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Collective Location Selection (CLS) has received significant research attention in the spatial database community due to its wide range of applications. The CLS problem selects a group of k preferred locations among candidate sites to establish facilities, aimed at collectively attracting the maximum number of users. Existing studies commonly assume every user is located in a fixed position, without considering the competition between peer facilities. Unfortunately, in real markets, users are mobile and choose to patronize from a host of competitors, making traditional techniques unavailable. To this end, this paper presents the first effort on a CLS problem in competition scenarios, called mc$^{2}$2ls, taking into account the mobility factor. Solving mc$^{2}$2ls is a non-trivial task due to its NP-hardness. To overcome the challenge of pruning multi-point users with highly overlapped minimum boundary rectangles (MBRs), we exploit a position count threshold and design two square-based pruning rules. We introduce IQuad-tree, a user-MBR-free index, to benefit the hierarchical and batch-wise properties of the pruning rules. We propose an $(1-\frac{1}{e})$(1-1e)-approximate greedy solution to mc$^{2}$2ls and incorporate a candidate-pruning strategy to further accelerate the computation for handling skewed datasets. Extensive experiments are conducted on real datasets, demonstrating the superiority of our proposed pruning rules and solution compared to the state-of-the-art techniques.

Deep learning-based low-light image enhancement (LLIE) is a task of leveraging deep neural networks to enhance the image illumination while keeping the image content unchanged. From the perspective of training data, existing methods complete the LLIE task driven by one of the following three data types: paired data, unpaired data and zero-reference data. Each type of these data-driven methods has its own advantages, e.g., zero-reference data-based methods have very low requirements on training data and can meet the human needs in many scenarios. In this paper, we leverage pure Gaussian noise to complete the LLIE task, which further reduces the requirements for training data in LLIE tasks and can be used as another alternative in practical use. Specifically, we propose Noise SElf-Regression (NoiSER) without access to any task-related data, simply learns a convolutional neural network equipped with an instance-normalization layer by taking a random noise image, $\mathcal {N}(0,\sigma ^{2})$N(0,σ2) for each pixel, as both input and output for each training pair, and then the low-light image is fed to the trained network for predicting the normal-light image. Technically, an intuitive explanation for its effectiveness is as follows: 1) the self-regression reconstructs the contrast between adjacent pixels of the input image, 2) the instance-normalization layer may naturally remediate the overall magnitude/lighting of the input image, and 3) the $\mathcal {N}(0,\sigma ^{2})$N(0,σ2) assumption for each pixel enforces the output image to follow the well-known gray-world hypothesis (Buchsbaum, 1980) when the image size is big enough. Compared to current state-of-the-art LLIE methods with access to different task-related data, NoiSER is highly competitive in enhancement quality, yet with a much smaller model size, and much lower training and inference cost. In addition, the experiments also demonstrate that NoiSER has great potential in overexposure suppression and joint processing with other restoration tasks.

This study presents an energy-efficient extractive distillation (ED) process for separating the quaternary azeotropic mixture of tetrahydrofuran (THF), Ethyl acetate (EtAC), Methanol (MeOH) from waste-effluent coming out from the production of betamethasone process. The methodology involved solvent screening, conceptual design and mechanism analysis using thermodynamic principles and quantum chemical calculations, and process optimization. Two different configurations were developed, i.e., a four column ED (FCED) as base case and its corresponding intensified process (thermally coupled ED (TCED)) and the performance were compared based on economic, environmental, and energy efficiency metrics. Altogether, the optimized TCED provides 4 % lower total annual cost (TAC) than FCED, primarily due to reduced capital costs. The TCED also demonstrated 5.3 % lower total energy consumption, though this was partly offset by increased operational costs due to higher steam grade requirements in column C3. The TCED process also reduced CO2emission by 5.3 % and improved thermodynamic efficiency by 13.8 %. Overall, while the TCED process incurs higher operational costs due to specific column requirements, its enhanced thermodynamic efficiency and lower CO2emission suggest it is a promising approach for separating the quaternary azeotropic mixture of THF/MeOH/EtAC /water.

