Cgs Units Research Articles

Low-temperature sintering of bauxite raw material with alkali as an alternative to the parallel Bayer sintering process

The aim is to develop an alternative technology of bauxite raw material processing based on low-temperature sintering of bauxite with caustic alkali, as well as to solve the issue of carbon footprint control at alumina refineries in the Urals. Laboratory tests were carried out by sintering artificial bemite and hematite with chemically pure caustic alkali at temperatures of 300, 500 and 700°C and their further leaching in weakly alkaline solutions. To study the phase, chemical, and particle size distribution of red muds after leaching, various physical and chemical methods of analysis were used, such as X-ray fluorescence, titration method, X-ray phase analysis, scanning electron microscopy, magnetometry with a vibrating sample. The Brunauer – Emmett – Teller method was used to determine the specific surface area and porosity. The study of the kinetics of the solid-phase reaction of the bemite interaction with caustic alkali has shown the kinetic interaction in the temperature range under study. Moreover, sintering of hematite at temperatures of 300 and 500°C and further leaching of the sinter with water resulted in mineralogical changes in the sludge with the production of a new mineral, maghemite, which possesses magnetic properties. When studying the magnetic properties of red mud of lowtemperature sintering of bauxite, we determined that the magnetization was as high as 19–20 electromagnetic units per g (at a sample density of 2.38 g/cm3) at a magnetic field of 10 kE. The specific surface area of these samples was 54.97 and 51.77 m2/g. The performed studies confirm that the proposed technology can be adapted for bauxite to produce highiron red slimes, thus contributing to the integrated processing of bauxite raw materials. In addition, ways to reduce carbon emissions at alumina refineries by eliminating the sintering operation with soda and limestone, which is accompanied by CO2 emission during decomposition of these compounds, can be studied.

Diffusion Coefficients of Variable-Size Amphiphilic Additives in a Glass-Forming Polyethylene Matrix.

Diffusion of additives in polymers is an important issue in the plastics industry since migratory-type molecules are widely used to tune the properties of polymeric composites. Predicting the diffusional behavior of new additives can minimize the need for repetitive experiments. This work presents molecular dynamics simulations at the microsecond time scale and uses the MARTINI force field to estimate self-diffusion coefficients, D, of six monounsaturated amides and their analogs carboxylic acids in polyethylene matrices (PE, MW = 5600 Da). The results are strongly influenced by the glass-forming properties of the PE matrix, which we characterize by three distinct temperatures. The metastability region (T < 325 K), the glass transition temperature (Tg = 256-260 K), and the end of the transition (T ≅ 200 K). Self-diffusion mechanisms are inferred from the results of the dependence of D on the molecular mass of the additive, observing a Rouse-like behavior at high temperatures and deviations from it within the metastability region of the matrix. Interestingly, D values are nonsensitive to the nature of the considered polar head for additives of similar size. The temperature-dependent behavior of D follows, at fixed additive size, a linear Arrhenius pattern at high temperatures and a super Arrhenius trend at lower temperatures, which is well represented with a power law equation as predicted by the Mode Coupling Theory (MCT). We offer a conceptual explanation for the observed super-Arrhenius behavior. This explanation draws on Truhlar and Kohen's interpretation of the available energies at both the initial and the transition states along the diffusion pathway. The matrix's mobility significantly affects solute self-diffusion, yielding equal activation enthalpies for the Arrhenius region or the same power law parameters for the super-Arrhenius regime. Finally, we establish a one-to-one time-equivalence of the self-diffusion processes between CG and all-atom systems for the largest additives and the PE matrix in the high-temperature regime.

Optical red shift spectra in CuxGe32S68-x films for infrared and solar cell window applications

