Search Results (149)

Background: Cervical cancer (CC) ranks as the third most common cancer in incidence and mortality in females worldwide. Let-7c is a tumor suppressor miRNA, and its role has been little studied in CC. Runt-related transcription factor 1 (RUNX1) is upregulated in several human cancers, such as colorectal cancer. It is a transcription factor that promotes cell proliferation, metastasis, chemotherapy resistance and angiogenesis in colorectal cancer. In this study, we performed a bioinformatic analysis to understand how Let-7c and RUNX1 are involved in the development of CC. Methods: We performed a bioinformatic analysis of Let-7c in CC using GSE and TCGA datasets from GEO, KM-plotter, miRPathDB and Enrich databases. Then, we conducted a comprehensive analysis of RUNX1’s role in CC using TCGA, GSE and HPA datasets from OncoDB, CISTROME, ExPASy, Alibaba, ALGGEN, ENCODE, IGV, GEO, KM-plotter and DiseaseMeth databases. Results: We found that Let-7c expression is decreased in CC. Interestingly, we identified a transcription factor known as RUNX1, as a potential target of Let-7c. Finally, we suggest that RUNX1 could regulate the expression of several genes, promoting CC. Conclusions: The Let-7c/RUNX1 axis promotes CC. Full article

Promoting sustainable and balanced economic growth remains a key challenge for developing countries. This study empirically investigates the impact of China’s Pilot Free Trade Zone (PFTZ) on regional economic growth from 2010 to 2023, offering important insights into how targeted policy instruments can contribute to sustainable economic growth. Employing a multiperiod difference-in-differences model and a capital–technology–marketization framework, this study finds that PFTZ implementation has a significant and direct influence on promoting provincial economic growth. The growth effects are primarily driven by improved capital flows and enhanced technological innovation. Notably, these positive effects are more pronounced in central and western Chinese provinces and regions with lagging economic development, indicating that PFTZs can serve as effective tools for reducing regional disparities. These findings provide new empirical evidence regarding the regional heterogeneity of PFTZ policy impacts and offer valuable insights into the design, timing, and spatial targeting of PFTZ initiatives in developing countries seeking to support inclusive and sustainable development across the country. Full article

Cross-border data flows are critical to the operation of global supply chains, particularly for digital enterprises such as Alibaba and Infosys. However, these flows introduce substantial challenges related to digital supply chain risk and cybersecurity management. This study examines how blockchain technology addresses these challenges within the operational contexts of Alibaba and Infosys. Unlike earlier research that often focused on sector-specific implementations or conceptual models, this study positions its findings within broader empirical evidence on blockchain-enabled supply chain governance, offering a comparative perspective that has been largely absent in prior work. Using an explanatory mixed-methods approach, the research combines thematic analysis of 85 peer-reviewed studies with in-depth case evaluations of the two firms. NVivo-based qualitative coding was applied to supporting sources, including GDPR audit reports, blockchain transaction records, and company disclosures. The findings demonstrate that blockchain adoption reduces cybersecurity breaches, enhances data integrity, and improves supply chain resilience. The study further shows how blockchain integration strengthens digital collaboration and regulatory alignment, enabling secure and uninterrupted data flows that support operational continuity and innovation. Overall, the research offers practical insights for digital enterprises and contributes to a deeper understanding of blockchain’s strategic role in cross-border data risk management. Full article

As coastal tidal flats—ecosystems of high ecological significance and socio-economic value—face accelerating degradation driven by climate change and intensified anthropogenic disturbances, there is an urgent need for efficient, automated, and scalable monitoring solutions. Traditional monitoring approaches are constrained by high implementation costs and limited spatial coverage, whereas remote sensing—particularly multispectral satellite imagery such as Sentinel-2—has emerged as a primary and widely adopted tool for large-scale environmental observation. Building upon recent advancements in cloud computing and WebGIS technologies, this study presents a web-based, interactive tidal flat extraction system implemented on Alibaba’s AI Earth platform. The system integrates multiple water indices (NDWI, mNDWI, and IWI) with a machine learning algorithm (Random Forest), and is deployed through a user-friendly interface developed using Vue.js and Leaflet, enabling flexible parameter configuration and real-time visualization of extraction results. Its front-end/back-end decoupled architecture enables non-programming users to conduct large-scale tidal flat mapping, thereby substantially lowering the technical barriers to coastal tidal flat monitoring and management in China. Full article

