Competitive Model Research Articles

Government influence on national competitiveness (evidence from the COVID era)

Purpose This paper aims to examine how government policy and institutions affect national competitiveness. A combined microeconomic and institutionalist model of competitiveness is applied. This structure is suitable for incorporating factors considered by global competitiveness rankings. The paper proposes that there are various possible government policy “configurations” leading to similar competitiveness outcomes, but different resilience outcomes during a crisis. Design/methodology/approach Using the Institute for Management Development competitiveness rankings, covering 62 countries, between 2010 and 2019 the authors first build clusters based on observed “government policy configurations”. These clusters show an interpretable pattern: except for a few outliers, individual clusters contain countries that are economically and culturally similar. Then the authors examine how different clusters, with similar overall competitiveness scores, have performed in 2020–2021 during the COVID pandemic. Findings The analysis shows that government efficiency is correlated with other factors of overall competitiveness. It shows that while similar levels of competitiveness are possible with different government “configurations”, it provides evidence that more welfare-oriented government “configurations” during the crisis led to a higher resilience of national competitiveness. Originality/value The paper connects an institutionalist and microeconomic view of competitiveness in a unique model and embeds government policy in this structure. It shows that a similar level of competitiveness is possible through different government policy “configurations” and exploits the COVID shock to analyse resilience of these “configurations”.

Tet2-mediated clonal hematopoiesis modestly improves neurological deficits and is associated with inflammation resolution in the subacute phase of experimental stroke

IntroductionRecent work has revealed that clonal hematopoiesis (CH) is associated with a higher risk of numerous age-related diseases, including ischemic stroke, however little is known about whether it influences stroke outcome independent of its widespread effects on cardiovascular disease. Studies suggest that leukocytes carrying CH driver mutations have an enhanced inflammatory profile, which could conceivably exacerbate brain injury after a stroke.MethodsUsing a competitive bone marrow transplant model of Tet2-mediated CH, we tested the hypothesis that CH would lead to a poorer outcome after ischemic stroke by augmenting brain inflammation. Stroke was induced in mice by middle cerebral artery occlusion and neurological outcome was assessed at acute (24 h) and subacute (14 d) timepoints. Brains were collected at both time points for histological, immunofluorescence and gene expression assays.ResultsUnexpectedly, Tet2-mediated CH had no effect on acute stroke outcome but led to a reduction in neurological deficits during the subacute phase. This improved neurological outcome was associated with lower levels of brain inflammation as evidenced by lower transcript levels of various inflammatory molecules alongside reduced astrogliosis.DiscussionThese findings suggest that Tet2-mediated CH may have beneficial effects on outcome after stroke, contrasting with the conventional understanding of CH whereby leukocytes with driver mutations promote disease by exacerbating inflammation.

Complexity Analysis and Control of Output Competition in a Closed-Loop Supply Chain of Cross-Border E-Commerce Under Different Logistics Modes Considering Chain-to-Chain Information Asymmetry

To investigate the dynamic complexity of chain-to-chain output decisions in a closed-loop supply chain system of cross-border e-commerce (CBEC), this study decomposes the system into four product–market (PM) chains, based on the e-commerce platform’s information-sharing strategy and the manufacturer’s selected logistics mode (direct mail or bonded warehouse). By combining game theory with complex systems theory, discrete dynamic models for output competition among PM chains under four scenarios are constructed. The Nash equilibrium solution and stability conditions of the models are derived according to the principles of nonlinear dynamics. The stability of the model under the four scenarios, as well as the impacts of the initial output level and comprehensive tax rates on the stability and stability control of the system, are analyzed using numerical simulation methods. Our findings suggest that maintaining system stability requires controlling the initial output levels, the output adjustment speeds, and tariff rates to remain within specific thresholds. When these thresholds are exceeded, the entropy value of the model increases, and the system outputs decisions to enter a chaotic or uncontrollable state via period-doubling bifurcations. When the output adjustment speed of the four PM chains is high, the direct-mail logistics mode exhibits greater stability. Furthermore, under increased tariff rates for CBEC, the bonded warehouse mode has a stronger ability to maintain stability in system output decisions. Conversely, when the general import tax rate increases, the direct-mail mode demonstrates better stability. Regardless of the logistics mode, the information-sharing strategy can enhance the stability of system output decisions, while increased e-commerce platform commission rates tend to reduce stability. Interestingly, the use of a non-information-sharing strategy and the direct-mail logistics mode may be more conducive to increasing the profit levels of overseas manufacturers. Finally, the delayed feedback control method can effectively reduce the entropy value, suppress chaotic phenomena in the system, and restore stability to output decisions from a fluctuating state.