The physical characteristics of Cu-doped ternary glasses have been analyzed. The CuxGe32S68 − x (x = 0.0, 4.0, 7.0, 10.0 and 13.0 at.%) chalcogenide alloys have been manufactured by the conventional melt quenching method. The glass densities (ρ) increase with the Cu amounts, whereas the main atomic volume (Vm) decreases. Transmission and reflectance were measured in the wavelength range of 0.35–2.5 μm. The generated envelops (RM and Rm) of reflectance spectra were used to calculate the film thickness (t), the refractive index (n), and the extinction coefficient (k). With the increase in Cu contents from 0.0 to 13.0 at.%., the n values increased from 2.07 to 2.43, whereas the optical gap (Eg) decreased from 2.81 eV to 2.42 eV. The obtained results were explained in terms of electronic polarizability, plasma frequency, metallization parameter, and refraction losses, which have been calculated to understand the need for amorphous chalcogenide compositions that can be applied for optical device purposes. The effect of Cu content on the non-linear index of refraction (n2) and third-order susceptibility (χ (3)) has been studied. The value of n2 increased from 13.88 × 10−12 to 60.2 × 10−12 esu, whereas the value of χ (3) rose from 7.6 × 10−11 to 38.83 × 10−13 esu with the addition of denser and higher atomic radius elements Cu in the CGS system. Overall, the composition of Cu13Ge32S55 is suitable, as all-optical parameters show the maximum values that can be used for optical device purposes.

Bionic Fish-Shaped Triboelectric-Electromagnetic Hybrid Generator via a Two-Stage Swing Mechanism for Water Flow Energy Harvesting and Condition Monitoring.

The water flow energy of rivers is an important renewable and clean energy that plays a vital role in human life but is challenging to harvest at low flow velocity. This work proposes a bionic fish-shaped triboelectric-electromagnetic hybrid generator (BF-TEHG) via a two-stage swing mechanism for harvesting water flow energy. It is designed to simulate the shape of fish, effectively improving its ability to utilize low-velocity water flow energy and enabling it to operate at a minimum flow rate of 0.24 m/s. Furthermore, the impact of motion parameters on electrical performance is studied. The triboelectric and electromagnetic power-generation units can generate peak powers of 0.55 and 0.34 mW in the simulated river environments with a flow velocity of 0.98 m/s. In applications, after being immersed in water for 40 days, the BF-TEHG maintains its electrical performance without reduction, indicating excellent water immersion durability. Therefore, this work proposes an efficient strategy to harvest low-velocity water flow energy and provides an acceptable candidate for monitoring water flow conditions.

Self-sustained and self-wakeup wireless vibration sensors by electromagnetic-piezoelectric-triboelectric hybrid energy harvesting

Self-sustained wireless sensing nodes (WSN) with energy harvesting, low power consumption, and intelligent sensing are the key devices in artificial intelligence of things (AIoT) area. This paper designs a tri-hybrid generator with electromagnetic-piezoelectric-triboelectric units integrated into one vibration energy harvesting structure. The tri-hybrid generator has three functions with electromagnetic unit for power supply with 13.1 mW, two piezoelectric units as the accelerometer for amplitude and frequency sensing with broadband decoupling function, triboelectric unit for triggering the self-wakeup WSN. The tri-hybrid generator structure is simulated and optimized by theory and finite element modeling, and the performance is tested in a vibration platform. To achieve self-sustained WSN, this study designs the power management circuit with maximum power point tracking and undervoltage lock function, combining periodic wake-up with overload self-wakeup to decrease 94.2% WSN power consumption. Self-sustained WSN is realized in the laboratory and verified on the vehicle engine. Finally, the application of wireless vibration sensing is verified on the vehicle engine. This work has a wide range of applications in vibration scenarios and hopefully promotes the development of AIoT.

Evaluation of efficiency of air channel cooling of a multiple-turn coil for electromagnetic metal forming

s An efficient and safe method of air channel cooling for multiple-turn coils is proposed. Minimal discharge cycle duration for electromagnetic unit is defined for performing large number of similar electromagnetic metal forming technological operations. Keywords: electromagnetic metal forming, multiple-turn coil, cooling, vortex tube, ejector. amokhvalov.vn@ssau.ru, chernikov.dg@ssau.ru, vvz@ssau.ru

Gaussian dynamic recurrent unit for emitter classification

Emitter classification plays a crucial role in electronic support measurement systems. A data-driven model named Gaussian dynamic recurrent unit is proposed to accomplish the end-to-end emitter classification tasks, which is simplified based on the long short-term memory unit by using a Gaussian function to characterize the relationship between the input and the network state, and a learnable exponentially weighted average to update the states. The Gaussian function and the dynamic exponentially weighted average amplify the difference between the input signals from different classes. The parameter size and computational time of many emitter classification methods are measured. The results show that the model proposed in this paper has the highest parameter efficiency, and low computational and storage costs. The results on a real-world dataset show that the proposed model has the highest accuracy and stability. These advantages have important significance for deploying the model in practical applications.

