Flexible Combination Research Articles

In Silico Prediction of Skin Sensitization for Compounds via Flexible Evidence Combination Based on Machine Learning and Dempster-Shafer Theory.

Skin sensitization is increasingly becoming a significant concern in the development of drugs and cosmetics due to consumer safety and occupational health problems. In silico methods have emerged as alternatives to traditional in vivo animal testing due to ethical and economic considerations. In this study, machine learning methods were used to build quantitative structure-activity relationship (QSAR) models on five skin sensitization data sets (GPMT, LLNA, DPRA, KeratinoSens, and h-CLAT), achieving effective predictive accuracies (correct classification rates of 0.688-0.764 on test sets). To address the complex mechanisms of human skin sensitization, the Dempster-Shafer theory was applied to merge multiple QSAR models, resulting in an evidence-based integrated decision model. Various evidence combinations and combination rules were explored, with the self-defined Q3 rule showing superior balance. The combination of evidence such as GPMT and KeratinoSens and h-CLAT achieved a correct classification rate (CCR) of 0.880 and coverage of 0.893 while maintaining the competitiveness of other combinations. Additionally, the Shapley additive explanations (SHAP) method was used to interpret important features and substructures related to skin sensitization. A comparative analysis of an external human test set demonstrated the superior performance of the proposed method. Finally, to enhance accessibility, the workflow was implemented into a user-friendly software named HSkinSensDS.

Sparse Group Penalties for bi-level variable selection.

Many data sets exhibit a natural group structure due to contextual similarities or high correlations of variables, such as lipid markers that are interrelated based on biochemical principles. Knowledge of such groupings can be used through bi-level selection methods to identify relevant feature groups and highlight their predictive members. One of the best known approaches of this kind combines the classical Least Absolute Shrinkage and Selection Operator (LASSO) with the Group LASSO, resulting in the Sparse Group LASSO. We propose the Sparse Group Penalty (SGP) framework, which allows for a flexible combination of different SGL-style shrinkage conditions. Analogous to SGL, we investigated the combination of the Smoothly Clipped Absolute Deviation (SCAD), the Minimax Concave Penalty (MCP) and the Exponential Penalty (EP) with their group versions, resulting in the Sparse Group SCAD, the Sparse Group MCP, and the novel Sparse Group EP (SGE). Those shrinkage operators provide refined control of the effect of group formation on the selection process through a tuning parameter. In simulation studies, SGPs were compared with other bi-level selection methods (Group Bridge, composite MCP, and Group Exponential LASSO) for variable and group selection evaluated with the Matthews correlation coefficient. We demonstrated the advantages of the new SGE in identifying parsimonious models, but also identified scenarios that highlight the limitations of the approach. The performance of the techniques was further investigated in a real-world use case for the selection of regulated lipids in a randomized clinical trial.

Context-aware multi-modal route selection service for urban computing scenarios

The rapid urban population growth introduces novel challenges to urban mobility scenarios, requiring innovative and connected solutions to relieve traffic congestion and enhance transportation efficiency, using unusual roads and public transportation modals. For instance, multi-modal routes allow more flexible modal combinations to offer smart multi-modal mobility. In this context, the route selection service must rely on different context information, such as criminality, accidents, air quality, and others, where Internet of Things technologies introduce active and ubiquitous sensing of many contexts along the urban scenario, providing data for statistical analysis to offer a safer and healthier urban reality. However, designing an efficient multi-modal route selection service that considers different context information to offer personalized routes is important. In this article, we describe a context-aware route selection service that considers adequate contextual information to provide routes according to the user’s preference. The multi-modal route selection service applies a multi-criteria method to give different degrees of importance to each criterion based on the user profile (i.e., Worker, Green, Safe, and Tourist). We present extensive evaluation results from applying our multi-modal route selection approach to a London dataset. The approach successfully enables user-preferred transportation choices using four balanced selection profiles and ten route features. The proposed multi-modal route selection service demonstrates better performance in terms of economic, ecological, and time-saving metrics for each user profile compared to a greedy selection manner.

Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins.

Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented inside the computational protein modeling suite Rosetta, which allows flexible combination with existing protocols to model the modified sites and understand their impact on protein stability as well as function. Lastly, we developed a new design protocol that either maximizes or minimizes the predicted probability of a particular site being modified. We find that this combination of ANN prediction and structure-based design can enable the modification of existing, as well as the introduction of novel, PTMs. The potential applications of our work include, but are not limited to, glycan masking of epitopes, strengthening protein-protein interactions through phosphorylation, as well as protecting proteins from deamidation liabilities. These applications are especially important for the design of new protein therapeutics where PTMs can drastically change the therapeutic properties of a protein. Our work adds novel tools to Rosetta's protein engineering toolbox that allow for the rational design of PTMs.

Food classification systems: A short description

This paper highlights different food classification systems that categorize foods in terms of degree of processing, study the similarities and differences between them and the applicability for Tunisian context will be addressed. The main challenge of this study is to harmonize national food processing for international comparison, yet to date there is a lack of comparable food classification system that incorporates food description in Tunisia which provides a flexible combination of classifications.

Tuning Optical Properties of Carbon Dots by Co‐doping Strategy for Versatile Applications

AbstractCarbon dots (CDs) with tunable photoluminescence was successfully prepared via selective co‐doping of three elements, including nitrogen, phosphorus and sulfur element by green one‐step hydrothermal method. Nitrogen‐sulfur‐doped CDs (red NS‐CDs), which emitted red fluorescence under UV excitation, were used to detect iron ions in the linear range 1–50 μM, yielding detectable limits as low as 94 nM. The nitrogen‐phosphorus‐doped CDs (blue NP‐CDs) exhibited blue fluorescence with quantum yields up to 34.5 %. When the concentration is 800 μg/mL, it showed excellent anticancer effect in vitro. The nitrogen‐sulfur‐phosphorus‐doped CDs (green NPS‐CDs) emitting green fluorescence showed a low cytotoxicity at a high concentration of 800 μg/mL and were used for intracelluar imaging of NCl−H292 cells. This study demonstrated the photoluminescent characteristics of carbon dots (CDs) and highlighted the ability to modify their peak fluorescence emission wavelengths through the flexible combination of specific reagents under comparable hydrothermal conditions. Additionally, it provided valuable insights into the efficient methodologies for synthesizing CDs suitable for various application scenarios.

Using radial basis function for estimating the high-performance concrete compressive strength with optimizing AVOA and SSA

Compressive strength (CS) is concrete’s most important mechanical property, as it plays an important role in setting design criteria. Thus, an accurate and early assessment of the CS of concrete can minimize time, labor, and cost. This paper investigated the ability of the Radial Basis Function (RBF) to handle the prediction of CS. The nonlinearities raised from the novel utilized admixtures between the input variables and output CS is tried to be conducted with the RBF model. In order to make a flexible framework combination of the RBF model with the African Vulture Optimization (AVOA) and Salp Swarm Algorithm (SSA) techniques are considered. The results achieved from the RBF-AVOA model indicated good agreement between the actual and predicted values. The proposed model provides a very accurate HPC compressive strength prediction. In addition, the correlation coefficient R2 is equal to (0.997), and the values of mean absolute error (MAE) (0.1917 MPa), root mean square error (RMSE) (0.937 MPa), and variance account coefficient (VAF) (99.73%) are low. The performance of the RBF-AVOA model, compared to other models, provided the desired advantage and more stable predictions. AVOA plays a key role in modeling results, improving generalization capabilities, avoiding redundant data, and decreasing uncertainty.

Enhancing Security and Flexibility in the Industrial Internet of Things: Blockchain-Based Data Sharing and Privacy Protection

To address the complexities, inflexibility, and security concerns in traditional data sharing models of the Industrial Internet of Things (IIoT), we propose a blockchain-based data sharing and privacy protection (BBDSPP) scheme for IIoT. Initially, we characterize and assign values to attributes, and employ a weighted threshold secret sharing scheme to refine the data sharing approach. This enables flexible combinations of permissions, ensuring the adaptability of data sharing. Subsequently, based on non-interactive zero-knowledge proof technology, we design a lightweight identity proof protocol using attribute values. This protocol pre-verifies the identity of data accessors, ensuring that only legitimate terminal members can access data within the system, while also protecting the privacy of the members. Finally, we utilize the InterPlanetary File System (IPFS) to store encrypted shared resources, effectively addressing the issue of low storage efficiency in traditional blockchain systems. Theoretical analysis and testing of the computational overhead of our scheme demonstrate that, while ensuring performance, our scheme has the smallest total computational load compared to the other five schemes. Experimental results indicate that our scheme effectively addresses the shortcomings of existing solutions in areas such as identity authentication, privacy protection, and flexible combination of permissions, demonstrating a good performance and strong feasibility.