Graphical Abstract: Schematic illustration of preparation, synergistic anti-obesity effect, safety and mechanism of action of PDA-Emodin-PVP-MN patches

• CSMA offers excellent electromagnetic stealth performance for a variety of complex applications. • The dual structures synergistically drive the formation of current vector vortices to enhance the loss of electromagnetic wave energy. • CSMA has an effective absorption bandwidth of 6.07–18 GHz and a thickness of 4 mm. The amount of absorbent used is half that of a slab coating of equivalent thickness. The performance breakthroughs of some stealth materials have benefited from incorporating biomimetic concepts, and the design ideas of wave-absorbing metamaterials have been greatly broadened. However, stealth materials developed based on a single biological structure still have limitations regarding overall performance and design freedom. Herein, a dual-structure element combination model with a butterfly-wing porous structure and moth-eye raised structure arranged in an orderly manner is established. Carbonyl iron and polyurethane are mixed as wave absorbents, and the model is utilized to make a biomimetic metamaterial (CSMA), which has an absorption rate of more than 90 % at 6.07–18 GHz, achieving broadband effective absorption. It has been verified that the two biostructures designed after an ordered arrangement show synergistic effects in the combined model, and the cooperation between the structures induces the formation of current vector vortices, which are able to induce microwave losses to broaden the effective absorbing bandwidth. Further, the model has the combined application performance of polarization insensitivity, strong stability of oblique incidence, and low bistatic RCS. Such a thought based on the combination of multiple components provides an effective strategy for the design of broadband-absorbing metamaterials. [Display omitted] [ABSTRACT FROM AUTHOR]

Waterborne polyurethane (WPU) with self-healing properties is highly desirable for sustainable development. However, balancing the mechanical and self-healing properties of WPU is challenging. In this study, ultrahigh mechanical and self-healing performance was simultaneously achieved by introducing dopamine-modified iron-oxide nanoparticles (Fe3O4@DA NPs) to a WPU matrix consisting of soft poly(tetramethylene ether) glycol segments and hard isoforone diisocyanate and dimethylolpropionic acid segments. Consequently, the tensile strength and toughness of the WPU/Fe3O4@DA composite increased to 63.6 MPa and 179.35 MJ·m–3, respectively; these values correspond to improvements of 276% and 403% over those of pure WPU (16.9 MPa and 44.50 MJ·m–3, respectively). The proposed composite also exhibited excellent self-healing ability at room temperature; that is, the stress and strain self-healing efficiencies after 6 h at 60 °C increased by 93.5% and 100%, respectively. This performance was achieved because the Fe3O4@DA NPs, which acted as reinforcing fillers, interacted strongly with the hard WPU segments via the covalent reaction of the DA amine groups with the WPU-matrix isocyanate groups, remarkably enhancing mechanical properties. Moreover, catechol–Fe(III) coordination bonds formed between the DA and Fe3O4NPs, which promoted relaxation behavior and, thus, enhanced the self-healing efficiency. The approach presented in this study constitutes an effective strategy for developing an efficient self-healing polymer with high mechanical strength, which can endow self-healing coatings and adhesives with long service life in severe environments.

• A novel high entropy pyrochlore ceramic (La 0.3 Gd 0.3 Ca 0.4) 2 (Ti 0.2 Zr 0.2 Hf 0.2 Nb 0.2 Ta 0.2) 2 O 7 with significant atomic radius and mass fluctuation is proposed for the first time. • Enhanced fracture toughness and amorphous-like low thermal conductivity was reported for the first time. • Close thermal expansion coefficient to Al 2 O 3 was demonstrated for the first time. • The influences of disorders in the atomic weight, ionic size and electronegativity of substitutional cations, and intrinsic oxygen vacancies on thermal conductivity reduction are investigated deeply. Low thermal conductivity and excellent mechanical strength are essential to pyrochlore A 2 B 2 O 7 ceramic for environmental/thermal barrier coating applications. To collaboratively tailor the mechanical and thermal properties of A 2 B 2 O 7 ceramic, a novel high entropy pyrochlore ceramic (La 0.3 Gd 0.3 Ca 0.4) 2 (Ti 0.2 Zr 0.2 Hf 0.2 Nb 0.2 Ta 0.2) 2 O 7 with significant atomic radius and mass fluctuation is proposed by simultaneously introducing various elements with different valence states at A and B cation sites. The as-synthesized (La 0.3 Gd 0.3 Ca 0.4) 2 (Ti 0.2 Zr 0.2 Hf 0.2 Nb 0.2 Ta 0.2) 2 O 7 exhibits enhanced fracture toughness (1.68 MPa m1/2), amorphous-like low thermal conductivity (1.45 W m–1 K–1 at 900 °C) and matched thermal expansion coefficient (9.0 × 10–6 K–1 at 1200 °C) with Al 2 O 3 /Al 2 O 3 CMCs. The extensive misfits in atomic weight, ionic radius among the substitutional cations in combination with the intrinsic oxygen vacancies in the anion sublattice play significant roles in the thermal conductivity reduction of (La 0.3 Gd 0.3 Ca 0.4) 2 (Ti 0.2 Zr 0.2 Hf 0.2 Nb 0.2 Ta 0.2) 2 O 7 ceramic. The combination of outstanding mechanical and thermal properties indicates that this type of material has a good application prospect for environmental/thermal barrier coatings. [Display omitted] [ABSTRACT FROM AUTHOR]