Формування моделі міжнародних економічних відносин у контексті попередньої траєкторії розвитку національного господарства України

In the conditions of war and post-war reconstruction, Ukraine’s further integration into the world economy and trade-economic cooperation with the EU, the USA, and other developed countries is a priority task. However, the Soviet economic heritage in the form of the extensive elements and forms of economic management continues to create obstacles to building a competitive model of foreign trade cooperation. And overcoming these obstacles is impossible without rethinking and evaluating the historical experience. The research’s aim is to summarize the determinants and structural-institutional characteristics of the model of Ukrainian international economic relations shaped within the Soviet economic system. The theoretical basis of the research includes the concepts of dependent development and the «track effect», which allow assessing the influence of the Soviet legacy on the modern economic processes. The research methodology is based on the systemic approach, and problem-logical, comparative, institutional, and statistical analysis. The author defines the institutional and structural characteristics of the Ukrainian model of international economic relations shaped within the Soviet economic space and outside it. These include the republic's lack of autonomy regarding foreign trade and its subordination to the state system of centralized export-import planning; absence of closed production and technological cycles within one republic and deepening of all-Union trade and economic integration; production specialization of regions according to the central interests; the dominance of trade with other socialist countries and the so-called countries of non-capitalist orientation; a high share of barter operations and compensatory cooperation aimed at strengthening the so-called socialist integration; and an active application of the policy of customs and trade protectionism in relation to countries with a market economy. It is confirmed that the place of Ukraine's economy in the Soviet Union’s division of labor and the system of foreign economic relations was determined by the republic’s political and economic dependence on the center and its subordination to the Soviet concept of building an all-Union economic space. This largely limited Ukraine's opportunities in the formation and implementation of its own strategies of foreign economic interaction and led to an inertial pursuit in the fairway of the post-Union political entities and economic agreements after the collapse of the Soviet Union in the first decades of Ukraine's state independence.

Optimal pricing and collection decisions in a two-period closed-loop supply chain considering channel inconvenience

Improving recovery efficiency is a key concern for collectors in a closed-loop supply chain (CLSC) with remanufacturing, as customers often consider the inconvenience of recycling channels when returning used products. This issue profoundly affects collectors’ capacity to recover materials. In a two-period CLSC with remanufacturing, including a manufacturer and a retailer, we develop game-theoretical models in the centralized and decentralized scenarios and compare the optimal solutions, consumer surplus, social welfare and environmental impact of different models through analytical and numerical analysis. Our aim is to examine firms’ dynamic pricing strategies and collection investment decisions by considering customers’ perception of channel inconvenience. There are four main findings. Firstly, in the centralized model and the retailer collection model, the decision-maker lowers the retail price in the first period. However, in the competitive collection model, the manufacturer and the retailer raise the wholesale and retail prices in the first period, respectively. Secondly, in the retailer collection model, as the recycling revenue increases, the manufacturer, although not involved in collecting, the profit also increases due to the free-rider behavior. Thirdly, in the competitive collection model, when the remanufacturing cost savings is relatively high, the collection investment of the manufacturer is much larger than that of the retailer, resulting in the retailer failing to collect any product and giving up collecting. Finally, the collection competition improves the total collection rate and environmental performance but reduces the profit of the manufacturer and the retailer, as well as the consumer surplus and social welfare. Therefore, we design a two-part tariff contract to coordinate the decentralized model and effectively improve the performance of the supply chain.