Feature Extraction of a Planetary Gearbox Based on the KPCA Dual-Kernel Function Optimized by the Swarm Intelligent Fusion Algorithm

The feature extraction problem of coupled vibration signals with multiple fault modes of planetary gears has not been solved effectively. At present, kernel principal component analysis (KPCA) is usually used to solve nonlinear feature extraction problems, but the kernel function selection and its blind parameter setting greatly affect the performance of the algorithm. For the optimization of the kernel parameters, it is very urgent to study the theoretical modeling to improve the performance of kernel principal component analysis. Aiming at the deficiency of kernel principal component analysis using the single-kernel function for the nonlinear mapping of feature extraction, a dual-kernel function based on the flexible linear combination of a radial basis kernel function and polynomial kernel function is proposed. In order to increase the scientificity of setting the kernel parameters and the flexible weight coefficient, a mathematical model for dual-kernel parameter optimization was constructed based on a Fisher criterion discriminant analysis. In addition, this paper puts forward a swarm intelligent fusion algorithm to increase this method’s advantages for optimization problems, involving the shuffled frog leaping algorithm combined with particle swarm optimization (SFLA-PSO). The new fusion algorithm was applied to optimize the kernel parameters to improve the performance of KPCA nonlinear mapping. The optimized dual-kernel function KPCA (DKKPCA) was applied to the feature extraction of planetary gear wear damage, and had a good identification effect on the fuzzy damage boundary of the planetary gearbox. The conclusion is that the DKKPCA optimized by the SFLA-PSO swarm intelligent fusion algorithm not only effectively improves the performance of feature extraction, but also enables the adaptive selection of parameters for the dual-kernel function and the adjustment of weights for the basic kernel function through a certain degree of optimization; so, this method has great potential for practical use.

Han Meilin’s style path of creating ancient Chinese character art——Taking “TianShu” as an example

Han Meilin pursued the “beauty of lines, composition, and form” in the genes of ancient Chinese characters through modern “prioritization operation, flexible combination, and two-dimensional and three-dimensional cross-border”. He created a series of works in both Chinese and modern “Tianshu” style, and held an art exhibition in the main building of the Meridian Gate of the Forbidden City. It can be seen that Han Meilin’s style of ancient Chinese characters conforms to the modern aesthetic of Chinese style in the context of contemporary art and design. The article establishes a connection between the formation path of artist style and personality power from the perspective of subjective anthropology, extracts the method of Han Meilin’s transformation of ancient Chinese characters genes, and provides a style path that can be referenced for the development of national art resources in modernity.

Uncertainty analysis of intra-module environmental stress parameter design in light use efficiency-based gross primary productivity estimation models

Terrestrial gross primary productivity is a key part in the carbon cycle, and the light use efficiency model is a widely employed tool for its estimation. However, since the specific designs of environmental stress parameters in light use efficiency models have obvious disparities, even for a single module (e.g., the temperature stress module), the uncertainties related to intra-module environmental stress parameters in light use efficiency models remain unclear. Thus, we gathered mainstream temperature and water stress parameters in light use efficiency models from existing publications, and employed a compatible framework to assess them from three perspectives: (1) identifying similarities and differences of environmental stress parameters; (2) evaluating the error propagation effect of input data under different environmental stress parameter combinations; and (3) assessing the generalization ability of environmental stress parameters. The results showed that the temperature stress parameters exhibited general homogeneity (shared 67.87% variance), while water stress parameters displayed noticeable internal variations (shared variance below 1.0%). Meanwhile, we revealed that the current flexible parameter combination method in light use efficiency model construction should be more cautious, since the flexible environmental stress parameter combinations would influence the error propagation effect from input data to final gross primary productivity estimations. In addition, our analysis found that the variance of environmental stress parameters was closely coupled to model estimation accuracy, and there was a positive relationship between the unique variance of temperature stress parameters and the ability of temperature stress parameters to describe gross primary productivity variation. Overall, the current environmental stress parameters demonstrated acceptable performance across most situations, except for biomes with high temperatures. This study provided a foundation towards the development of future parameter design, which may also inspire the application of empirical environmental factors in other gross primary productivity models.

