Commercial-grade poster design demands the seamless integration of aesthetic appeal with precise, informative content delivery. Current automated poster generation systems face significant limitations, including incomplete design workflows, poor text rendering accuracy, and insufficient flexibility for commercial applications. To address these challenges, we propose PosterVerse, a full-workflow, commercial-grade poster generation method that seamlessly automates the entire design process while delivering high-density and scalable text rendering. PosterVerse replicates professional design through three key stages: (1) blueprint creation using fine-tuned LLMs to extract key design elements from user requirements, (2) graphical background generation via customized diffusion models to create visually appealing imagery, and (3) unified layout-text rendering with an MLLM-powered HTML engine to guarantee high text accuracy and flexible customization. In addition, we introduce PosterDNA, a commercial-grade, HTML-based dataset tailored for training and validating poster design models. To the best of our knowledge, PosterDNA is the first Chinese poster generation dataset to introduce HTML typography files, enabling scalable text rendering and fundamentally solving the challenges of rendering small and high-density text. Experimental results demonstrate that PosterVerse consistently produces commercial-grade posters with appealing visuals, accurate text alignment, and customizable layouts, making it a promising solution for automating commercial poster design.

Compared with botanical medicines, the intelligent identification of animal-derived medicines has developed relatively slowly and presents greater challenges due to species diversity, substantial morphological variations after processing, and the prevalence of adulteration. Tortoise plastron is a representative animal-derived medicine whose subtle morphological differences complicate reliable authentication. This study aimed to establish a standardized image-based framework to achieve accurate and reproducible identification of tortoise plastron and its common adulterants. An RGB image dataset covering Chinemys reevesii , Mauremys mutica , Ocadia sinensis , Malayemys subtrijuga , and Trachemys scripta elegans was constructed. Images were enhanced through geometric transformations, color jittering, and Gaussian blurring to simulate diverse acquisition conditions. Segmentation was performed using the SAM2 model to remove background noise and extract core regions, and classification was conducted using YOLO11 models of three scales (m, l, x). Training and validation were carried out on datasets with and without segmentation-based augmentation, each repeated three times, with average performance recorded. Statistical analysis included comparison of model performance metrics across datasets and model scales. The combined preprocessing–segmentation–classification workflow effectively captured both global and fine-grained features of tortoise plastron. YOLO11m with segmentation-based augmentation achieved the most balanced performance across accuracy and robustness. Eight technical modules, including attention-enhanced feature extraction and multi-scale pooling, contributed to improved classification precision. A practical recognition application was developed to facilitate user-friendly deployment. This study established a comprehensive digital framework for the objective identification of tortoise plastron and adulterants, transforming subjective trait-based evaluation into quantitative image analysis. The integration of advanced segmentation and multi-scale feature fusion provides a transferable paradigm for the intelligent identification of animal-derived medicines, with potential to enhance quality control and authenticity assurance in traditional Chinese medicine.

Abstract Resistance to single-agent therapy in acute myeloid leukemia (AML) often arises from compensatory epigenetic circuits. We present a causal inference framework that treats therapy as an intervention (do-operator) and defines resistance as the deviation of dual perturbations from the additive expectation of monotherapies. By modeling the causal structure of the leukemia stem-cell epigenetic landscape that maintains identity and fitness, the framework shows that synergistic and antagonistic interactions can be inferred directly from single perturbations, suggesting interaction inference may be simpler than previously assumed. We applied this model to Perturb-seq of 16 epigenetic regulators in KMT2A-rearranged AML (>31,000 single-cell transcriptomes). From single-knockout profiles, the framework correctly predicted synergistic pairs (Menin+KAT6A, Menin+DOT1L) that enhanced differentiation and induced cell death, as well as an antagonistic pair (DOT1L + PCGF1) that conferred resistance. Predictions were validated by pharmacologic inhibition and bulk RNA-seq, confirming the model’s predictive accuracy. This work presents a mechanism-guided approach for rational prioritization of epigenetic drug combinations in AML and other cancers.

The non-cooperative game between rational vehicle users will generate unnecessary costs, manifested as the gap between user equilibrium (UE) and system optimal (SO). This issue primarily arises due to the competition or the untrusted collaboration among vehicles. The traditional marginal cost pricing (MCP) method is constrained by factors such as vehicle density and communication protocols, resulting in suboptimal performance. In this paper, the immutability of Hashgraph is leveraged to enable trusted services in the Internet of Vehicles (IoV), transforming non-cooperative games into a global optimization problem, while a two-phase optimization method is proposed to achieve low-cost services. Firstly, this paper simplifies the process of determining consensus timestamps for Hashgraph and constrains the actions of participants through immutability, thereby guaranteeing trusted services more efficiently. Subsequently, this paper systematically analyzes the key factors affecting travel and network service costs to optimize them in turn. Specifically, regarding the travel costs, this paper introduces a segment shielding method in trusted scenarios to avoid Braess’s paradox. As for the network service costs of data sharing, this paper presents a latency-sensitive dynamic programming method to integrate each server’s status to optimize resource scheduling. When the vehicle density is 400, the proposed method reduces the total cost by 20.920% and improves the QoE by 122.535%. The advantages become more significant as vehicle density increases.