Prediction of fixed-bed GAC filter consumption for complex VOC mixtures using a parsimonious competitive adsorption model and a single batch test

Prediction of fixed-bed GAC filter consumption for complex VOC mixtures using a parsimonious competitive adsorption model and a single batch test

Dynamic changes in wettability within a nanoscale slit of coal during hot flue gas injection

Aiming at exploring the dynamic changes in wettability within a nanoscale slit of coal during hot flue gas injection, a competitive wetting model of coal–water–multicomponent gas that considers real gas occurrence pressures was established in this study. Additionally, various methods that influence the wettability within the slit at different hot flue gas injection pressures were investigated, and the mechanism of wettability changes within the slit was elucidated based on the analyses on interaction energy, radial distribution function curve, molecular migration characteristics at the slit outlet, density distribution, and contact angle. The research findings are as follows. The intensities of the interaction energy between high-temperature and low-temperature water molecules differ markedly after the completion of hot flue gas injection at different pressures. Both the radial distribution function and the interaction energy indicate that the two types of water molecules fuse the most thoroughly at 20 MPa. At low pressures, the number of water molecules in the observation area changes slightly. The high-yielding period of the two types of water molecules at 20 MPa is 1.5 ns longer than that at 25 MPa, and the numbers of the two types of water molecules are less different at the same time at 20 MPa except for the case at 3 ns. After the injection, the densities of the preexisting water molecules and CO2 and the contact angle of the hot flue gas bubble demonstrate that the wettability transition within the slit is more complete at relatively high pressures (20 and 25 MPa) from two perspectives. At 20 MPa, the “re-absorption” is milder, and the bubble is milder and forms a smaller contact angle on the coal substrate. Based on the above research findings, it is concluded that raising the injection pressure of hot flue gas is conducive to the transition from water wetting to gas wetting and the elimination of the water lock effect. This research provides support for selecting the optimal hot flue gas injection pressure at the molecular level.

A class of free boundary problems describing the local-nonlocal cross-diffusion models with two species in ecology

We study a class of free boundary problems describing the local-nonlocal cross-diffusion models with two species in ecology. The first is a native species with nonlocal cross-diffusion, and the second is an invasion species with local cross-diffusion. Since the local and nonlocal cross-diffusions are taken into account, the models are described by the strongly coupled systems of integral and differential equations. In addition, the position of each free boundary is determined by the Stefan condition. By using the transformations of function and variable, various estimates and contraction mapping theorem, we show the global existence, uniqueness and regularity of solutions for the free boundary problems. Besides, the long time behavior of the solution for a special type of local-nonlocal cross-diffusion competition model is also obtained.

Banking research in the context of digitalization: On the transition to ecosystem business

Subject. This article discusses the tools and mechanisms for the transformation of international and Russian banking activities aimed at forming a competitive model for the functioning of a modern bank in the context of digitalization of the economy. Objectives. The article aims to develop a model for the transition of banking activities to an ecosystem business. Methods. For the study, we used analysis and synthesis, induction and deduction, generalization, abstraction, and modeling. Results. The article offers certain tools that involve the formation of a model for the transition of banking activities to ecosystem business in the context of digitalization. The proposed methodological development takes into account the review of the conceptual foundations of the banking system formation and the analysis of current trends in its functioning in the world economy. Conclusions. The digital transformation of the Russian banking system can lead to the introduction of new technologies, the creation of new banking products (including para-banking and non-banking services) and their successful promotion to the market, attracting new customers and expanding the scope of services provided, improving sources of financing and reducing costs, managing risks and negative impacts on the banking system and the economy as a whole, as well as achieving sustainable development goals and supporting the Russian economy.