Fractional ordering of activation functions for neural networks: A case study on Texas wind turbine

Activation functions play an important role in deep learning models by introducing non-linearity to the output of a neuron, enabling the network to learn complex patterns and non-linear relationships in data and make predictions on more complex tasks. Deep learning models’ most commonly used activation functions are Purelin, Sigmoid, Tansig, Rectified Linear Unit (ReLU), and Exponential Linear Unit (ELU), which exhibit limitations such as non-differentiability, vanishing gradients, and neuron inactivity with negative values. These functions are typically defined over a finite range, and their outputs are integers or real numbers. Using fractional calculus in designing activation functions for neural networks has shown promise in improving the performance of deep learning models in specific applications. These activation functions can capture more complex non-linearities than traditional integer-order activation functions, improving performance on tasks such as image classification and time series prediction. This paper focuses on deriving and testing linear and non-linear fractional-order forms of activation functions and their variants. The linear activation function includes Purelin. In contrast, the non-linear activation functions are Binary Step, Sigmoid, Tansig, ReLU, ELU, Gaussian Error Linear Unit (GELU), Hexpo, and their variants. Besides, the standard formula has been implemented and used in developing the fractional-order linear activation function. Furthermore, various expansion series, such as Euler and Maclaurin, have been used to design non-linear fractional-order activation functions and their variants. The single- and multi-layer fractional-order neural network models have been developed using the designed fractional-order activation functions. The simulation study uses developed fractional-order neural network models for predicting the Texas wind turbine systems’ generated power. The performance of single and multi-layer fractional-order neural network models has been evaluated by changing the activation functions in the hidden layer while keeping the Purelin function constant at the output layer. Experiments on neural network models demonstrate that the designed fractional-order activation functions outperform traditional functions like Sigmoid, Tansig, ReLU, ELU, and their variants, effectively addressing limitations.

Magnetic properties of Al3+ doped Fe3O4 by solid-solid reaction and biological applications of GQDs grafted MNPs

2.79 % Al3+ doped Fe3O4 magnetic nanoparticles (Al-Fe3O4 MNPs) was prepared by a solid–solid reaction between aluminum formic acid hydrazide and as-prepared Fe3O4 magnetic nanoparticles. The obtained Al-Fe3O4 MNPs were strongly ferromagnetic, and their greatest saturation magnetization value was 82.3 electromagnetic units per gram (emu/g). The Al-Fe3O4 MNPs were subsequently coated with SiO2. High-resolution transmission electron microscopy (HRTEM), X-ray powder diffraction (XRD), Mössbauer spectroscopy, and vibrating-sample magnetometry (VSM) were used to characterize the morphology, structure, magnetic properties, and doping mechanism of the doped magnetic particles, respectively. Then graphene quantum dot (GQD) is loaded on the surface of aminated Al-Fe3O4@SiO2 (AFS) MNPs by a covalent connection, and Al-Fe3O4@SiO2-GQDs (AFSG) MNPs. Furthermore, AFSG MNPs were labeled with the metastable nuclear isomer of technetium-99 (99mTc) and their in vivo targeting performance was investigated. The AFSG-99mTc MNPs demonstrated a relatively strong magnetic response in a pulsed magnetic gradient field. The data obtained from the control and experimental groups indicated that the ability to uptake radioactivity of the livers of New Zealand rabbits via the magnetic targeted intervention was 3.69 times higher than that without the magnetic targeted intervention.

Boosting output performance of tri-hybrid vibration-based generator via quin-stable nonlinearity and speed amplification

A high-performance tri-hybrid vibration-driven generator with quin-stability and speed amplification characteristics is presented. This design integrates three different mechanisms in a compact and interactive working mode to enhance power density. Exploiting the quin-stable nonlinear behavior to the dynamic system creates a shallow potential well that can be triggered under broadband, low-level, and random ambient sources. In addition, the implementation of the rack and pinion steering system not only doubles the relative motion of the translators’ moving components in the electromagnetic and triboelectric units, and also increases the number of operations per cycle of the frequency up-conversion piezoelectric units. Consequently, the output performance of this tri-hybrid generator is significantly enhanced. To examine the working efficiency of this design, an experimental model is assembled and tested. In the shaker test, the peak output power of the prototype can be boosted to ∼822 mW, and the maximum power density is 7.74 mW cm−3 g−2 subjected to low excitation levels (5 Hz and 1-g acceleration). Experimental demonstrations are also carried out to exhibit the exceptional performance of this design that can support various power levels of electronic devices under human-induced motions.