In HPC systems, job scheduling plays a critical role in determining resource allocation and task execution order. With the continuous expansion of computing scale and increasing system complexity, modern HPC scheduling faces two major challenges: a massive decision space consisting of tens of thousands of computing nodes and a huge job queue, as well as complex temporal dependencies between jobs and dynamically changing resource states.Traditional heuristic algorithms and basic reinforcement learning methods often struggle to effectively address these challenges in dynamic HPC environments. This study proposes a novel scheduling framework that combines GTrXL with PPO, achieving significant performance improvements through multiple technical innovations. The framework leverages the sequence modeling capabilities of the Transformer architecture and selectively filters relevant historical scheduling information through a dual-gate mechanism, improving long sequence modeling efficiency compared to standard Transformers. The proposed SECT module further enhances resource awareness through dynamic feature recalibration, achieving improved system utilization compared to similar attention mechanisms. Experimental results on multiple datasets (ANL-Intrepid, Alibaba, SDSC-SP2) demonstrate that the proposed components achieve significant performance improvements over baseline PPO implementations. Comprehensive evaluations on synthetic workloads and real HPC trace data show improvements in resource utilization and waiting time, particularly under high-load conditions, while maintaining good robustness across various cluster configurations. Full article

In the context of global e-commerce platform supply chains dominated by Alibaba and Amazon, power reconfiguration among tripartite stakeholders (platforms, manufacturers, and retailers) remains a critical yet underexplored issue in supply chain contract design. To analyze the strategic interactions between platforms, manufacturers, and retailers, as well as how platforms select the contract format within a tripartite supply chain, this study proposes a Stackelberg game-theoretic framework incorporating participation constraints to compare fixed-fee and revenue-sharing contracts. The results demonstrate that revenue-sharing contracts significantly enhance supply chain efficiency by aligning incentives across members, leading to improved pricing and sales outcomes. However, this coordination benefit comes with reduced platform dominance, as revenue-sharing inherently redistributes power toward upstream and downstream partners. The analysis reveals a nuanced contract selection framework: given the revenue sharing rate, as the additional value increases, the optimal contract shifts from the mode RR to the mode RF, and ultimately to the mode FF. Notably, manufacturers and retailers exhibit a consistent preference for revenue-sharing contracts due to their favorable profit alignment properties, regardless of the platform’s value proposition. These findings may contribute to platform operations theory by (1) proposing a dynamic participation framework for contract analysis, (2) exploring value-based thresholds for contract transitions, and (3) examining the power-balancing effects of alternative contract formats. This study offers actionable insights for platform operators seeking to balance control and cooperation in their supply chain relationships, while providing manufacturers and retailers with strategic guidance for contract negotiations in platform-mediated markets. These findings are especially relevant for large e-commerce platforms and their partners managing the complexities of contemporary digital supply chains. Full article

Urban systems are transforming as artificial intelligence (AI) evolves from automation to Agentic Urban AI (AI systems with autonomous goal-setting and decision-making capabilities), which independently define and pursue urban objectives. This shift necessitates reassessing governance, planning, and ethics. Using a conceptual-methodological approach, this study integrates urban studies, AI ethics, and governance theory. Through a literature review and case studies of platforms like Alibaba’s City Brain and CityMind AI Agent, it identifies early agency indicators, such as strategic adaptation and goal re-prioritisation. A typology distinguishing automation, autonomy, and agency clarifies AI-driven urban decision-making. Three trajectories are proposed: fully autonomous Agentic AI, collaborative Hybrid Urban Agency, and constrained Non-Agentic AI to mitigate ethical risks. The findings highlight the need for participatory, transparent governance to ensure democratic accountability and social equity in cognitive urban ecosystems. Full article