In vitro analysis of a competitive inhibition model for T7 RNA polymerase biosensors

T7 RNA polymerase (T7 RNAP) biosensors, in which T7 RNAP transcribes some reporter gene or signal in response to external stimuli, have wide applications in synthetic biology and metabolic engineering. We adapted a biochemical reaction network model and used an in vitro transcription assay to determine network parameters for different T7 RNAP constructs. Under conditions where template DNA is limiting, the EC50 values of native and engineered T7 RNAPs ranged from 33 nM (29–37 95 % c.i.) to 570 nM (258–714 95 % c.i.) (wild-type T7 RNAP). The measured EC50 values were largely insensitive to free magnesium, pH, or other buffer conditions. Many biosensor configurations use a split RNAP construct, where the C-terminal (CT7) and N-terminal T7 (NT7) are fused to proximity induced dimerization modules. We used proteolysis and ion exchange chromatography to prepare a CT7 (80 kDa) product. The impact of free CT7 on T7 RNAP transcriptional activity was well described by a competitive inhibition model, with an inhibitory constant KI = 23 nM (18–28 95 % c.i.) of the sensor. These model parameters will be useful for forward modeling and design of T7 RNAP-based genetic circuits.

Modeling indirectly detected analyte peaks in ion-pair reversed-phase chromatography

In indirect detection, sample components lacking detectable properties are detected by adding a detectable component to the eluent, a so-called probe that interacts with the analytes to be detected. This study focuses on modeling indirect detection in two principally different cases. In case (1), the analyte component has the same charge as the probe component, so the probe acts as a co-ion of the analyte. In case (2), the analyte component has the opposite charge to the probe, so the probe acts as a counter-ion of the analyte.In the co-ion case (1), the analytes are alkyl sulfonates, and a competitive bi-Langmuir isotherm model was used. In the counter-ion case (2), the analytes are amines, and a modified bi-Langmuir isotherm model, incorporating ion-pairing on the stationary phase surface, was derived and applied for simulating the elution profiles. The chromatographic system comprised an XBridge Phenyl column as the stationary phase and an acetonitrile/phosphate buffer mixture with varying concentrations of sodium 2-naphthalenesulfonate as the eluent. In both cases, the detectable probe component was sodium 2-naphthalenesulfonate.The applied isotherm models successfully predicted system peaks with high agreement in both model cases, with calculated relative errors in retention times typically below 4.72 % and often below 1 %. The models were employed to predict the sensitivity of analytical methods, demonstrating excellent agreement between experimental and calculated sensitivities. These findings confirm the validity of the new adsorption isotherm model under these experimental conditions.

Densification mechanism and surface mechanical failure evolution of complex silver nanoparticle interconnection interface via pressure-assisted forced bonding

Nano-silver paste is considered the most promising die-attach material due to the small size effect and low-temperature sintering characteristics. Pressure-assisted manufacturing process can accelerate the sintering of silver (Ag) nanoparticles, forming high-quality chip interconnect interface. In this paper, molecular dynamics (MD) simulation was performed for the first time to reveal the densification mechanism and surface mechanical failure evolution behavior of complex Ag nanoparticle interconnect interface in pressure-assisted manufacturing processes. The roles of sintering pressure, sintering parameters, and nanoparticle diameter in the densification process of Ag nanoparticles were evaluated via atomic strain, mean square displacement, sintering density, and atomic layer fault analysis. External pressure can promote the formation of sintering necks through a forced mechanical action. Changing the sintering temperature can regulate the crystal evolution mode of the interface. To describe the mechanism of densification process, we propose a competitive model that correlates sintering pressure, sintering temperature, optimal steady-state sintering time, and initial system stability. Shortening the optimal sintering steady-state time of the system can make a positive contribution to sintering performance. From the perspective of improving mechanical performance, we should try to avoid the occurrence of nanoparticle morphology characteristics inside the sintered interconnect interface, which may cause sintering neck failure. The expansion and failure of random defects are beneficial for increasing mechanical reliability. This study can provide comprehensive and effective theoretical guidance for manufacturing process of high-performance chip interconnect interface.

