Dynamic Modulation Research Articles

A Novel Fusion Perception Algorithm of Tree Branch/Trunk and Apple for Harvesting Robot Based on Improved YOLOv8s

Aiming to accurately identify apple targets and achieve segmentation and the extraction of branch and trunk areas of apple trees, providing visual guidance for a picking robot to actively adjust its posture to avoid branch trunks for obstacle avoidance fruit picking, the spindle-shaped fruit trees, which are widely planted in standard modern apple orchards, were focused on, and an algorithm for apple tree fruit detection and branch segmentation for picking robots was proposed based on an improved YOLOv8s model design. Firstly, image data of spindle-shaped fruit trees in modern apple orchards were collected, and annotations of object detection and pixel-level segmentation were conducted on the data. Training set data were then augmented to improve the generalization performance of the apple detection and branch segmentation algorithm. Secondly, the original YOLOv8s network architecture’s design was improved by embedding the SE module visual attention mechanism after the C2f module of the YOLOv8s Backbone network architecture. Finally, the dynamic snake convolution module was embedded into the Neck structure of the YOLOv8s network architecture to better extract feature information of different apple targets and tree branches. The experimental results showed that the proposed improved algorithm can effectively recognize apple targets in images and segment tree branches and trunks. For apple recognition, the precision was 99.6%, the recall was 96.8%, and the mAP value was 98.3%. The mAP value for branch and trunk segmentation was 81.6%. The proposed improved YOLOv8s algorithm design was compared with the original YOLOv8s, YOLOv8n, and YOLOv5s algorithms for the recognition of apple targets and segmentation of tree branches and trunks on test set images. The experimental results showed that compared with the other three algorithms, the proposed algorithm increased the mAP for apple recognition by 1.5%, 2.3%, and 6%, respectively. The mAP for tree branch and trunk segmentation was increased by 3.7%, 15.4%, and 24.4%, respectively. The proposed detection and segmentation algorithm for apple tree fruits, branches, and trunks is of great significance for ensuring the success rate of robot harvesting, which can provide technical support for the development of an intelligent apple harvesting robot.

A New Method for Non-Destructive Identification and Tracking of Multi-Object Behaviors in Beef Cattle Based on Deep Learning.

The method proposed in this paper provides theoretical and practical support for the intelligent recognition and management of beef cattle. Accurate identification and tracking of beef cattle behaviors are essential components of beef cattle production management. Traditional beef cattle identification and tracking methods are time-consuming and labor-intensive, which hinders precise cattle farming. This paper utilizes deep learning algorithms to achieve the identification and tracking of multi-object behaviors in beef cattle, as follows: (1) The beef cattle behavior detection module is based on the YOLOv8n algorithm. Initially, a dynamic snake convolution module is introduced to enhance the ability to extract key features of beef cattle behaviors and expand the model's receptive field. Subsequently, the BiFormer attention mechanism is incorporated to integrate high-level and low-level feature information, dynamically and sparsely learning the behavioral features of beef cattle. The improved YOLOv8n_BiF_DSC algorithm achieves an identification accuracy of 93.6% for nine behaviors, including standing, lying, mounting, fighting, licking, eating, drinking, working, and searching, with average 50 and 50:95 precisions of 96.5% and 71.5%, showing an improvement of 5.3%, 5.2%, and 7.1% over the original YOLOv8n. (2) The beef cattle multi-object tracking module is based on the Deep SORT algorithm. Initially, the detector is replaced with YOLOv8n_BiF_DSC to enhance detection accuracy. Subsequently, the re-identification network model is switched to ResNet18 to enhance the tracking algorithm's capability to gather appearance information. Finally, the trajectory generation and matching process of the Deep SORT algorithm is optimized with secondary IOU matching to reduce ID mismatching errors during tracking. Experimentation with five different complexity levels of test video sequences shows improvements in IDF1, IDS, MOTA, and MOTP, among other metrics, with IDS reduced by 65.8% and MOTA increased by 2%. These enhancements address issues of tracking omission and misidentification in sparse and long-range dense environments, thereby facilitating better tracking of group-raised beef cattle and laying a foundation for intelligent detection and tracking in beef cattle farming.

Seed to Prune: A Seeded Graph Neural Network for Two-View Correspondence Learning.

