Chirality-Dependent Dynamic Evolution for Trions in Monolayer WS2.

Monolayer transition metal dichalcogenides exhibit valley-dependent excitonic characters with a large binding energy, acting as the building block for future optoelectronic functionalities. Herein, combined with pump-probe ultrafast transient transmission spectroscopy and theoretical simulations, we reveal the chirality-dependent trion dynamics in h-BN encapsulated monolayer tungsten disulfide. By resonantly pumping trions in a single valley and monitoring their temporal evolution, we identify the temperature-dependent competition between two relaxation channels driven by chirality-dependent scattering processes. At room temperature, the phonon-assisted upconversion process predominates, converting excited trions to excitons within the same valley on a sub-picosecond (ps) time scale. As temperature decreases, this process becomes less efficient, while alternative channels, notably valley depolarization process for trions, assume importance, leading to an increase of trion density in the unpumped valley within a ps time scale. Our time-resolved valley-contrast results provide a comprehensive insight into trion dynamics in 2D materials, thereby advancing the development of novel valleytronic devices.

PRMT6 functionally associates with PRMT5 to promote colorectal cancer progression through epigenetically repressing the expression of CDKN2B and CCNG1.

BACKGROUND: Protein arginine methyltransferase 6 (PRMT6) is a type I arginine methyltransferase that asymmetrically dimethylates histone H3 arginine 2 (H3R2me2a). However, the biological roles and underlying molecular mechanisms of PRMT6 in colorectal cancer (CRC) remain unclear. METHODS: PRMT6 expression in CRC tissue was examined using immunohistochemistry. The effect of PRMT6 on CRC cells was investigated in vitro and in vivo. Mass spectrometry, co-immunoprecipitation and GST pulldown assays were performed to identify interaction partners of PRMT6. RNA-seq, chromatin immunoprecipitation, Western blot and qRT-PCR assays were used to investigate the mechanism of PRMT6 in gene regulation. RESULTS: PRMT6 is significantly upregulated in CRC tissues and facilitates cell proliferation of CRC cells in vitro and in vivo. Through RNA-seq analysis, CDKN2B (p15INK4b) and CCNG1 were identified as new transcriptional targets of PRMT6. PRMT6-dependent H3R2me2a mark was predominantly deposited at the promoters of CDKN2B and CCNG1 in CRC cells. Furthermore, PRMT5 was firstly characterized as an interaction partner of PRMT6. Notably, H3R2me2a coincides with PRMT5-mediated H4R3me2s and H3R8me2s marks at the promoters of CDKN2B and CCNG1 genes, thus leading to transcriptional repression of these genes. CONCLUSIONS: PRMT6 functionally associates with PRMT5 to promote CRC progression through epigenetically repressing the expression of CDKN2B and CCNG1. These insights raise the possibility that combinational intervention of PRMT6 and PRMT5 may be a promising strategy for CRC therapy.

ASSOCIATION OF TESSELLATION DENSITY WITH PROGRESSION OF AXIAL LENGTH AND REFRACTION IN CHILDREN: An Artificial Intelligence-Assisted 4-Year Study.

PURPOSE: To investigate fundus tessellation density (TD) and its association with axial length (AL) elongation and spherical equivalent (SE) progression in children. METHODS: The school-based prospective cohort study enrolled 1,997 individuals aged 7 to 9 years in 11 elementary schools in Mojiang, China. Cycloplegic refraction and biometry were performed at baseline and 4-year visits. The baseline fundus photographs were taken, and TD, defined as the percentage of exposed choroidal vessel area in the photographs, was quantified using an artificial intelligence-assisted semiautomatic labeling approach. After the exclusion of 330 ineligible participants because of loss to follow-up or ineligible fundus photographs, logistic models were used to assess the association of TD with rapid AL elongation (>0.36 mm/year) and SE progression (>1.00 D/year). RESULTS: The prevalence of tessellation was 477 of 1,667 (28.6%) and mean TD was 0.008 ± 0.019. The mean AL elongation and SE progression in 4 years were 0.90 ± 0.58 mm and -1.09 ± 1.25 D. Higher TD was associated with longer baseline AL (ß, 0.030; 95% confidence interval: 0.015-0.046; P < 0.001) and more myopic baseline SE (ß, -0.017; 95% confidence interval: -0.032 to -0.002; P = 0.029). Higher TD was associated with rapid AL elongation (odds ratio, 1.128; 95% confidence interval: 1.055-1.207; P < 0.001) and SE progression (odds ratio, 1.123; 95% confidence interval: 1.020-1.237; P = 0.018). CONCLUSION: Tessellation density is a potential indicator of rapid AL elongation and refractive progression in children. TD measurement could be a routine to monitor AL elongation.