Camellia oleifera Fruit Detection Algorithm in Natural Environment Based on Lightweight Convolutional Neural Network

At present, Camellia oleifera fruit harvesting relies on manual labor with low efficiency, while mechanized harvesting could result in bud damage because flowering and fruiting are synchronized. As a prerequisite, rapid detection and identification are urgently needed for high accuracy and efficiency with simple models to realize selective and intelligent harvesting. In this paper, a lightweight detection algorithm YOLOv5s-Camellia based on YOLOv5s is proposed. First, the network unit of the lightweight network ShuffleNetv2 was used to reconstruct the backbone network, and thereby the number of computations and parameters of the model was reduced to increase the running speed for saving computational costs. Second, to mitigate the impact of the lightweight improvement on model detection accuracy, three efficient channel attention (ECA) modules were introduced into the backbone network to enhance the network’s attention to fruit features, and the Concat operation in the neck network was replaced by the Add operation with fewer parameters, which could increase the amount of information under features while maintaining the same number of channels. Third, the Gaussian Error Linear Units (GELU) activation function was introduced to improve the nonlinear characterization ability of the network. In addition, to improve the ability of the network to locate objects in the natural environment, the penalty index was redefined to optimize the bounding box loss function, which can improve the convergence speed and regression accuracy. Furthermore, the final experimental results showed that this model possesses 98.8% accuracy, 5.5 G FLOPs computation, and 6.3 MB size, and the detection speed reached 60.98 frame/s. Compared with the original algorithm, the calculation amount, size, and parameters were reduced by 65.18%, 56.55%, and 57.59%, respectively. The results can provide a technical reference for the development of a Camellia oleifera fruit-harvesting robot.

Design and Implementation of a Novel Radar Cross Section Reduction Metasurface Covering 12.5-17.4 GHz and 20.5-36.2 GHz

Based on the theory of polarization conversion, this paper designs and implements two electromagnetic units with a 180-degree reflection phase difference in the range of 10-40 GHz. By coding and optimizing the spatial arrangement of the two basic units to obtain a metasurface, which makes the incident electromagnetic waves reflect diffusely, the energy in a single beam direction is reduced to realize the reduction of the radar cross section (RCS). The full wave simulated and experimental results of the reduction of the RCS of the metasurface have been presented. The RCS of the proposed metasurface can be reduced by more than 10 dB in the two broadband bands of 12.5-17.4 GHz and 20.5-36.2 GHz, and the maximum reduction can reach 26 dB, compared with a metallic surface of the same size. In addition, the optimized metasurface can also maintain a good reduction effect under oblique incident wave directions of 30, 45, 60, and 70 degrees. A very good agreement between simulation and measurement results is observed.

Magellan/M2FS and MMT/Hectochelle Spectroscopy of Dwarf Galaxies and Faint Star Clusters within the Galactic Halo* * This paper presents data gathered with the Magellan Telescopes at Las Campanas Observatory, Chile, and the MMT Observatory, a joint facility of the Smithsonian Institution, and the University of Arizona.

We present spectroscopic data for 16,369 stellar targets within and/or toward 38 dwarf spheroidal galaxies and faint star clusters within the Milky Way halo environment. All spectra come from observations with the multiobject, fiber-fed echelle spectrographs M2FS at the Magellan/Clay telescope or Hectochelle at the MMT, reaching a typical limiting magnitude G ≲ 21. Data products include processed spectra from all observations and catalogs listing estimates—derived from template model fitting—of line-of-sight velocity (median uncertainty 1.4 km s−1) effective temperature (255 K), (base-10 logarithm of) surface gravity (0.59 dex in cgs units), [Fe/H] (0.4 dex) and [Mg/Fe] (0.27 dex) abundance ratios. The sample contains multiepoch measurements for 3720 sources, with up to 15 epochs per source, enabling studies of intrinsic spectroscopic variability. The sample contains 6087 likely red giant stars (based on surface gravity), and 4492 likely members (based on line-of-sight velocity and Gaia-measured proper motion) of the target systems. The number of member stars per individual target system ranges from a few, for the faintest systems, to ∼850 for the most luminous. For most systems, our new samples extend over wider fields than have previously been observed; of the likely members in our samples, 820 lie beyond 2 times the projected half-light radius of their host system, and 42 lie beyond 5 R half.