We present a simple yet tough-to-beat method dubbed SGNNet, for correspondence learning. Instead of focusing on devising sophisticated geometric extractors to explore the global or local contextual information involving all sparse correspondences as most existing studies have done, which may be biased by heavy outliers, we propose to first delve into elaborate contextual information encoded in several specific reliable correspondences, and later leverage it to achieve per-correspondence representation updating. To this end, the proposed network contains three pivotal modules: 1) dynamic seeding module, which aims to dynamically sample a set of reliable matches from the putative set as seeds to guide the network learning; 2) intraseed attention module (ISAM), which intends to capture the geometrical relations among seed matches and further leverage them to enhance seed features; and 3) dynamic unseeding module, which is designed to sufficiently aggregate favorable contextual information from seed matches and broadcast it back to features of original matches. With all the aforementioned components, the proposed SGNNet is capable of rejecting outliers from putative correspondences effectively. Extensive experiments indicate that our method beats current solid baselines and sets new SOTA scores across multiple domains and datasets. Notably, SGNNet attains an AUC@ 5° of 56.43% on YFCC100M without RANSAC, surpassing the most cutting-edge model by 4.51 absolute percentage points and exceeding the 55% AUC@ 5° bar for the first time. Project page: https://github.com/ZizhuoLi/SGNNet.

Understanding wave energy converters dynamics: High-fidelity modeling and validation of a moored floating body

In ocean engineering, one of the most challenging phenomena to replicate is the interaction between waves and a moored floating body. Accurately evaluating such systems is essential for minimizing uncertainties, mitigating risks, and advancing technologies like wave energy converters. For this purpose, this study aims to develop a high-fidelity numerical model capable of reducing reliance on costly experimental campaigns during the device design phases. The model integrates a fluid dynamics module based on the Navier–Stokes equations, implemented in Star CCm+, with a mooring model utilizing a mass–spring–damper system, specifically MoorDyn. The primary objective is to introduce this coupled approach and demonstrate its efficacy through validation against experimental data from two distinct wave energy converters: ISWEC and PeWEC. The validation process encompasses comprehensive comparisons between simulated and observed kinematic behavior, mooring tensions, and crucially, pressure loads. Results indicate that the model is able to faithfully predict the complex phenomena involving a moored floating body, affirming the accuracy and reliability of the proposed coupling methodology. The coupling developed is available online link, where a simplified case study is present to show how to compile the library and add it in the numerical model.

Diffusion-based diverse audio captioning with retrieval-guided Langevin dynamics

Audio captioning, a comprehensive task of audio understanding, aims to provide a natural-language description of an audio clip. Beyond accuracy, diversity is also a critical requirement for this task. Human-produced captions possess rich variability due to the ambiguity of audio semantics (such as insects buzzing and electrical humming making similar sounds) and the existence of subjective judgments (metaphor, affections, etc.). However, current diverse audio captioning systems fail to produce captions with near-human diversity. Recently, diffusion models have demonstrated the potential to generate data with diversity while maintaining decent accuracy, yet they have not been explored in audio captioning. On the other hand, diffusion models tend to have low generation accuracy and fluency on text data. Directly applying diffusion models to audio captioning tasks may aggravate this problem due to the small size of annotated datasets and the mutable supervision target caused by the variability in human-produced captions. In this work, we propose a diffusion-based diverse audio captioning model by incorporating the BART language model into the diffusion model for better utilization of the pre-trained linguistic knowledge. We also propose a retrieval-guided Langevin dynamics module, which enables dynamic run-time alignment between generated captions and the target audio. Extensive experiments on standard audio captioning benchmark datasets (Clotho and AudioCaps) demonstrate that our model can achieve better performance on both accuracy and diversity metrics compared with state-of-the-art diverse audio captioning systems. The implementation is available at https://github.com/AI4MyBUPT/DACRLD.