Feed spacers are essential components of any spiral wound membrane module, maintaining the integrity of feed channels and enhancing fluid turbulence. However, they increase channel pressure gradients and create local stagnation zones, thereby exacerbating membrane fouling. These issues result in additional costs in the water treatment process. Now to fully utilize the potential of membrane filtration technology, it is necessary to reduce the problem of membrane fouling through the development of improved membrane feed spacers. Previously an Arch-Hole (Spacer-4) configuration was introduced, and herein three new configurations with higher channel porosity and shear force than the original Spacer-4 design have been assessed alongside that design. The four innovative feed spacers were studied experimentally under conditions that generate biofouling. Channel porosity and shear stress play important roles in biofouling development. Spatial distribution of shear stress was obtained via computational fluid dynamic simulations. Findings suggest that achieving uniformity of the shear force and eliminating dead zones are more crucial than high shear stress values. Such conditions were achieved with Spacer-4 type which maintained superior flux and gave for the module itself a relative specific energy consumption of 0.26 kWh/m3, significantly better than the next best value at 0.43 kWh/m3.

In this study, three distinct brands of soybean oils with varying proportions of transgenic and non-transgenic were subjected to analysis. The fluorescence intensity (F) was obtained via a fluorescence spectrophotometer, while the absorption (µa) and reduced scattering coefficients (µ’s) were obtained by through a self-developed double integrating sphere (DIS) system. A quantitative detection method for the adulteration ratio based on fluorescence spectroscopy was proposed which considered the entangling effect of absorption and scattering. The method entails initially conducting principal component analysis (PCA) on the F, µaand µ’sspectra in the range of 350–700 nm, thereby obtaining the first five principal components (PCs) of each kind of spectrum were obtained. Furthermore, the three brands of oil exhibited a discernible clustering tendency when subjected to a three-dimensional PCA mapping approach. The distribution positions of the three spectra in the three plots indicated that they could be considered to complement each other. Following further normalization processing, the PCs were fused and quantitative models were calibrated by using multiple linear regression (MLR), partial least squares regression (PLSR), and support vector regression (SVR). The results indicated that, in comparison to the utilisation of individual spectral characteristics, the fusion of Fand µacan effectively mitigate the impact of fluorescence internal filtering, thereby improving the prediction accuracy. Furthermore, the combination of F, µaand µ’scan effectively eliminate the interference of scattering on fluorescence, and achieve optimal prediction results. Among them, the MLR model based on F, µaand µ’scould reach the best performance, with determination coefficients of calibration (R2c) and validation sets (R2v) reaching 0.959 and 0.947, respectively, while the root mean square error of calibration (RMSEC) and validation sets (RMSEV) were as low as 2.970% and 3.429%, respectively. In comparison, the MLR model based solely on Fyielded unsatisfactory results, with R2cand R2vwere 0.571 and 0.595. It can be concluded that it can greatly improve the accuracy of predicting the adulteration of transgenic in non-transgenic soybean oil by integrating F, µaand µ’sspectroscopy.