Model of optimizing operating modes of autonomous power supply systems based on hydrogen technologies for the needs of the Arctic

The possibilities of implementing a “hydrogen transition” in the local energy sector of the Arctic are considered based on the application of the concepts of environmentally friendly technologies and socio-technological transit according to the criterion of profit maximization. The relevance of the work is due to the reform of the electric power industry, which implies a phased transition to a competitive market model, when each manufacturer will be interested in maximizing its own profit and independently determine the volume of production of electrical and thermal energy based on modern technologies. An analysis of foreign experience in creating autonomous hybrid power systems was carried out. The article discusses the possibilities of using hydrogen produced from natural gas in fuel cells in decentralized electricity and heat supply systems. The conditions for the competitiveness of the new technology in comparison with centralized systems and decentralized ones based on mini-CHP with gas turbine or gas piston engines have been determined. It has been shown that fuel cell systems using natural gas as fuel are not energy-saving technologies, but can reduce emissions of carbon dioxide and other pollutants into the environment. Massive use of fuel cells in decentralized energy supply systems is possible only if their cost is reduced to less than $100/kW.

Efficient deep learning based rail fastener screw detection method for fastener screw maintenance robot under complex lighting conditions

For the rail fastener replacement operation at night in the wilderness, the lighting conditions on the rail fastener screws are complex, due to the multiple illuminants like headlamps and flashlights at the site, making some parts of the objects appear dark or low light status in the camera. These complex lighting conditions (CLCs) interfere with the fastener recognition ability of the fastener screw detection algorithm since it can hardly maintain fixed and optimized lighting conditions of the fastener screw. We propose the LFGB-YOLO, a novel YOLO-based model that contains two principal parts: the Light-Fast part and the GB-Neck part. The Light-Fast part can reduce the network Params, FLOPs, and memory access frequency in feature extraction while keeping a high precision. The GB-Neck part can lighten the feature fusion network while maintaining the ability of accurate feature information extraction operation. Experimental results demonstrate that the LFGB-YOLO performs excellently in metrics like Recall, mAP@0.5, F1 score, and FPS, better than the performance of competitive models like YOLOv5n, YOLOv7-miny, and YOLOv8. The performance metrics of the proposed model, Recall, mAP@0.5, F1-score, and FPS are increased by 8.9%, 4%, 4.8%, and 8.1% compared with the baseline model. It shows that our work not only performs satisfactorily in detecting fastener screws under CLCs but also inspires new studies that focus on the fastener screw detection affected by environmental factors.

Advanced AI technologies for optimizing Crop–weed interaction management: A data-driven modeling approach

Artificial intelligence has become the most widely used and trusted component of research in almost all disciplines of science and technology, starting from engineering, online businesses, and industry, to biotechnology and agriculture. Successful rice crops with maximum yield and weed management are the target set by several developed as well as developing countries, based on a combination of cultural and chemical control methods. In this paper, the weed control strategy through the competition model is documented with the aid of the time-series forecasting tool of artificial intelligence. A time-dependent computational framework is built based on the real data, and by utilizing supervised learning algorithm, incorporated with delay. It is emphasized during this research that the accurate precision of time delays in competition models can help in developing the weed control strategies more efficiently and can further support in implementing these strategies as precision models for other crop protection challenges.