Intercomparison of Brown Dwarf Model Grids and Atmospheric Retrieval Using Machine Learning

Understanding differences between substellar spectral data and models has proven to be a major challenge, especially for self-consistent model grids that are necessary for a thorough investigation of brown dwarf atmospheres. Using the random forest supervised machine-learning method, we study the information content of 14 previously published model grids of brown dwarfs (from 1997 to 2021). The random forest method allows us to analyze the predictive power of these model grids, as well as interpret data within the framework of Approximate Bayesian Computation. Our curated data set includes three benchmark brown dwarfs (Gl 570D, ϵ Indi Ba, and Bb) as well as a sample of 19 L and T dwarfs; this sample was previously analyzed by Lueber et al. using traditional Bayesian methods (nested sampling). We find that the effective temperature of a brown dwarf can be robustly predicted independent of the model grid chosen for the interpretation. However, inference of the surface gravity is model-dependent. Specifically, the BT-Settl, Sonora Bobcat, and Sonora Cholla model grids tend to predict –4 (cgs units) even after data blueward of 1.2 μm have been disregarded to mitigate for our incomplete knowledge of the shapes of alkali lines. Two major, longstanding challenges associated with understanding the influence of clouds in brown dwarf atmospheres remain: our inability to model them from first principles and also to robustly validate these models.

Mathematical Analysis and Performance Evaluation of the GELU Activation Function in Deep Learning

Selecting the most suitable activation function is a critical factor in the effectiveness of deep learning models, as it influences their learning capacity, stability, and computational efficiency. In recent years, the Gaussian error linear unit (GELU) activation function has emerged as a dominant method, surpassing traditional functions such as the rectified linear unit (ReLU) in various applications. This study presents a rigorous mathematical investigation of the GELU activation function, exploring its differentiability, boundedness, stationarity, and smoothness properties in detail. In addition, we conduct an extensive experimental comparison of the GELU function against a broad range of alternative activation functions, utilizing a residual convolutional network trained on the CIFAR-10, CIFAR-100, and STL-10 datasets as the empirical testbed. Our results demonstrate the superior performance of GELU compared to other activation functions, establishing its suitability for a wide range of deep learning applications. This comprehensive study contributes to a more profound understanding of the underlying mathematical properties of GELU and provides valuable insights for practitioners aiming to select activation functions that optimally align with their specific objectives and constraints in deep learning.

Δ-Learning applied to coarse-grained homogeneous liquids.

Coarse-grained molecular dynamics (CGMD) simulations address lengthscales and timescales that are critical to many chemical and material applications. Nevertheless, contemporary CGMD modeling is relatively bespoke and there are no black-box CGMD methodologies available that could play a comparable role in discovery applications that density functional theory plays for electronic structure. This gap might be filled by machine learning (ML)-based CGMD potentials that simplify model development, but these methods are still in their early stages and have yet to demonstrate a significant advantage over existing physics-based CGMD methods. Here, we explore the potential of Δ-learning models to leverage the advantages of these two approaches. This is implemented by using ML-based potentials to learn the difference between the target CGMD variable and the predictions of physics-based potentials. The Δ-models are benchmarked against the baseline models in reproducing on-target and off-target atomistic properties as a function of CG resolution, mapping operator, and system topology. The Δ-models outperform the reference ML-only CGMD models in nearly all scenarios. In several cases, the ML-only models manage to minimize training errors while still producing qualitatively incorrect dynamics, which is corrected by the Δ-models. Given their negligible added cost, Δ-models provide essentially free gains over their ML-only counterparts. Nevertheless, an unexpected finding is that neither the Δ-learning models nor the ML-only models significantly outperform the elementary pairwise models in reproducing atomistic properties. This fundamental failure is attributed to the relatively large irreducible force errors associated with coarse-graining that produces little benefit from using more complex potentials.