Unraveling the Ultrafast Coherent Dynamics of Exciton Polariton Propagation at Room Temperature.

Exciton polaritons are widely considered as promising platforms for developing room-temperature polaritonic devices, owing to the high-speed propagation and nonlinear interactions. However, it remains challenging to explore the dynamics of exciton polaritons specifically at room temperature, where the lifetime could be as small as a few picoseconds and the prevailing time-averaged measurement cannot give access to the true nature of it. Herein, by using the time-resolved photoluminescence, we have successfully traced the ultrafast coherent dynamics of a moving exciton polariton condensate in a one-dimensional perovskite microcavity. The propagation speed is directly measured to be ∼12.2 ± 0.8 µm/ps. Moreover, we have developed a time-resolved Michelson interferometry to quantify the time-dependent phase coherence, which reveals that the actual coherence time of exciton polaritons could be much longer (nearly 100%) than what was believed before. Our work sheds new light on the ultrafast coherent propagation of exciton polaritons at room temperature.

Integrating topographic factors for effective urban sponge construction in mountainous regions: A case study.

The construction of sponge cities in mountainous areas is crucial to achieving high-quality development in these regions. Owing to rugged terrain, significant changes in elevation, and uneven distribution of cities, the construction of sponge cities in mountainous areas faces challenges such as difficulties in clearing mountains and roads, high cost, and varying regional development requirements. However, there is currently limited research focusing on the impact of terrain on sponge city construction plans. In this study, we developed an optimal low impact development (LID) system layout method based on the annual runoff control rate. This study suggests implementing LID plans in stages to balance cost-effectiveness and enhance resilience. The optimized case1_100 scheme, which takes regional differences into account, can effectively achieve a runoff control coefficient of less than 0.25 in 98.86% of the area. Remarkably, this achievement comes at a significantly lower total cost of only 1.22 billion RMB compared to the unoptimized case2_100 scheme (which does not consider regional differences) with a cost of 3.03 billion RMB. Interestingly, the optimized case1_100 plan, selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, has an LID layout that is closely related to the surface terrain. Structural equation modeling analysis indicates that terrain affects land types, which in turn impacts the surface impermeability and runoff coefficients, ultimately influencing the corresponding LID deployment plan. The coefficients of relative elevation and slope on the final plan are determined as -0.13 and -0.77, respectively, with a high overall explanatory power of 0.84. This indicates that terrain characteristics have a significant impact on the spatial patterns and surface features of typical mountainous cities in China and the optimal LID strategy largely depends on the initial terrain conditions. This study provides valuable insights for optimizing LID construction in sponge cities, particularly in the context of new mountainous urban planning.

Spatiotemporally Coupled Electron-Hole Dynamics in Two Dimensional Heterostructures.

Coulomb interactions play a crucial role in low-dimensional semiconductor materials, e.g., 2D layered semiconductors, dictating their electronic and optical properties. However, fundamental questions remain as to whether and how Coulomb interactions affect the charge or energy flow in 2D heterostructures, which is essential for their light-electricity conversions. Herein, using ultrafast spectroscopy, we report real space coupled electron-hole dynamics in 2D heterostructures. We show in (WSe2/)WS2/MoTe2 with a controlled energy gradient for the hole and a near flat band for electron transfer, the fate of the electron is controlled by the hole in coupled dynamics. The interfacial electron transfer from WS2 to MoTe2 follows the hole closely and can be facilitated or suppressed by dynamic Coulomb interaction. In parallel to the band alignment, this study reveals the critical role of Coulomb interactions on the fate of photogenerated charges in 2D heterostructures, providing experimental evidence for coupled electron-hole dynamics and a new knob for steering nanoscale charge or energy transfer process.

Four differentially expressed genes can predict prognosis and microenvironment immune infiltration in lung cancer: a study based on data from the GEO.

