reference data to train a universal machine learning potential (UPot) and transfer learning to train a universal bulk modulus model (UBmod). Both UPot and UBmod were able to cover materials systems composed of any element among 42 elements. Interfaced with optimization algorithm and enhanced sampling, the FSIMD approach is applied to find the materials with the largest cohesive energy and the largest bulk modulus, respectively. NaCl-type ZrC was found to be the material with the largest cohesive energy. For bulk modulus, diamond was identified to have the largest value. The FSIMD approach is also applied to design materials with other multi-objective properties with accuracy limited principally by the amount, reliability, and diversity of the training data. The FSIMD approach provides a new way for inverse materials design with other functional properties for practical applications.

Generative attention based framework for implicit language change detection

Spoken language change detection (LCD) refers to detecting language switching points in a multilingual speech signal. Most approaches in literature use the explicit framework that requires the modeling of intermediate phonemes and Senones to distinguish language. However, such techniques are limited when used with resource scare/ zero resource languages. Hence as an alternative, this study explores implicit frameworks to perform LCD. The focus of this work is to detect language change when a single speaker is speaking two languages. In this direction, a subjective study is performed to analyze the method humans adapt to discriminate languages. The outcome of the subjective study suggests humans require more neighborhood duration to detect language change. The initial observation suggests, that detecting language change is challenging using the baseline implicit unsupervised distance-based approach. Inspired by human cognition, prior language knowledge is integrated into the computational framework through the Gaussian mixture model and universal background model (GMM-UBM), temporal information via attention, and pattern storage using the Generative adversarial network (GAN) to enhance language discrimination. The experimental results on the Microsoft code-switched (MSCS) dataset show, compared to the unsupervised distance-based approach, the performance of the proposed LCD relatively improved by 19.3%, 47.3%, and 50.7% using the GMM-UBM, attention, and GAN-attention based framework, respectively.

Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision

In-process measurement of machining precision is of great importance to advanced manufacturing, which is an essential technology to realize compensation machining. In terms of cost-effectiveness and repeatability of computer vision, it has become a trend to replace traditional manual measurement with computer vision measurement. In this paper, an in-process measurement method is proposed to improve precision and reduce the costs of machining precision. Firstly, a universal features model framework of machining parts is established to analyze the CAD model and give standard information on the machining features. Secondly, a window generator is proposed to adaptively crop the image of the machining part according to the size of features. Then, the automatic detection of the edges of machining features is performed based on regions of interest (ROIs) from the cropped image. Finally, the measurement of machining precision is realized through a Hough transform on the detected edges. To verify the effectiveness of the proposed method, a series of in-process measurement experiments were carried out on machined parts with various features and sheet metal parts, such as dimensional accuracy measurement tests, straightness measurement tests, and roundness measurement tests under the same part conditions. The best measurement accuracy of this method for dimensional accuracy, straightness, and roundness were 99%, 97%, and 96%, respectively. In comparison, precision measurement experiments were conducted under the same conditions using the Canny edge detection algorithm, the sub-pixel edge detection algorithm, and the Otsu–Canny edge detection algorithm. Experimental results show that the feature-model-based in-process measurement of machining precision using computer vision demonstrates superiority and effectiveness among various measurement methods.

Bell Sampling from Quantum Circuits.

A central challenge in the verification of quantum computers is benchmarking their performance as a whole and demonstrating their computational capabilities. In this Letter, we find a universal model of quantum computation, Bell sampling, that can be used for both of those tasks and thus provides an ideal stepping stone toward fault tolerance. In Bell sampling, we measure two copies of a state prepared by a quantum circuit in the transversal Bell basis. We show that the Bell samples are classically intractable to produce and at the same time constitute what we call a "circuit shadow": from the Bell samples we can efficiently extract information about the quantum circuit preparing the state, as well as diagnose circuit errors. In addition to known properties that can be efficiently extracted from Bell samples, we give several new and efficient protocols: an estimator of state fidelity, an error-mitigated estimator of Pauli expectation values, a test for the depth of a circuit, and an algorithm to estimate a lower bound on the number of T gates in the circuit. With some additional measurements, the latter algorithm can be used to learn a full description of states prepared by circuits with low T count.

Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces.

Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the computational efficiency of classical force fields. This allows simulations ranging from atoms, molecules, and biosystems, to solid and bulk materials, surfaces, nanomaterials, and their interfaces and complex interactions. A recent class of advanced MLIPs, which use equivariant representations and deep graph neural networks, is known as universal models. These models are proposed as foundation models suitable for any system, covering most elements from the periodic table. Current universal MLIPs (UIPs) have been trained with the largest consistent data set available nowadays. However, these are composed mostly of bulk materials' DFT calculations. In this article, we assess the universality of all openly available UIPs, namely MACE, CHGNet, and M3GNet, in a representative task of generalization: calculation of surface energies. We find that the out-of-the-box foundation models have significant shortcomings in this task, with errors correlated to the total energy of surface simulations, having an out-of-domain distance from the training data set. Our results show that while UIPs are an efficient starting point for fine-tuning specialized models, we envision the potential of increasing the coverage of the materials space toward universal training data sets for MLIPs.