Advancements in Achieving High Reversibility of Zinc Anode for Alkaline Zinc-Based Batteries.

Rechargeable alkaline zinc-based batteries (ZBBs) have attracted extensive research attention due to their advantages of low cost, high specific energy, and high safety. Although the investigation of cathodes for alkaline secondary ZBBs has reached a relatively advanced stage, the exploration of zinc anodes is still in its infancy. Zinc anodes in alkaline electrolytes encounter challenges such as dendrite formation, passivation, corrosion during periods of cell inactivity, and hydrogen evolution during cycling, thereby limiting their rechargeability and storability. Drawing upon the latest research on zinc anodes,six fundamental strategies that encompass a wide range of aspects are identified and categorized, from electrode modifications and electrolytes to charge protocols. Specifically, these strategies include 3D structures, coatings, alloying, additives, separators, and charge protocols. They serve as an insight summary of the current research progress on zinc anodes. Additionally, the complementary nature of these strategies allows for flexible combinations, enabling further enhancement of the overall performance of zinc anodes. Finally, several future directions for the advancement of practical alkaline Zn anode are proposed. This comprehensive review not only consolidates the existing knowledge but also paves the way for broader research opportunities in the pursuit of high-performance alkaline zinc anodes.

Jellyfish: A modular code for wave function‐based electron dynamics simulations and visualizations on traditional and quantum compute architectures

AbstractUltrafast electron dynamics have made rapid progress in the last few years. With Jellyfish, we now introduce a program suite that enables to perform the entire workflow of an electron‐dynamics simulation. The modular program architecture offers a flexible combination of different propagators, Hamiltonians, basis sets, and more. Jellyfish can be operated by a graphical user interface, which makes it easy to get started for nonspecialist users and gives experienced users a clear overview of the entire functionality. The temporal evolution of a wave function can currently be executed in the time‐dependent configuration interaction method (TDCI) formalism, however, a plugin system facilitates the expansion to other methods and tools without requiring in‐depth knowledge of the program. Currently developed plugins allow to include results from conventional electronic structure calculations as well as the usage and extension of quantum‐compute algorithms for electron dynamics. We present the capabilities of Jellyfish on three examples to showcase the simulation and analysis of light‐driven correlated electron dynamics. The implemented visualization of various densities enables an efficient and detailed analysis for the long‐standing quest of the electron–hole pair formation.This article is categorized under: Theoretical and Physical Chemistry > Spectroscopy Software > Simulation Methods

Surgical management of giant pituitary adenomas: institutional experience and clinical outcomes of 94 patients

BackgroundGiant (with a diameter of at least 40 mm and a volume of at least 10 cm3) pituitary adenomas (GPAs) are intricate tumors that pose considerable difficulty for surgical removal. While endoscopic transsphenoidal surgery (ETS) is a commonly employed technique for these destructive tumors, its effectiveness may be restricted in cases where invasion into multiple compartments is present, leading to limited resection.MethodsA retrospective review was conducted on the clinical records of 94 patients diagnosed with GPAs who had undergone surgical resection from 2014 to 2022. An analysis was conducted on the outcomes of the surgical and clinical procedures.ResultsIn this group, the average size of the tumor before surgery was 44.6 ± 5.6 mm (range, 40–73 mm), and the volume was 25. 5± 16.6 cm3 (range, 10–20.67 cm3). Of the total number of patients, 72 (76.6%) underwent a single ETS, 12 (12.8%) opted for transcranial surgery (TCS), and 10 (10.6%) chose a combined method. Gross total resection (GTR) was successfully performed in 49 (68.1%), 3 (25.0%), and 8 (80.0%) patients who underwent each surgical approach. Seventy-four (78.7%) patients had improved vision, 20 (21.3%) were unchanged, and none had deterioration. Twenty-two patients (23.4%) experienced a total of 43 complications, which comprised hormonal insufficiency (11/94, 11.7%), diabetic insipidus (6/88, 6.8%), electrolyte disorders (7/94, 7.4%), cerebrospinal fluid leakage (5/94, 5.3%), meningitis (8/94, 8.5%), and hydrocephalus (6/94, 6.4%). The GTR, subtotal resection (STR), and partial resection (PTR) rates were 63.8% (60/94), 21.3% (20/94), and 14.9% (14/94), respectively. Throughout the follow-up duration, 18.1% (17/94) of patients required reoperation and/or adjuvant radiation treatment as a result of tumor regrowth or inadequate biochemical remission of functioning GPAs.ConclusionETS remains the optimal surgical option for most GPAs and generally offers safe and efficient tumor resection. However, a combined approach with TCS remains a requirement in cases that are not suitable for treatment with a single ETS. To achieve optimal tumor removal and minimize the occurrence of surgical complications, a flexible combination of ETS and TCS is recommended based on the characteristics of the tumor.