Data service marketplaces are pivotal elements of an intelligent society as they bridge user requirements and the realization of value from data services. Ensuring the efficient circulation of data services and driving industrial chains to create value within data service marketplaces has become a core issue in developing intelligent societies, posing an urgent concern for researchers and governments. Within the context of service ecosystems, individual data services can no longer meet the requirements of users and organizations thus moving towards convergence, which brings new governance challenges both economically and technically. This paper comprehensively explores the governance of data service marketplaces by combining insights from service ecosystems and data service value. It begins by introducing data service marketplaces. Data servitization and standardized workflows are driving the massive expansion of data services marketplaces. Then it analyzes the complexity of marketplaces under service ecosystems. Furthermore, the governance issues and research themes of data service marketplaces were elaborated at macro, medium, and micro scales. This paper provides new ideas for the value creation and development of data service marketplaces.

Governance strategies related to platform ecosystems have become a vital issue for developing a smart society, attracting governments’ and practitioners’ attention. Under the consensus of “service as a commodity” and “platform as market,” service providers, platforms, services, and various supply demand matching methods form new supply processes. These elements are continuously and uncertainly changing during supply demand matching, which makes platform ecosystems constantly evolving. However, when multiple supply demand matching methods coexist such as service composition and crossover fusion, dynamic service supply and matching cause dilemmas in the platform ecosystem governance. To this end, this article proposes a model for platform ecosystem evolution with four dynamics: 1) dynamics between ISPs (services) and platforms; 2) dynamics between users and platforms; 3) dynamics among services; and 4) dynamics between services and demands. The model considers multiple supply demand matching methods and considers both fully online services and incompletely online services. Then, according to the market operation law, we design six evaluation indexes such as demand matching rate, service diversity, and market concentration to evaluate the efficiency of the platform market. Finally, a computational experiment system is established to simulate the dynamic supply and matching processes. The experimental results show that reducing the cost of service release can increase the amount of demand and the diversity of services, and the monopoly of digital platforms is a natural trend to improve the efficiency of supply and demand. The model provides a reference for the governance of platform ecosystems and lays a foundation for further research on the value cocreation mechanism of platform ecosystems.

With the advent of mobile internet, Online-to-Offline (O2O) home services have emerged, such as home healthcare and repair services, greatly facilitating our lives. Customers book services through online platforms, and workers provide the requested services at the customers’ homes offline. However, each time workers travel to customers’ homes, they incur opportunity cost, leading to increased cost for home services. In this paper, we propose to bundle several O2O home service requests close to each other and match them with a worker. Therefore, requests that are in a bundle can split the worker’s opportunity cost. Specifically, we formalize the request bundling problem for O2O home services, which aims to minimize the overall cost of completing all requests while satisfying the time and Quality of Service(QoS) constraints. We present three Bi-layer Greedy request bundling(BiG) algorithms to solve it, including BiG-LEV, BiG-DIST, and BiG-COST. Besides, a cost accounting method based on Shapley value is designed to calculate the actual cost of each service for in-depth analysis. Finally, we illustrate a case of bundling requests for home healthcare services and compare the performance of the three algorithms.

Unpaired image-to-image (I2I) translation often requires to maximize the mutual information between the source and the translated images across different domains, which is critical for the generator to keep the source content and prevent it from unnecessary modifications. The self-supervised contrastive learning has already been successfully applied in the I2I. By constraining features from the same location to be closer than those from different ones, it implicitly ensures the result to take content from the source. However, previous work uses the features from random locations to impose the constraint, which may not be appropriate since some locations contain less information of source domain. Moreover, the feature itself does not reflect the relation with others. This paper deals with these problems by intentionally selecting significant anchor points for contrastive learning. We design a query-selected attention (QS-Attn) module, which compares feature distances in the source domain, giving an attention matrix with a probability distribution in each row. Then we select queries according to their measurement of significance, computed from the distribution. The selected ones are regarded as anchors for contrastive loss. At the same time, the reduced attention matrix is employed to route features in both domains, so that source relations maintain in the synthesis. We validate our proposed method in three different I2I datasets, showing that it increases the image quality with-out adding learnable parameters. Codes are available at https://github.com/sapphire497/query-selected-attention.

The prosperous development of Internet services such as O2O, IoT, and Web API has brought new vitality to service commercial platforms. However, these services involve online and offline business, which are widely diverse without a unified design and development standard. In addition, Internet services update frequently, which leads to repeat releases on commercial platforms. Therefore, in this paper, we present a generic method to rapidly release Internet services on commercial platforms. The method uses a highly abstract metamodel to express service business extensively and realizes service functions by executing metamodel objects. This method has wide versatility. Meanwhile, it extends the DevOps theory to solve the frequent changes of service functions during use after the release. Finally, we verified the usability of this method in the elderly healthcare domain.

With the rapid advancement of electric vehicle (EV) technology, EV has been emerging as a promising transportation due to the low carbon emission. However, the frequent and long time charging is indispensable to continue travelling. During peak hours, EVs further spend long time on the path routing because of the traffic congestion and queuing in the charging stations. Therefore, we study the EV charging scheduling problem that minimizes the total elapsed time which includes charging time for EVs through jointly optimizing the charging path routing and charging station selection in this paper. Considering the NP-hardness of this optimization problem, we propose an efficient EV charging scheduling method to obtain the optimal solution based on crowd sensing through considering the remaining energy in the battery, traffic condition, and the queue length of charging stations. Simulation results demonstrate that the proposed backtracking method based on crowd sensing can effectively reduce the total elapsed time, in comparison with the greedy algorithm.