BACKGROUND: Lung cancer is among the major diseases threatening human health. Although the immune response plays an important role in tumor development, its exact mechanisms are unclear. MATERIALS AND METHODS: Here, we used CIBERSORT and ESTIMATE algorithms to determine the proportion of tumor-infiltrating immune cells (TICs) as well as the number of immune and mesenchymal components from the data of 474 lung cancer patients from the Gene Expression Omnibus database. And we used data from The Cancer Genome Atlas database (TCGA) for validation. RESULTS: We observed that immune, stromal, and assessment scores were only somewhat related to survival with no statistically significant differences. Further investigations revealed these scores to be associated with different pathology types. GO and KEGG analyses of differentially expressed genes revealed that they were strongly associated with immunity in lung cancer. In order to determine whether the signaling pathways identified by GO and KEGG signaling pathway enrichment analyses were up- or down-regulated, we performed a gene set enrichment analysis using the entire matrix of differentially expressed genes. We found that signaling pathways involved in hallmark allograft rejection, hallmark apical junction, hallmark interferon gamma response, the hallmark P53 pathway, and the hallmark TNF-α signaling via NF-ĸB were up-regulated in the high-ESTIMATE-score group. CIBERSORT analysis for the proportion of TICs revealed that different immune cells were positively correlated with the ESTIMATE score. Cox regression analysis of the differentially expressed genes revealed that CPA3, C15orf48, FCGR1B, and GNG4 were associated with patient prognosis. A prognostic model was constructed wherein patients with high-risk scores had a worse prognosis (p < 0.001 using the log-rank test). The Area Under Curve (AUC)value for the risk model in predicting the survival was 0.666. The validation set C index was 0.631 (95% CI: 0.580-0.652). The AUC for the risk formula in the validation set was 0.560 that confirmed predictivity of the signature. CONCLUSION: We found that immune-related gene expression models could predict patient prognosis. Moreover, high- and low-ESTIMATE-score groups had different types of immune cell infiltration.

Development and validation of a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score.

BACKGROUND: the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) dementia risk score is a recognised tool for dementia risk stratification. However, its application is limited due to the requirements for multidimensional information and fasting blood draw. Consequently, an effective and non-invasive tool for screening individuals with high dementia risk in large population-based settings is urgently needed. METHODS: a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score was developed and internally validated by a medical check-up dataset included 271,864 participants in 19 province-level administrative regions of China, and externally validated based on an independent dataset included 20,690 check-up participants in Beijing. The performance for identifying individuals with high dementia risk (CAIDE dementia risk score ≥ 10 points) was evaluated by area under the receiver operating curve (AUC) with 95% confidence interval (CI). RESULTS: the algorithm achieved an AUC of 0.944 (95% CI: 0.939-0.950) in the internal validation group and 0.926 (95% CI: 0.913-0.939) in the external group, respectively. Besides, the estimated CAIDE dementia risk score derived from the algorithm was significantly associated with both comprehensive cognitive function and specific cognitive domains. CONCLUSIONS: this algorithm trained via fundus photographs could well identify individuals with high dementia risk in a population setting. Therefore, it has the potential to be utilised as a non-invasive and more expedient method for dementia risk stratification. It might also be adopted in dementia clinical trials, incorporated as inclusion criteria to efficiently select eligible participants.

Precise Control of Selenium Functionalization in Non-Fullerene Acceptors Enabling High-Efficiency Organic Solar Cells.

Central π-core engineering of non-fullerene small molecule acceptors (NF-SMAs) is effective in boosting the performance of organic solar cells (OSCs). Especially, selenium (Se) functionalization of NF-SMAs is considered a promising strategy but the structure-performance relationship remains unclear. Here, we synthesize two isomeric alkylphenyl-substituted selenopheno[3,2-b]thiophene-based NF-SMAs named mPh4F-TS and mPh4F-ST with different substitution positions, and contrast them with the thieno[3,2-b]thiophene-based analogue, mPh4F-TT. When placing Se atoms at the outer positions of the π-core, mPh4F-TS shows the most red-shifted absorption and compact molecular stacking. The PM6 : mPh4F-TS devices exhibit excellent absorption, high charge carrier mobility, and reduced energy loss. Consequently, PM6 : mPh4F-TS achieves more balanced photovoltaic parameters and yields an efficiency of 18.05 %, which highlights that precisely manipulating selenium functionalization is a practicable way toward high-efficiency OSCs.