On Training Targets and Activation Functions for Deep Representation Learning in Text-Dependent Speaker Verification

Deep representation learning has gained significant momentum in advancing text-dependent speaker verification (TD-SV) systems. When designing deep neural networks (DNN) for extracting bottleneck (BN) features, the key considerations include training targets, activation functions, and loss functions. In this paper, we systematically study the impact of these choices on the performance of TD-SV. For training targets, we consider speaker identity, time-contrastive learning (TCL), and auto-regressive prediction coding, with the first being supervised and the last two being self-supervised. Furthermore, we study a range of loss functions when speaker identity is used as the training target. With regard to activation functions, we study the widely used sigmoid function, rectified linear unit (ReLU), and Gaussian error linear unit (GELU). We experimentally show that GELU is able to reduce the error rates of TD-SV significantly compared to sigmoid, irrespective of the training target. Among the three training targets, TCL performs the best. Among the various loss functions, cross-entropy, joint-softmax, and focal loss functions outperform the others. Finally, the score-level fusion of different systems is also able to reduce the error rates. To evaluate the representation learning methods, experiments are conducted on the RedDots 2016 challenge database consisting of short utterances for TD-SV systems based on classic Gaussian mixture model-universal background model (GMM-UBM) and i-vector methods.

Improved SinGAN for Single-Sample Airport Runway Destruction Image Generation

Aims: To solve the problem of difficult acquisition of airport runway destruction image data. Objectives: This paper introduces SinGAN, a single-sample generative adversarial network algorithm. Methods: To address the shortcomings of SinGAN in image realism and diversity generation, an improved algorithm based on the combination of Gaussian error linear unit GELU and efficient channel attention mechanism ECANet is proposed Results: Experiments show that its generated image results are subjectively better than SinGAN and its lightweight algorithm ConSinGAN, and the model can obtain an effective balance in both quality and diversity of image generation. Conclusion: The algorithm effect is also verified using three objective evaluation metrics, and the results show that the method in this paper effectively improves the generation effect compared with SinGAN, in which the SIFID metric is reduced by 46.67%.

Ultra-Wideband and High-Gain Vivaldi Antenna with Artificial Electromagnetic Materials.

An ultra-wideband and high-gain Vivaldi antenna with artificial electromagnetic material, suitable for ground-penetrating radar (GPR) systems, is proposed. Directors loaded inside the antenna gradient slot direct electromagnetic waves by inducing current to improve gain. The artificial electromagnetic material, also called metamaterial, is composed of multiple "H"-shaped units arranged in a certain regular pattern, loaded at the antenna aperture. The artificial electromagnetic units affect the antenna radiation waves by changing the refractive index to improve radiation directivity. The four Vivaldi units are arranged into a horn-shaped array, and each two units are orthogonally fed to realize dual polarization. Experimental results demonstrate that the antenna has good impedance matching of S11≤-10 dB in 0.9-4 GHz, and the maximum realized gain can reach 15.2 dBi.

Technical note: Multi-MATE, a high-throughput platform for automated image-guidedsmall-animal irradiation.

Small animal irradiation is essential to study the radiation response of new interventions before or parallel to human therapy. Image-guided radiotherapy (IGRT) and intensity-modulated radiotherapy (IMRT) are recently adopted in small animal irradiation to more closely mimic human treatments. However, sophisticated techniques require exceedingly high time, resources, and expertize that are often impractical. We propose a high throughput and high precision platform named Multiple Mouse Automated Treatment Environment (Multi-MATE) to streamline image-guided small animal irradiation. Multi-MATE consists of six parallel and hexagonally arranged channels, each equipped with a transfer railing, a 3D-printed immobilization pod, and an electromagnetic control unit, computer-controlled via an Arduino interface. The mouse immobilization pods are transferred along the railings between the home position outside the radiation field and the imaging/irradiation position at the irradiator isocenter. All six immobilization pods are transferred to the isocenter in the proposed workflow for parallel CBCT scans and treatment planning. The immobilization pods are then sequentially transported to the imaging/therapy position for dose delivery. The positioning reproducibility of Multi-MATE are evaluated using CBCT and radiochromic films. While parallelizing and automating the image-guided small animal radiation delivery, Multi-MATE achieved the average pod position reproducibility of 0.17 ± 0.04 mm in the superior-inferior direction, 0.20 ± 0.04 mm in the left-right direction, and 0.12 ± 0.02mm in the anterior-posterior direction in repeated CBCT tests. Additionally, in image-guided dose delivery tasks, Multi-MATE demonstrated the positioning reproducibility of 0.17 ± 0.06 mm in the superior-inferior direction, 0.19 ± 0.06 mm in the left-right direction. We designed, fabricated, and tested a novel automated irradiation platform, Multi-MATE to accelerate and automate image-guided small animal irradiation. The automated platform minimizes human operation and achieves high setup reproducibility and image-guided dose delivery accuracy. Multi-MATE thus removes a major barrier to implementing high-precision preclinical radiation research.