Cloth-changing person re-identification (ReID) is a newly emerging research topic that aims to retrieve pedestrians whose clothes are changed. Since the human appearance with different clothes exhibits large variations, it is very difficult for existing approaches to extract discriminative and robust feature representations. Current works mainly focus on body shape or contour sketches, but the human semantic information and the potential consistency of pedestrian features before and after changing clothes are not fully explored or are ignored. To solve these issues, in this work, a novel semantic-aware attention and visual shielding network for cloth-changing person ReID (abbreviated as SAVS) is proposed where the key idea is to shield clues related to the appearance of clothes and only focus on visual semantic information that is not sensitive to view/posture changes. Specifically, a visual semantic encoder is first employed to locate the human body and clothing regions based on human semantic segmentation information. Then, a human semantic attention (HSA) module is proposed to highlight the human semantic information and reweight the visual feature map. In addition, a visual clothes shielding (VCS) module is also designed to extract a more robust feature representation for the cloth-changing task by covering the clothing regions and focusing the model on the visual semantic information unrelated to the clothes. Most importantly, these two modules are jointly explored in an end-to-end unified framework. Extensive experiments demonstrate that the proposed method can significantly outperform state-of-the-art methods, and more robust features can be extracted for cloth-changing persons. Compared with multibiometric unified network (MBUNet) (published in TIP2023), this method can achieve improvements of 17.5% (30.9%) and 8.5% (10.4%) on the LTCC and Celeb-reID datasets in terms of mean average precision (mAP) (rank-1), respectively. When compared with the Swin Transformer (Swin-T), the improvements can reach 28.6% (17.3%), 22.5% (10.0%), 19.5% (10.2%), and 8.6% (10.1%) on the PRCC, LTCC, Celeb, and NKUP datasets in terms of rank-1 (mAP), respectively.

Though nuclear magnetic resonance (NMR) has been applied in soil science over several decades, the quantitative relation between NMR signals and soil pore-water distribution is complex and still covered by some cloud. The major debates include: 1) the quantitative relation between transverse relaxation time (T2) and pore radius varies in different studies; 2) Is the relation between NMR signals and soil-water contents unique? To clarify the aforementioned issues over the application of NMR in soil science, a comprehensive study was carried out. The results demonstrate that: 1) a unique linear relationship between peak area of NMR signals and soil volumetric water content (θ) exists, independent of the soil’s initial molding conditions, such as molding dry density (ρd) and molding water content (wini); 2) the ratio between T2and pore radius, defined as the pore structure coefficient (Cr) of NMR, varies with pore water morphologies and soil types; 3) three methods were proposed to determine the value of Crand can help to provide insights for better understanding of the NMR results in soil science.

The establishment of naive pluripotency is a continuous process starting with the generation of inner cell mass (ICM) that then differentiates into epiblast (EPI). Recent studies have revealed key transcription factors (TFs) for ICM formation, but which TFs initiate EPI specification remains unknown. Here, using a targeted rapid protein degradation system, we show that GABPA is not only a regulator of major ZGA, but also a master EPI specifier required for naive pluripotency establishment by regulating 47% of EPI genes during E3.5 to E4.5 transition. Chromatin binding dynamics analysis suggests that GABPA controls EPI formation at least partly by binding to the ICM gene promoters occupied by the pluripotency regulators TFAP2C and SOX2 at E3.5 to establish naive pluripotency at E4.5. Our study not only uncovers GABPA as a master pluripotency regulator, but also supports the notion that mammalian pluripotency establishment requires a dynamic and stepwise multi-TF regulatory network.

Nanozymes are nanomaterials with catalytic properties similar to enzymes and offer advantages over natural enzymes such as lower cost, ease of storage, and convenience for large-scale preparation. Thus, they have received increasing attention and are extensively applied in fields such as chemistry, sensing, food, environment, and medicine. Herein, a hemin-derived nanozyme (Hemin-CDs) was prepared using hemin as the precursor and used to substitute the natural horseradish peroxidase (HRP) for colorimetric detection. The prepared Hemin-CDs exhibit excellent water dispersibility, stability and superior peroxidase-like activity. They catalyze the oxidative coupling of colorless 4-aminoantipyrine (4-AAP) with N-ethyl-N-(2-hydroxy-3-sulfopropyl)-3-methylaniline sodium salt (TOOS) in the presence of H2O2, forming a purple quinone diimine dye with an absorption peak at 554 nm, which enables the quantification of H2O2by measuring the absorbance. Quantitative detection of glucose was further demonstrated under physiological conditions. The detection of H2O2and glucose takes only 10 minutes, with linear ranges of 1–150 μM and 2.5–300 μM, and LODs of 0.867 μM and 1.026 μM, respectively. Glucose in human serum was successfully detected with satisfactory recoveries (87.5–104.0%). Furthermore, a test strip was developed, and a smartphone was utilized to recognize the color of the test position, enabling rapid and accurate quantification of glucose concentrations within 5 minutes, which promises to enhance the accessibility and convenience of glucose testing.