The value of blockchain for shipping platforms: a perspective from multi-homing and network effects

Traditional information structures within shipping supply chain platforms often grapple with inefficient decision-making processes, hindering overall efficiency. Blockchain is increasingly utilised in shipping platforms, employing decentralised distributed ledgers to transform the information architecture of supply chains, which are typically characterised by centralised management and fragmented data. However, the ceasing of operations such as TradeLens and 300cubits due to high operating costs, user privacy concerns, and multiple bets also makes investing in blockchain an obscure choice. Motivated by this dilemma about the value of blockchain to platforms, we investigate the impact of user behaviour on blockchain investments in shipping platforms through a game-theoretic model of heterogeneous platform competition. First, our results show that the relationship between the intrinsic value and the option value of blockchain is a crucial determinant of its applicability to platforms. Second, we establish that, although the network effects enhance platform profits, higher network effects exacerbate price competition and hinder the value of the blockchain. Moreover, multi-homing can weaken the strategic dependency between platforms and users, thereby reducing the profitability of platforms. Finally, it is observed that blockchain investment in shipping platforms brings more consumer surplus and social welfare, with moderate network effects and multi-homing behaviour.

An integrated BLS-Net optimized with dual PCA and improved PSO-CARS variable selection strategy to determine heavy metals in soil by XRF

Combining machine learning with X-ray fluorescence (XRF) spectroscopy is a promising solution for quantitatively analyzing heavy metal elements in soil. However, the implied linear and nonlinear interferences between spectral intensities and elemental concentrations are difficult to quantify by a single model, thus degrading the prediction performance for low-concentration elements. This paper presents a novel combined spectral variable selection and fusion modeling framework for quantitative analysis of heavy metal elements in soil XRF, which consists of the proposed Particle Swarm Optimization-based Competitive Adaptive Re-weighted Sampling (PSO-CARS) by adaptive decay strategy and Dual Principal Component Analysis-based broad learning system (BLS-Net). The proposed method is compared with advanced machine learning methods by predicting the concentrations of Cr, Cd, Cu, and Pb. The results show that PSO-CARS provides more efficient characteristic and auxiliary spectral variables for BLS-Net. BLS-Net precisely quantified concentrations of heavy metal elements by distinguishing between linear and nonlinear responses in spectra, resulting in average predictive coefficients of determination (R2) exceeding 0.995 of the different elements. The proposed method is a competitive spectral quantitative fusion modeling solution, offering a more precise and reliable analysis of heavy metal elements.

A polynomial proxy model approach to verifiable decentralized federated learning

Decentralized Federated Learning improves data privacy and eliminates single points of failure by removing reliance on centralized storage and model aggregation in distributed computing systems. Ensuring the integrity of computations during local model training is a significant challenge, especially before sharing gradient updates from each local client. Current methods for ensuring computation integrity often involve patching local models to implement cryptographic techniques, such as Zero-Knowledge Proofs. However, this approach becomes highly complex and sometimes impractical for large-scale models that use techniques such as random dropouts to improve training convergence. These random dropouts create non-deterministic behavior, making it challenging to verify model updates under deterministic protocols. We propose ProxyZKP, a novel framework combining Zero-Knowledge Proofs with polynomial proxy models to provide computation integrity in local training to address this issue. Each local node combines a private model for online deep learning applications and a proxy model that mediates decentralized model training by exchanging gradient updates. The multivariate polynomial nature of proxy models facilitates the application of Zero-Knowledge Proofs. These proofs verify the computation integrity of updates from each node without disclosing private data. Experimental results indicate that ProxyZKP significantly reduces computational load. Specifically, ProxyZKP achieves proof generation times that are 30–50% faster compared to established methods like zk-SNARKs and Bulletproofs. This improvement is largely due to the high parallelization potential of the univariate polynomial decomposition approach. Additionally, integrating Differential Privacy into the ProxyZKP framework reduces the risk of Gradient Inversion attacks by adding calibrated noise to the gradients, while maintaining competitive model accuracy. The results demonstrate that ProxyZKP is a scalable and efficient solution for ensuring training integrity in decentralized federated learning environments, particularly in scenarios with frequent model updates and the need for strong model scalability.