Interleukin-1 receptor antagonist inhibits matastatic potential by down-regulating CXCL12/CXCR4 signaling axis in colorectal cancer.

BACKGROUND: The aim of this study was to investigate the co-operative role of CXCR4/CXCL12 axis and IL-1Ra in metastatic processes mechanism by interactions between colorectal cancer cells and stromal cells in their microenvironment. METHODS: Expression of IL-1α, interleukin-1 receptor type I (IL-1 RI), CXCL12 and CXCR4 mRNA and proteins were determined by RT-PCR and Western blot. The effect of secreted level of CXCL12 by IL-1Ra on fibroblasts was measured by ELISA. CXCL12 regulate metastatic potential of colorectal cancer was evaluated by proliferation, invasion and angiogenesis assays, respectively, in which invasion and angiogenesis assays used an in vitro system consisting of co-cultured colorectal cells and stromal cells. RESULTS: IL-1α was expressed in high liver metastatic colorectal cancer cell lines (HT-29 and WiDr). The colorectal cancer cell-derived IL-1α and rIL-1α significantly promoted CXCL12 expression by fibroblasts, and this enhancing effect can be significantly inhibited by IL-1Ra (P < 0.01). CXCL12 not only enhanced the migration and proliferation of human umbilical vein endothelial cells, but also significantly enhanced angiogenesis (P < 0.01). Furthermore, the high liver-metastatic colorectal cancer cell line (HT-29), which secretes IL-1α, significantly enhanced angiogenesis compared to the low liver-metastatic cell line (CaCo-2), which does not produce IL-1α (P < 0.01). On the contrary, IL-1Ra can significantly inhibit migration, proliferation and angiogenesis (P < 0.01). CONCLUSION: Autocrine IL-1α and paracrine CXCL12 co-enhances the metastatic potential of colorectal cancer cells; IL-1Ra can inhibit the metastatic potential of colorectal cancer cells via decrease IL-1α/CXCR4/CXCL12 signaling pathways. Video Abstract.

Highly Efficient Multiple Exciton Generation and Harvesting in Few-Layer Black Phosphorus and Heterostructure.

Multiple exciton generation (MEG) in semiconductors that yields two or more excitons by absorbing one high-energy photon has been proposed to break the Shockley-Queisser limit and boost photon-to-electron conversion efficiency. However, MEG performance in conventional bulk semiconductors or later colloidal nanocrystals is far from satisfactory. Here, we report efficient MEG in few-layer black phosphorus (BP), a direct narrow bandgap two-dimensional (2D) semiconductor with layer-tunable properties. MEG performance improves with decreasing layer number and reaches 2.09Eg threshold and 93% efficiency for two-layer BP, approaching energy conservation limit. The enhanced MEG can be attributed to strong Coulomb interaction and high density of states in 2D materials. Furthermore, MEG of BP shows negligible degradation in vertical heterostructure and multielectron can be extracted by interfacial transfer with near unity yield. These results suggest 2D semiconductors as an ideal system for next generation highly efficient light emission and charge transfer devices.

Overlapping Community Detection Based on Attribute Augmented Graph.

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

Conformation-Tuning Effect of Asymmetric Small Molecule Acceptors on Molecular Packing, Interaction, and Photovoltaic Performance.

Understanding the conformation effect on molecular packing, miscibility, and photovoltaic performance is important to open a new avenue for small-molecule acceptor (SMA) design. Herein, two novel acceptor-(donor-acceptor1-donor)-acceptor (A-DA1D-A)-type asymmetric SMAs are developed, namely C-shaped BDTP-4F and S-shaped BTDTP-4F. The BDTP-4F-based polymer solar cells (PSCs) with PM6 as donor, yields a power conversion efficiency (PCE) of 15.24%, significantly higher than that of the BTDTP-4F-based device (13.12%). The better PCE for BDTP-4F-based device is mainly attributed to more balanced charge transport, weaker bimolecular recombination, and more favorable morphology. Additionally, two traditional A-D-A-type SMAs (IDTP-4F and IDTTP-4F) are also synthesized to investigate the conformation effect on morphology and device performance. Different from the device result above, here, IDTP-4F with S-shape conformation outperforms than IDTTP-4F with C-shape conformation. Importantly, it is found that for these two different types of SMA, the better performing binary blend has similar morphological characteristics. Specifically, both PM6:BDTP-4F and PM6:IDTP-4F blend exhibit perfect nanofibril network structure with proper domain size, obvious face-on orientation and enhance donor-acceptor interactions, thereby better device performance. This work indicates tuning molecular conformation plays pivotal role in morphology and device effciciency, shining a light on the molecular design of the SMAs.