CuW composites are widely used in high voltage transmission, electronic information, aerospace and other cutting-edge fields. At present, the preparation methods of CuW composites based on powder metallurgy have the defects of uneven microstructure and low density. Reducing the size of tungsten particles in the CuW composites can significantly improve the microstructure uniformity and density. In this paper, an innovative idea for regulating the microstructure of CuW composites by aluminothermic coupling with magnesiothermic reduction was proposed. The thermodynamics and dynamics of the reaction system for preparation of CuW composites by this method were studied, and the microstructure of CuW composites were systematically analysed and evaluation. The researches show that the inclusion in CuW prepared by aluminothermic reduction was Al2O3, and the inclusion in CuW prepared by aluminothermic coupling with magnesiothermic reduction was MgO·Al2O3. The addition of magnesium to the reducing agent had an obvious refining effect on the tungsten particles. The mechanism was that magnesium was converted into magnesium vapor during the rapid heating process of the system, and the gas-solid, gas-liquid-solid reaction occurred with tungsten oxide to generate smaller tungsten particles. With an increase of magnesium content, the average size of tungsten particles in the microstructure of CuW gradually decreased from 2.38 μm to 0.82 μm, and the microstructure uniformity increased from 56.26% to 76.96%.

Generalized zero-shot learning (GZSL) endeavors to identify the unseen categories using knowledge from the seen domain, necessitating the intrinsic interactions between the visual features and attribute semantic features. However, GZSL suffers from insufficient visual-semantic correspondences due to the attribute diversity and instance diversity. Attribute diversity refers to varying semantic granularity in attribute descriptions, ranging from low-level (specific, directly observable) to high-level (abstract, highly generic) characteristics. This diversity challenges the collection of adequate visual cues for attributes under a uni-granularity. Additionally, diverse visual instances corresponding to the same sharing attributes introduce semantic ambiguity, leading to vague visual patterns. To tackle these problems, we propose a multi-granularity progressive semantic-visual mutual adaption (PSVMA+) network, where sufficient visual elements across granularity levels can be gathered to remedy the granularity inconsistency. PSVMA+ explores semantic-visual interactions at different granularity levels, enabling awareness of multi-granularity in both visual and semantic elements. At each granularity level, the dual semantic-visual transformer module (DSVTM) recasts the sharing attributes into instance-centric attributes and aggregates the semantic-related visual regions, thereby learning unambiguous visual features to accommodate various instances. Given the diverse contributions of different granularities, PSVMA+ employs selective cross-granularity learning to leverage knowledge from reliable granularities and adaptively fuses multi-granularity features for comprehensive representations. Experimental results demonstrate that PSVMA+ consistently outperforms state-of-the-art methods.

An increasing number of early-stage lung adenocarcinomas (LUAD) are detected as lung nodules. The radiological features related to LUAD progression warrant further investigation. Exploration is required to bridge the gap between radiomics-based features and molecular characteristics of lung nodules.

Reactive nitrogen (Nr) emissions significantly affect air quality and the nitrogen cycle in ecosystems. Heavy-duty diesel vehicles (HDDVs), as major sources of these emissions, exhibit complex emission characteristics because of the combined effects of different driving conditions and aftertreatment technologies. This study first investigated the emission factors (EFs) of Nr species, including NO, NO2, HONO, N2O, and NH3, from HDDVs under different emission standards (China IV/V/VI) and cheating strategies, with a particular focus on the impact of selective catalytic reduction (SCR) systems. Vehicles employing water injection cheating present NO, NO2, and HONO EFs that are consistent with the China III standards, significantly undermining the effectiveness of Nr emission control. The evolution of SCR technology in China IV, V, and VI standards has generally led to substantial reductions in NO, NO2, and HONO emissions, yet the integration of ammonia slip catalysts (ASC) systems in China VI vehicles presents new challenges. While ASCs have successfully reduced NH3slip to an average of 17 ± 12 mg/km, they have also caused a 6–13-fold increase in N2O emissions compared with those of China IV and V vehicles, reaching levels of 205 ± 85 mg/km. Additionally, China VI vehicles exhibit a marked increase in the HONO/NOx ratio, which increases from 0.9% in China V to 4.6%. These increases are attributed to high-temperature oxidation of NH3within the ASC catalyst, leading to undesirable byproducts. The temporal dynamics of Nr emissions under real-world driving conditions further reveal that the effectiveness of aftertreatment technologies and their selectivity toward byproducts vary depending on the driving mode. This variability underscores the need for further optimization of the SCR and ASC technologies to balance the control of all the reactive nitrogen species effectively.