A novel in-silico model explores LanM homologs among Hyphomicrobium spp

Investigating microorganisms in metal-enriched environments holds the potential to revolutionize the sustainable recovery of critical metals such as lanthanides (Ln3+). We observe Hyphomicrobium spp. as part of a Fe2+/Mn2+-oxidizing consortia native to the ferruginous bottom waters of a Ln3+-enriched lake in Czechia. Notably, one species shows similarities to recently discovered bacteria expressing proteins with picomolar Ln3+ affinity. This finding was substantiated by developing an in-silico ionic competition model and recombinant expression of a homolog protein (Hm-LanM) from Hyphomicrobium methylovorum. Biochemical assays validate Hm-LanM preference for lighter Ln3+ ions (from lanthanum to gadolinium). This is comparable to established prototypes. Bioinformatics analyses further uncover additional H. methylovorum metabolic biomolecules in genomic proximity to Hm-LanM analogously dependent on Ln3+, including an outer membrane receptor that binds Ln3+-chelating siderophores. These combined observations underscore the remarkable strategy of Hyphomicrobium spp. for thriving in relatively Ln3+ enriched zones of metal-polluted environments.

Abstract C021: Tumor-associated microbiota suppresses anti-tumor immunity by driving cDC1 dysfunction in lung cancer

Abstract Type 1 conventional dendritic cells (cDC1s) are critical for initiating and sustaining tumor-reactive CD8 T cells; lack of functional cDC1s is a key driver for failed tumor immunosurveillance and immunotherapy. However, what factors cause the cDC1 insufficiency in the tumor microenvironment (TME) remain poorly understood. Commensal microbiota has emerged as a key regulator of TME and the efficacy of cancer immunotherapies. Nonetheless, most studies have focused on the intestinal microbiome; it remains underexplored whether and how the local microbiota within tumors influences the anti-cancer immunity. Using the autochthonous Kras LSL-G12D/+ ; p53 flox/flox (KP) mouse model of lung cancer, we found an increased local bacterial burden and altered bacterial composition associated with lung tumors. Importantly, this local microbiota suppresses the anti-tumor CD8 T cell response both at baseline and in response to ICB therapies. Specifically, single-cell RNA-seq analysis revealed that the microbiota shapes the functional subsets of tumor-reactive CD8 T cells. Bacterial depletion promotes the accumulation of stem-like Tpex cells (precursor of exhausted T cells) in the TME, which serve as a reservoir of effector CD8 T cells and mediate an effective response upon ICB treatment. To distinguish the function of the local microbiota within TME from the distal gut microbiota, we developed a new method of aerosolized antibiotic treatment, which selectively ablates the intra-tumoral lung microbiota while preserving the intact gut microbiota. Using this approach, we demonstrated that removal of the intra-tumoral lung microbiota markedly improves the tumor-reactive T cell response and sensitizing “cold” lung tumors to ICB therapies. Mechanistically, we identified cDC1s as a central player in microbiota-mediated suppression of anti-tumor immunity. Local microbiota diminishes cDC1 quality and quantity in the lung TME, resulting in impaired T cell priming and Tpex maintenance. At the molecular level, our data revealed that bacterial products derived from the local microbiota induce type I interferon expression in tumor-associated myeloid cells and enhance GM-CSF production from cancer cells: chronic type I interferon and GM-CSF signaling acts cooperatively to reduce the functional cDC1 population. Combining in vitro cDC1 assays, competitive bone marrow chimera experiments and cDC1-specific knockout models, we establish the causal relationships between the local microbiota, IFN-I and GM-CSF signaling in cDC1s, and the tumor response to ICB. Altogether, our study uncovered a dominant role of tumor-associated microbiota in inhibiting cDC1s and suppressing anti-tumor immunity through type I IFN and GM-CSF dependent pathways in lung cancer. Our findings not only identify the intra-tumoral microbiota as a critical therapeutic target for lung cancer treatment, but also provide new insights into the tissue-specific, context-dependent regulatory mechanisms of cDC1s in the TME. Citation Format: Qiang Dong, Chengcheng Jin. Tumor-associated microbiota suppresses anti-tumor immunity by driving cDC1 dysfunction in lung cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr C021.