Fading suppression in the Ф-OTDR system based on a phase-modulated optical frequency comb.

In this paper, what we believe to be a novel method is proposed to suppress the fading effect of the phase-sensitive optical time domain reflectometer (Ф-OTDR) by using a phase-modulated optical frequency comb. In the Ф-OTDR system, intensity distributions of Rayleigh backscattering (RBS) light are different for pulsed probe lights with different central frequencies, therefore the locations of the fading points corresponding to signals of different frequencies are differently distributed, allowing the use of frequency division multiplexing to suppress the fading effects. In the experimental system of this paper, a continuous light in the form of a frequency comb is firstly generated through phase modulation. It is then modulated into a pulsed probe light and injected into the sensing fiber to produce different RBS intensity distributions. Finally, the extracted phase is processed by using the amplitude evaluation method, so that the distorted phase can be eliminated. Fading suppression is achieved using our system, and the effect of suppression is evaluated. By using an equal-amplitude optical frequency comb containing seven frequency components, the fading probability density of the system is dramatically reduced from the range of 5.49%-9.83% to 0.08%. Compared with the conventional system using a single acoustic-optic modulator to generate the frequency shift, the method proposed in this paper features a larger modulation bandwidth and more flexible frequency combination scheme to better suppress the fading effect. This method does not sacrifice the response bandwidth of the system, and the phase delay can be precisely controlled, which helps to fully suppress the fading effect.

Optimal scheduling of a microgrid with power quality constraints based on demand side management under grid-connected and islanding operations

Power quality (PQ) issues are direct consequences of integrating power electronics components and non-linear loads into microgrids. These issues can also be triggered by an unbalanced loading in the microgrid. Certainly, they affect the daily operation scheduling of the microgrid. This paper proposes an optimal harmonic power flow (OHPF) framework for the daily optimal scheduling of a grid-connected microgrid, which is constructed by combining the optimization formulation and harmonic power flow (HPF). Within the framework, PQ is evaluated by observing three indices including voltage magnitude, voltage total harmonic distortion (THDV), and voltage unbalance factor (VUF). A non-iterative mitigation scheme based on demand side management (DSM) is proposed to avoid PQ indices violations and is integrated into the optimization formulation part of the OHPF as a set of load constraints. The proposed constraints allow a flexible combination of different DSM actions including load shedding and interphase load transfer (ILT). Furthermore, the constraints to limit voltage magnitude, voltage THD, and VUF are integrated into the formulation in the form of penalty functions. The software implementation of the proposed OHPF involves Julia-based JuMP.jl and Gurobi solver for optimization part and OpenDSS for harmonic load flow part. The effectiveness of the proposed framework is tested using a modified IEEE 37-bus feeder for different load fluctuations, which allow different combinations of PQ indices violations. The assessment involves both normal and intentional islanding conditions. The results demonstrate that, under different random events, the proposed framework can avoid the violation of PQ indices limits in most cases without adding an excessive computational burden to the original optimization and harmonic load flow algorithms.

Chemical looping CO2 capture and in-situ conversion: Fundamentals, process configurations, bifunctional materials, and reaction mechanisms

As an emerging and promising technology to debate the energy and environment issues raised by anthropogenic CO2 emissions, chemical looping CO2 capture and in-situ conversion (CL-ICCC) exhibits the merit of high efficiency, low cost, and high safety to achieve integrated CO2 capture and conversion (ICCC) by eliminating the purification, compression, transportation, and storage procedures. However, the interpretation and state-of-the-art of CL-ICCC are still unclear, which contribute to a harmful effect on promoting this technology for industrial applications. Herein, this work presents a timely review on CL-ICCC by giving fundamental discussions on definition, process configuration, bifunctional material, reaction mechanism and thermo-economic behavior. Based on the product distributions (syngas, CO, CH4, C2H4/C3H6), CL-ICCC systems using abundant bifunctional materials with flexible combinations of sorbents and catalysts are classified to satisfy the demand of customers. Lots of moderating strategies are proposed to enhance the activity and stability of bifunctional material, meanwhile, reaction mechanism is revealed to explain the underlying reason for superior performance. The challenges and future prospectives from the aspects of microscopic reaction mechanism, bifunctional material rational design, reaction equipment development and system integration and optimization are discussed to provide possible suggestions. This review aims to illustrate CL-ICCC more clearly and accelerate its commercial demonstration.