Stretchable supercapacitors are essential components in wearable electronics due to their low heat generation and seamless integration capabilities. Thermoplastic polyurethane elastomers, recognized for their dynamic hydrogen-bonding structure, exhibit excellent stretchability, making them well-suited for these applications. This study introduces fluorine-based interactions in the hard segments of thermoplastic polyurethanes, resulting in polyurethanes with a low elastic modulus, high fracture strength, exceptional fatigue resistance, and self-healing properties. By utilizing these polyurethanes as binders and meshed fabric as scaffolds, we developed highly stretchable conductors. These conductors maintain low resistance (∼26 ohms) under biaxial stretching and exhibit a stable bidirectional conductivity after 1600 stretching cycles. The fabricated supercapacitor electrode, incorporating fabric current collectors, polyurethane, and MXene, achieves an ultrahigh areal specific capacitance of 7200 mF cm–2and retains 100% capacity after 2300 cycles. This material design strategy offers significant potential in elastic materials, stretchable conductors, and high-performance energy storage for wearable electronics.

High-temperature (HT) kagome magnets provide important platforms to explore nontrivial topological physics and promising potentials for spintronic applications, due to the complicated interactions among their electrons, lattices, and magnetism. Herein, the nontrivial electronic properties of a HT layered kagome-magnet, HoMn6Sn6, are systematically resolved by quantum oscillation measurements and density functional theory (DFT) calculations. The prominent Shubnikov–de Haas (SdH) oscillations under pulsed high magnetic fields reveal a high quantum mobility of 0.37 m2·V–1·s–1for this HT ferrimagnet. The observed multiple-frequency quantum oscillations exhibit various angular dependences, consistent with DFT calculations which suggest a complex Fermi topology of three three-dimensional hole pockets and two electron pockets. The observed π shift of the Berry phase for quantum oscillations unveils nontrivial topological properties in HoMn6Sn6, further confirmed by DFT calculated Dirac fermions and large anomalous Hall conductivity. Our findings establish HoMn6Sn6as an HT magnetic candidate for topological magnetoelectronics or spin quantum applications.

Metal and acid sites are two of the most crucial catalytically active components in heterogeneous catalysis. While variations in the size, morphology, and heterogeneity of metal species, or the manipulation of the strength, location, and density of acid sites, could significantly impact the catalytic performance, the combination and interplay between these sites are even more critical and have been a recent research focus. To achieve highly efficient and selective synergistic catalysis, it is desired to design a catalyst capable of orchestrating the sequential transformation of all reactants and intermediates at different active sites. In this study, we demonstrate that both acid and metal (Pd) sites can be introduced onto a nanodiamond@graphene (NDG) support particle through simple air oxidation and metal salt deposition-precipitation methods, respectively. The presence and assembly of these two catalytically active sites significantly alter the reaction network for the cyclohexanol conversion reaction. Under this strategy, the selectivity toward designated products─cyclohexene, phenol, and benzene─can be precisely tuned by the presence and patterning of these two sites on the nanodiamond particles. Specifically, we show that the catalyst with both acid sites and Pd ensemble sites, i.e., Pd/NDG, can efficiently convert cyclohexanol through consecutive dehydration and dehydrogenation reactions to form benzene with high selectivity (>80%). These findings underscore the potential of integrating metal and acid sites to design advanced catalysts with tailored reactivity and selectivity, paving the way for more efficient and versatile catalytic processes in industrial applications.

Hepatic stellate cells (HSCs) activation results in liver fibrosis. When HSCs are activated, metabolism is reprogrammed. However, metabolic alteration in HSCs activation has not been sufficiently addressed. This study aims to investigate the role of lactate dehydrogenase (LDH) inhibition in HSCs activation with an emphasis on the metabolic reprogramming. Mice were subjected to carbon tetrachloride (CCl4) to induce liver injury. In addition, the primary HSCs were isolated for mechanism investigation. Our study demonstrated that LDH inhibition impaired HSCs activation through suppressing the enhanced glycolysis by blocking nicotinamide adenine dinucleotide (NAD+) regeneration. Meanwhile, LDH inhibition also impeded the glutamine metabolism through the lactic acid/histone deacetylase (HDAC)/histone acetylation/cellular-myelocytomatosis viral oncogene (c-Myc) signaling pathway. Our findings demonstrated that LDH inhibition is a potential target for liver fibrosis treatment, which provides new insight into the pathogenesis of liver fibrosis from the aspect of metabolic reprogramming, contributing to the design of a novel therapeutic strategy in the management of liver fibrosis.