Fluorescent, multifunctional anti-counterfeiting, fast response electrophoretic display based on TiO2/CsPbBr3 composite particles

Traditional optical anti-counterfeiting (AC) is achieved by static printed images, which makes them susceptible to lower levels of security and easier replication. Therefore, it is essential to develop AC device with dynamic modulation for higher security. Electrophoretic display (EPD) has the advantages of low power consumption, high ambient contrast ratio, and capability of showing dynamic images which is suitable for dynamic AC applications. Herein, we prepared a dynamical AC device based on a fluorescent EPD, and achieving the image switch between black, white, and green fluorescence states under the dual-mode driving (electronic field and UV light). We loaded perovskite quantum dots (CsPbBr3) onto the TiO2 particles and further prepared fluorescent electrophoretic particles TiO2/CsPbBr3-3-PLMA (TiO/CPB-3) by grafting and polymerizing method. In addition, we fabricated the AC devices based on the fluorescent EPD, which exhibits the multifunctional AC, where the fluorescent EPD has a fast response time of 350 ms, a high contrast ratio of 17, and bright green fluorescence. This prototype demonstrates a new way for future dynamic AC and identification.

A Multi-Scale Mask Convolution-Based Blind-Spot Network for Hyperspectral Anomaly Detection

Existing methods of hyperspectral anomaly detection still face several challenges: (1) Due to the limitations of self-supervision, avoiding the identity mapping of anomalies remains difficult; (2) the ineffective interaction between spatial and spectral features leads to the insufficient utilization of spatial information; and (3) current methods are not adaptable to the detection of multi-scale anomaly targets. To address the aforementioned challenges, we proposed a blind-spot network based on multi-scale blind-spot convolution for HAD. The multi-scale mask convolution module is employed to adapt to diverse scales of anomaly targets, while the dynamic fusion module is introduced to integrate the advantages of mask convolutions at different scales. The proposed approach includes a spatial–spectral joint module and a background feature attention mechanism to enhance the interaction between spatial–spectral features, with a specific emphasis on highlighting the significance of background features within the network. Furthermore, we propose a preprocessing technique that combines pixel shuffle down-sampling (PD) with spatial spectral joint screening. This approach addresses anomalous identity mapping and enables finite-scale mask convolution for better detection of targets at various scales. The proposed approach was assessed on four real hyperspectral datasets comprising anomaly targets of different scales. The experimental results demonstrate the effectiveness and superior performance of the proposed methodology compared with nine state-of-the-art methods.

MsFNet: Multi-Scale Fusion Network Based on Dynamic Spectral Features for Multi-Temporal Hyperspectral Image Change Detection

With the development of satellite technology, the importance of multi-temporal remote sensing (RS) image change detection (CD) in urban planning, environmental monitoring, and other fields is increasingly prominent. Deep learning techniques enable a profound exploration of the intrinsic features within hyperspectral (HS) data, leading to substantial enhancements in CD accuracy while addressing several challenges posed by traditional methodologies. However, existing convolutional neural network (CNN)-based CD approaches frequently encounter issues during the feature extraction process, such as the loss of detailed information due to downsampling, which hampers a model’s ability to accurately capture complex spectral features. Additionally, these methods often neglect the integration of multi-scale information, resulting in suboptimal local feature extraction and, consequently, diminished model performance. To address these limitations, we propose a multi-scale fusion network (MsFNet) which leverages dynamic spectral features for effective multi-temporal HS-CD. Our approach incorporates a dynamic convolution module with spectral attention, which adaptively modulates the receptive field size according to the spectral characteristics of different bands. This flexibility enhances the model’s capacity to focus on critical bands, thereby improving its ability to identify and differentiate changes across spectral dimensions. Furthermore, we develop a multi-scale feature fusion module which extracts and integrates features from deep feature maps, enriching local information and augmenting the model’s sensitivity to local variations. Experimental evaluations conducted on three real-world HS-CD datasets demonstrate that the proposed MsFNet significantly outperforms contemporary advanced CD methods in terms of both efficacy and performance.

Single CsPbBr3 Perovskite Microcrystals: From Microcubes to Microrods with Improved Crystallinity and Green Emission.

All-inorganic perovskite materials are promising in optoelectronics, but their morphology is a key parameter for achieving high device efficiency. We prepared CsPbBr3 perovskite microcrystals with different shapes grown directly on planar substrate by conventional drop casting. We observed the formation of CsPbBr3 microcubes on bare indium tin oxide (ITO)-coated glass. Interestingly, with the same technique, CsPbBr3 microrods were obtained on (3-Aminopropyl) triethoxysilane (APTES)-modified ITO-glass, which we ascribe to the modification of formation kinetics. The obtained microcrystals exhibit an orthorhombic structure. A green photoluminescence (PL) emission is revealed from the CsPbBr3 microrods. Contact angle measurements, Fourier-transform infrared and PL spectroscopies confirmed that APTES linked successfully to the ITO-glass substrate. We propose a qualitative mechanism to explain the anisotropic growth. The microrods exhibited improved PL and a slower PL lifetime compared to the microcubes, likely due to the diminished occurrence of defects. This work demonstrates the importance of the substrate surface to control the growth of perovskite single crystals and to boost the radiative recombination in view of high-performance optoelectronic devices.