Efficient hot-electron extraction in two-dimensional semiconductor heterostructures by ultrafast resonant transfer.

Energy loss from hot-carrier cooling sets the thermodynamic limit for the photon-to-power conversion efficiency in optoelectronic applications. Efficient hot-electron extraction before cooling could reduce the energy loss and leads to efficient next generation devices, which, unfortunately, is challenging to achieve in conventional semiconductors. In this work, we explore hot-electron transfer in two-dimensional (2D) layered semiconductor heterostructures, which have shown great potential for exploring new physics and optoelectronic applications. Using broadband micro-area ultrafast spectroscopy, we firmly established a type I band alignment in the WS2-MoTe2 heterostructure and ultrafast (∼60 fs) hot-electron transfer from photoexcited MoTe2 to WS2. The hot-electron transfer efficiency increases with excitation energy or excess energy as a result of a more favorable continuous competition between resonant electron transfer and cooling, reaching 90% for hot electrons with 0.3 eV excess energy. This study reveals exciting opportunities of designing extremely thin absorber and hot-carrier devices using 2D semiconductors and also sheds important light on the photoinduced interfacial process including charge transfer and generation in 2D heterostructures and optoelectronic devices.

Hardware Trojans in Chips: A Survey for Detection and Prevention.

Diverse and wide-range applications of integrated circuits (ICs) and the development of Cyber Physical System (CPS), more and more third-party manufacturers are involved in the manufacturing of ICs. Unfortunately, like software, hardware can also be subjected to malicious attacks. Untrusted outsourced manufacturing tools and intellectual property (IP) cores may bring enormous risks from highly integrated. Attributed to this manufacturing model, the malicious circuits (known as Hardware Trojans, HTs) can be implanted during the most designing and manufacturing stages of the ICs, causing a change of functionality, leakage of information, even a denial of services (DoS), and so on. In this paper, a survey of HTs is presented, which shows the threatens of chips, and the state-of-the-art preventing and detecting techniques. Starting from the introduction of HT structures, the recent researches in the academic community about HTs is compiled and comprehensive classification of HTs is proposed. The state-of-the-art HT protection techniques with their advantages and disadvantages are further analyzed. Finally, the development trends in hardware security are highlighted.

Distributed Attack Modeling Approach Based on Process Mining and Graph Segmentation.

Attack graph modeling aims to generate attack models by investigating attack behaviors recorded in intrusion alerts raised in network security devices. Attack models can help network security administrators discover an attack strategy that intruders use to compromise the network and implement a timely response to security threats. However, the state-of-the-art algorithms for attack graph modeling are unable to obtain a high-level or global-oriented view of the attack strategy. To address the aforementioned issue, considering the similarity between attack behavior and workflow, we employ a heuristic process mining algorithm to generate the initial attack graph. Although the initial attack graphs generated by the heuristic process mining algorithm are complete, they are extremely complex for manual analysis. To improve their readability, we propose a graph segmentation algorithm to split a complex attack graph into multiple subgraphs while preserving the original structure. Furthermore, to handle massive volume alert data, we propose a distributed attack graph generation algorithm based on Hadoop MapReduce and a distributed attack graph segmentation algorithm based on Spark GraphX. Additionally, we conduct comprehensive experiments to validate the performance of the proposed algorithms. The experimental results demonstrate that the proposed algorithms achieve considerable improvement over comparative algorithms in terms of accuracy and efficiency.

Detection of Algorithmically Generated Domain Names Using the Recurrent Convolutional Neural Network with Spatial Pyramid Pooling.