Metal-Phenolic Networks for Chronic Wounds Therapy.

Chronic wounds are recalcitrant complications of a variety of diseases, with pathologic features including bacterial infection, persistent inflammation, and proliferation of reactive oxygen species (ROS) levels in the wound microenvironment. Currently, the use of antimicrobial drugs, debridement, hyperbaric oxygen therapy, and other methods in clinical for chronic wound treatment is prone to problems such as bacterial resistance, wound expansion, and even exacerbation. In recent years, researchers have proposed many novel materials for the treatment of chronic wounds targeting the disease characteristics, among which metal-phenolic networks (MPNs) are supramolecular network structures that utilize multivalent metal ions and natural polyphenols complexed through ligand bonds. They have a flexible and versatile combination of structural forms and a variety of formations (nanoparticles, coatings, hydrogels, etc.) that can be constructed. Functionally, MPNs combine the chemocatalytic and bactericidal properties of metal ions as well as the anti-inflammatory and antioxidant properties of polyphenol compounds. Together with the excellent properties of rapid synthesis and negligible cytotoxicity, MPNs have attracted researchers' great attention in biomedical fields such as anti-tumor, anti-bacterial, and anti-inflammatory. This paper will focus on the composition of MPNs, the mechanisms of MPNs for the treatment of chronic wounds, and the application of MPNs in novel chronic wound therapies.

A Numerical Algorithm for Calculating Critical Points and Its Application to Predictive Mixture Models and Binary CO_2 Mixtures

The use of supercritical fluids in technical applications requires an accurate knowledge of their critical points. For mixtures, these can deviate significantly and without a linear dependency from the critical points of its individual pure components. Since even small amounts of admixture can have noticeable effects, this not only concerns blends of targeted compositions, but also unintentional mixtures for example caused by impurities. Within this work, a method for the calculation of critical points is presented which focuses on numerical robustness promoting a fast and reliable generation of results. Implemented into the thermodynamic property software TREND, with its mixture modeling capabilities, the method allows a flexible combination of different equations of state and mixture models which also includes predictive approaches. Against the background of an increasing relevance of mixtures based on supercritical CO_2 (sCO_2) for energy applications, critical lines are calculated and compared against experimental results for selected sCO_2-based mixtures recently considered for power plant applications. Herein, several combinations of equations of state (EoS) and mixture models are compared. Critical lines are calculated for the first time in this work with the combination of the multi-fluid mixture model with excess Gibbs energy (g^text {E}) models. It was found that the critical lines calculated with the combination of the multi-fluid mixture model with the g^text {E}-model COSMO-SAC yields good predictive results for the investigated CO_2 mixtures.

Influence of dual-credit policy on the energy conservation and emission reduction technology R&D of Chinese auto industry

The dual-credit policy, as an important industrial policy in the automotive industry to replace fiscal subsidies, aims to achieve the dual objectives of energy conservation and emission reduction (ECER) and promote the development of new energy vehicles by establishing a dual credit system based on the average fuel consumption of conventional vehicles and credits for new energy vehicles. This paper employs a game model to investigate the differential impact of the dual-credit policy on ECER among automobile manufacturers with different industry structures. The study finds that the existing policy primarily favors manufacturers adopting the integration mode, followed by the multilateral mode and supply-monopoly mode respectively. Furthermore, R&D cost coefficient, unit price of credits, degree of competition and elasticity of cross-demand for fuel-saving functions are the four main factors that influence the profit model of the automobile industry. The findings of this study suggest that targeted policies need to be formulated for different industry structures during the adjustment of the policy in order to fully stimulate their enthusiasm for ECER. Additionally, the flexible combination of the four factors should be considered when formulating a specific dual-credit policy so as to achieve more energy saving and emission reduction effect in the automobile industry.