EDASNet: efficient dynamic adaptive-scale network for infrared pedestrian detection

Abstract Infrared images are widely utilized due to their exceptional anti-interference capabilities. However, challenges such as low resolution and an absence of detailed texture can impede the effective recognition of multi-scale target information, particularly for small targets. To address these issues, we introduce a multi-scale detection framework named efficient dynamic adaptive-scale network (EDASNet), which focuses on enhancing the feature extraction of small objects while ensuring efficient detection of multi-scale. Firstly, we design a lightweight dynamic enhance network as the backbone for feature extraction. It mainly includes a lightweight adaptive-weight downsampling module and a dynamic enhancement convolution module. In addition, a multi-scale aggregation feature pyramid network is proposed, which improves the perception effect of small objects through a multi-scale convolution module. Then, the Repulsion Loss term was introduced based on CIOU to effectively solve the missed detection problem caused by target overlap. Finally, the dynamic head was used as the network detection head, and through the superposition of dynamic convolution and multiple attention, the network was able to accurately realize multi-scale object detection. Comprehensive experiments show that EDASNet outperforms existing efficient models and achieves a good trade-off between speed and accuracy.

Simulation of deflection and photon emission of ultra-relativistic electrons and positrons in a quasi-mosaic bent silicon crystal

Abstract A comprehensive numerical investigation has been conducted on the angular distribution and spectrum of radiation emitted by 855 MeV electron and positron beams while traversing a ‘quasi-mosaic’ bent silicon (111) crystal. This interaction of charged particles with a bent crystal gives rise to various phenomena such as channeling, dechanneling, volume reflection, and volume capture. The crystal’s geometry, emittance of the collimated particle beams, as well as their alignment with respect to the crystal, have been taken into account as they are essential for an accurate quantitative description of the processes. The simulations have been performed using a specialized relativistic molecular dynamics module implemented in the MBN Explorer package. The angular distribution of the particles after traversing the crystal has been calculated for beams of different emittances as well as for different anticlastic curvatures of the bent crystals. For the electron beam, the angular distributions of the deflected particles and the spectrum of radiation obtained in the simulations are compared with the experimental data collected at the Mainz Microtron facility. For the positron beam such calculations have been performed for the first time. We predict significant differences in the angular distributions and the radiation spectra for positrons versus electrons.

Monolithically integrated mode converter from terahertz substrateless silicon guide to conductive slotline

Although substrateless micro-scale silicon waveguides are a useful and versatile platform for terahertz waves, the fact that modal fields occupy the volume of the core limits the potential to dynamically manipulate guided waves. To address this, we introduce an aperiodic lattice structure to enable the monolithic co-integration of a Vivaldi antenna-like mode converter with a substrateless silicon waveguide. This broadband transition is experimentally confirmed to exhibit ∼2.5 dB average loss for two couplers, from 220 GHz to 330 GHz, and enables a photoexcited variable attenuator as proof-of-concept demonstration. This is an important enabling step to incorporate general-purpose dynamic reconfigurability, sensing, and modulation functionality into terahertz-range silicon-based integrated circuits, which are currently limited to primarily all-passive structures.

INA-Net: An integrated noise-adaptive attention neural network for enhanced medical image segmentation