Domain generation algorithms (DGAs) use specific parameters as random seeds to generate a large number of random domain names to prevent malicious domain name detection. This greatly increases the difficulty of detecting and defending against botnets and malware. Traditional models for detecting algorithmically generated domain names generally rely on manually extracting statistical characteristics from the domain names or network traffic and then employing classifiers to distinguish the algorithmically generated domain names. These models always require labor intensive manual feature engineering. In contrast, most state-of-the-art models based on deep neural networks are sensitive to imbalance in the sample distribution and cannot fully exploit the discriminative class features in domain names or network traffic, leading to decreased detection accuracy. To address these issues, we employ the borderline synthetic minority over-sampling algorithm (SMOTE) to improve sample balance. We also propose a recurrent convolutional neural network with spatial pyramid pooling (RCNN-SPP) to extract discriminative and distinctive class features. The recurrent convolutional neural network combines a convolutional neural network (CNN) and a bi-directional long short-term memory network (Bi-LSTM) to extract both the semantic and contextual information from domain names. We then employ the spatial pyramid pooling strategy to refine the contextual representation by capturing multi-scale contextual information from domain names. The experimental results from different domain name datasets demonstrate that our model can achieve 92.36% accuracy, an 89.55% recall rate, a 90.46% F1-score, and 95.39% AUC in identifying DGA and legitimate domain names, and it can achieve 92.45% accuracy rate, a 90.12% recall rate, a 90.86% F1-score, and 96.59% AUC in multi-classification problems. It achieves significant improvement over existing models in terms of accuracy and robustness.

The Constipation-Relieving Property of d-Tagatose by Modulating the Composition of Gut Microbiota.

d-tagatose, a monosaccharide as well as a dietary supplement, has been reported as having a wide range of applicability in the food industry, however, the prebiotic activity, anticonstipation effects, and related mechanisms are still unclear. In this study, using the loperamide-induced constipation Kunming mice as the animal model, the effects of d-tagatose for the prevention of constipation were evaluated by gastrointestinal transit experiment and defecation experiment. Furthermore, the underlying mechanism was clarified by evaluating the change of the biochemical indicators and analyzing 16S rRNA amplicon of gut microbiota among the different mice groups. The results showed that the gastrointestinal transit rate, fecal number, and weight in six hours were significantly enhanced after the administration of d-tagatose. In addition, d-tagatose significantly increased the serum levels of acetylcholine (Ach) and substance P (SP), whereas the serum levels of nitric oxide (NO) were significantly decreased. Moreover, the 16S rRNA sequencing analysis revealed that the changes in the gut microbiota caused by constipation were restored by d-tagatose treatment. In conclusion, this study indicated that the administration of d-tagatose as a dietary supplement can effectively prevent and relieve constipation in Kunming mice, and it is a promising prebiotic candidate with constipation-relieving properties.

Mesoionic pyrido[1,2-a]pyrimidinones: Discovery of dicloromezotiaz as a lepidoptera insecticide acting on nicotinic acetylcholine receptors1,2.

A novel class of mesoionic pyrido[1,2-a]pyrimidinones has been discovered with exceptional insecticidal activity controlling a number of insect species. In this communication, we report the part of the optimization program that led to the identification of dicloromezotiaz as a potent insecticide to control a broad range of lepidoptera. Our efforts in discovery, synthesis, structure-activity relationship elucidation, and biological activity evaluation are also presented.

A Game Theory Algorithm for Intra-Cluster Data Aggregation in a Vehicular Ad Hoc Network.

Vehicular ad hoc networks (VANETs) have an important role in urban management and planning. The effective integration of vehicle information in VANETs is critical to traffic analysis, large-scale vehicle route planning and intelligent transportation scheduling. However, given the limitations in the precision of the output information of a single sensor and the difficulty of information sharing among various sensors in a highly dynamic VANET, effectively performing data aggregation in VANETs remains a challenge. Moreover, current studies have mainly focused on data aggregation in large-scale environments but have rarely discussed the issue of intra-cluster data aggregation in VANETs. In this study, we propose a multi-player game theory algorithm for intra-cluster data aggregation in VANETs by analyzing the competitive and cooperative relationships among sensor nodes. Several sensor-centric metrics are proposed to measure the data redundancy and stability of a cluster. We then study the utility function to achieve efficient intra-cluster data aggregation by considering both data redundancy and cluster stability. In particular, we prove the existence of a unique Nash equilibrium in the game model, and conduct extensive experiments to validate the proposed algorithm. Results demonstrate that the proposed algorithm has advantages over typical data aggregation algorithms in both accuracy and efficiency.