Skin cancer, a growing health concern, poses a significant threat. Automated segmentation of skin lesions is crucial for precise clinical diagnosis and treatment. However, challenges such as hair interference, vascular occlusion, and ambiguous lesion boundaries in dermoscopic images complicate accurate segmentation. Recent research has focused on capturing multiscale-enhanced global information across spatial and channel dimensions to improve the segmentation of ambiguous boundaries. Despite these advancements, performance remains suboptimal in the presence of complex disturbances. To address this, we propose INA-Net, an Integrated Noise-Adaptive Attention Neural Network for enhanced medical image segmentation. INA-Net proposes a lightweight noise-range attention module that integrates local and simulated noise features with global information to improve robustness against complex mixed noises. Additionally, our proposed Edge-aware Spatial Attention (ESA) and Multi-scale Channel Attention (MCA) enhance feature information and boundary perception. The proposed Dynamic Noise Encoding module and Fourier Wavelet Analysis (FW-Parser) further improve robustness by handling high-frequency noise components. INA-Net effectively manages hair interference, vascular occlusion, and ambiguous lesion boundaries, thereby providing critical support for accurate lesion identification. Evaluations on the ISIC2016, ISIC2018, and PH2 datasets demonstrate outstanding performance, with IOU scores of 87.53%, 84.74%, and 85.30%, respectively. These results surpass those of other models with comparable complexity and size.

Palmitic acid-mediated modulation of crystallization dynamics in amylose microparticle formation: From spherical to macaron and disc shapes

Here, we investigated the complexation of short chain amylose (SCAs) and palmitic acid (PA), serving as polymeric building blocks that alter the selectivity and directionality of particle growth. This alteration affects the shape anisotropy of the particles, broadening their applications due to the increased surface area. By modifying the concentration of PA, we were able to make spherical, macaron, and disc-shaped particles, demonstrating that PA acts as a structure-directing agent. We further illustrated the lateral and longitudinal stacking kinetics between PA-SCA inclusion complexes during self-assembly, leading to anisotropy. Transmission electron microscope (TEM) and scanning electron microscope (SEM) revealed the structural difference between the initial and final morphologies of palmitic acid-short chain amylose particles (PA-SCAPs) compared to those of short-chain amylose particle (SCAPs). The presence of PA-SCA inclusion complex in the anisotropic particles was confirmed using nuclear magnetic resonance (NMR) and powder x-ray diffraction (XRD) analysis.

Enhanced cellular longevity arising from environmental fluctuations

Cellular longevity is regulated by both genetic and environmental factors. However, the interactions of these factors in the context of aging remain largely unclear. Here, we formulate a mathematical model for dynamic glucose modulation of a core gene circuit in yeast aging, which not only guided the design of pro-longevity interventions but also revealed the theoretical principles underlying these interventions. We introduce the dynamical systems theory to capture two general means for promoting longevity-the creation of a stable fixed point in the "healthy" state of the cell and the "dynamic stabilization" of the system around this healthy state through environmental oscillations. Guided by the model, we investigate how both of these can be experimentally realized by dynamically modulating environmental glucose levels. The results establish a paradigm for theoretically analyzing the trajectories and perturbations of aging that can be generalized to aging processes in diverse cell types and organisms.

Modulating Mitochondrial Dynamics Mitigates Cognitive Impairment in Rats with Myocardial Infarction.

We have previously demonstrated that oxidative stress and brain mitochondrial dysfunction are key mediators of brain pathology during myocardial infarction (MI). To investigate the beneficial effects of mitochondrial dynamic modulators, including mitochondrial fission inhibitor (Mdivi-1) and mitochondrial fusion promotor (M1), on cognitive function and molecular signaling in the brain of MI rats in comparison with the effect of enalapril. Male rats were assigned to either sham or MI operation. In the MI group, rats with an ejection Fraction less than 50% were included, and then they received one of the following treatments for 5 weeks: vehicle, enalapril, Mdivi-1, or M1. Cognitive function was tested, and the brains were used for molecular study. MI rats exhibited cardiac dysfunction with systemic oxidative stress. Cognitive impairment was found in MI rats, along with dendritic spine loss, blood-brain barrier (BBB) breakdown, brain mitochondrial dysfunction, and decreased mitochondrial and increased glycolysis metabolism, without the alteration of APP, BACE-1, Tau and p-Tau proteins. Treatment with Mdivi-1, M1, and enalapril equally improved cognitive function in MI rats. All treatments decreased dendritic spine loss, brain mitochondrial oxidative stress, and restored mitochondrial metabolism. Brain mitochondrial fusion was recovered only in the Mdivi-1-treated group. Mitochondrial dynamics modulators improved cognitive function in MI rats through a reduction of systemic oxidative stress and brain mitochondrial dysfunction and the enhancement of mitochondrial metabolism. In addition, this mitochondrial fission inhibitor increased mitochondrial fusion in MI rats.

Rationally reprogramming single-cell aging trajectories and lifespan through dynamic modulation of environmental inputs

How do variations in nutrient levels influence cellular lifespan? A dynamical systems model of a core circuit involved in yeast aging suggests principles underlying lifespan extension observed at static and alternating glucose levels that are reminiscent of intermittent fasting regimens.

Particle dispersion in atmospheric modelling: A comprehensive review

Atmospheric dispersion modeling, traditionally inclined towards gaseous dispersion, has undergone significant evolution in capturing the intricacies of particle dispersion in urban and open environments. This comprehensive review explores the nuances of particle-gas interactions, highlighting discrepancies in correlations between their concentrations, influenced by factors such as turbulence and multiple emission sources. The research accentuates the intriguing dynamics between PM2.5 and PM10 concentrations, suggesting the viability of models based on passive scalars for such particles in open environments. However, a marked challenge emerges in modeling particle number concentration, necessitating the integration of aerosol dynamics modules. Emphasizing the diversity of model types, this paper elucidates the specific requirements across varying spatial scales, identifying gaps in understanding particle dispersion and aerosol dynamics. The review critically assesses the performance of notable models, highlighting the paramount importance of quality data sources and underscoring the need for more dedicated focus on particle dynamics beyond mass predictions. Through a synthesis of existing literature and model evaluations, this review seeks to guide future research endeavors, fostering advancements in atmospheric dispersion modeling.

Robust and Deterministic Preparation of Bosonic Logical States in a Trapped Ion.

Encoding logical qubits in bosonic modes provides a potentially hardware-efficient implementation of fault-tolerant quantum information processing. Here, we demonstrate high-fidelity and deterministic preparation of highly nonclassical bosonic states in the mechanical motion of a trapped ion. Our approach implements error-suppressing pulses through optimized dynamical modulation of laser-driven spin-motion interactions to generate the target state in a single step. We demonstrate logical fidelities for the Gottesman-Kitaev-Preskill state as high as F[over ¯]=0.940(8), a distance-3 binomial state with an average fidelity of F=0.807(7), and a 12.91(5)dB squeezed vacuum state.

Empagliflozin impact on experimentally induced acetaminophen toxicity: Imprint of mitochondrial dynamics, biogenesis, and cGAS/STING signal in amending liver insult.

Acetaminophen (APAP) is one of the most clinically relevant medications associated with acute liver damage. A prolific deal of research validated the hepatoprotective effect of empagliflozin (EMPA); however, its effect on APAP-induced hepatotoxicity has still not been investigated. In this study, the prospective hepatoprotective impact of EMPA against APAP-induced hepatotoxicity was investigated. Twenty-eight Balb-C mice were assigned to four groups: control, APAP, EMPA10/APAP, and EMPA25/APAP. At the end of the experiment, serum hepatotoxicity biomarkers, MDA level, and GSH content were estimated. Hepatic mitofusin-2 (MFN2), optic atrophy 1 (OPA1), dynamin-related protein 1 (Drp1), and mitochondrial fission 1 protein (FIS1) were immunoassayed. PGC-1α, cGAS, and STING mRNA expression were assessed by real-time PCR. Histopathological changes and immunohistochemistry of INF-β, p-NF-κB, and iNOS were evaluated. APAP treatment caused significant hepatic functional impairment and increased hepatic MDA levels, as well as a concomitant decrease in GSH content. Marked elevation in Drp1 and FIS1 levels, INF-ß, p-NF-κB, and iNOS immunoreactivity, and reduction in MFN2 and OPA1 levels in the APAP-injected group, PGC-1α downregulation, and high expression of cGAS and STING were also documented. EMPA effectively ameliorated APAP-generated structural and functional changes in the liver, restored redox homeostasis and mitochondrial dynamics balance, and enhanced mitochondrial biogenesis, remarkably diminished hepatic expression of cGAS and STING, and elicited a reduction in hepatic inflammation. Moreover, the computational modeling data support the interaction of APAP with antioxidant system-related proteins as well as the interactions of EMPA against Drp1, cGAS, IKKA, and iNOS proteins. Our findings demonstrated for the first time that EMPA has an ameliorative impact against APAP-induced hepatotoxicity in mice via modulation of mitochondrial dynamics, biogenesis, and cGAS/STING-dependent inflammation. Thus, this study concluded that EMPA could be a promising therapeutic modality for acute liver toxicity.