Cyanobacterial blooms and aquatic vegetation dynamics are critical indicators of freshwater ecosystem health, increasingly shaped by climate change, nutrient enrichment, and ecological restoration efforts. Here, we present an automated monitoring system optimized for small- and medium-sized lakes. This system integrates phenology-based algorithms with Sentinel-2 MSI imagery, leveraging the AI Earth (AIE) platform developed by Alibaba DAMO Academy. Applied to monitor 12 ecologically sensitive lakes and reservoirs in Jiangsu Province, China, the system enables multi-year tracking of spatiotemporal changes from 2019 to 2024. A clear north-south gradient in cyanobacterial bloom intensity was observed, with southern lakes exhibiting higher bloom levels. Although bloom intensity decreased in lakes such as Changdang, Yangcheng, and Dianshan, Ge Lake displayed fluctuating patterns. In contrast, ecological restoration efforts in Cheng and Yuandang Lakes led to substantial increases in bloom intensity in 2024, with affected areas reaching 33.16% and 33.11%, respectively. Although bloom intensity remained low in northern lakes, increases were recorded in Hongze, Gaoyou, and Luoma Lakes after 2023, particularly in Hongze Lake, where bloom coverage surged to 3.29% in 2024. Aquatic vegetation dynamics displayed contrasting trends. In southern lakes—particularly Cheng, Dianshan, Yuandang, and Changdang Lakes—vegetation coverage significantly increased, with Changdang Lake reaching 44.56% in 2024. In contrast, northern lakes, including Gaoyou, Luoma, and Hongze, experienced a long-term decline in vegetation coverage. By 2024, compared to 2019, coverage in Gaoyou, Luoma, and Hongze Lakes decreased by 11.28%, 16.02%, and 47.32%, respectively. These declines are likely linked to increased grazing pressure following fishing bans, which may have disrupted vegetation dynamics and reduced their ability to suppress cyanobacterial blooms. These findings provide quantitative evidence supporting adaptive lake restoration strategies and underscore the effectiveness of satellite-based phenological monitoring in assessing freshwater ecosystem health. Full article

This article investigates the architecture and institutional distribution of policy tools supporting open science (OS) in China. Based on a corpus of 199 policy documents comprising 25,885 policy statements, we apply an AI-assisted classification to analyze how the Chinese government mobilizes different types of tools. Using Qwen-plus, a large language model developed by Alibaba Cloud and fine-tuned for OS-related content, each policy statement is categorized into one of fifteen subcategories under three main types: supply-oriented, environment-oriented, and demand-oriented tools. Our findings reveal a strong dominance of supply-oriented tools (63%), especially investments in infrastructure, education, and public services. Demand-oriented tools remain marginal (11%), with little use of economic incentives or regulatory obligations. Environment-oriented tools show more balance but still underrepresent key components like incentive systems and legal mandates for open access. To deepen the analysis, we introduce a normalized indicator of institutional focus, which captures the relative emphasis of each policy type across administrative levels. Results show that supply-oriented tools are concentrated at top-level institutions, reflecting a top-down governance model. Demand tools are localized at lower levels, highlighting limited strategic commitment. Overall, China’s OS policy mix prioritizes infrastructure over incentives, limiting systemic transformation toward a more sustainable open science ecosystem. Full article

This paper explores the application of large language models (LLMs) to automate the evaluation of programming assignments in an undergraduate “Introduction to Programming” course. This study addresses the challenges of manual grading, including time constraints and potential inconsistencies, by proposing a system that integrates several LLMs to streamline the assessment process. The system utilizes a graphic interface to process student submissions, allowing instructors to select an LLM and customize the grading rubric. A comparative analysis, using LLMs from OpenAI, Google, DeepSeek and ALIBABA to evaluate student code submissions, revealed a strong correlation between LLM-generated grades and those assigned by human instructors. Specifically, the reduced model using statistically significant variables demonstrates a high explanatory power, with an adjusted R² of 0.9156 and a Mean Absolute Error of 0.4579, indicating that LLMs can effectively replicate human grading. The findings suggest that LLMs can automate grading when paired with human oversight, drastically reducing the instructor workload, transforming a task estimated to take more than 300 h of manual work into less than 15 min of automated processing and improving the efficiency and consistency of assessment in computer science education. Full article

A technical challenge for workload prediction in microservice systems is how to capture both the dynamic features of workload and evolving dependencies among microservices. The existing work focused mainly on modeling dynamic features without taking adequate account of evolving dependencies due to their unpredictable temporal dynamics. To fill this gap, as an illustration of bridging theory and real-work solutions by integrating machine learning with data analysis, we propose a novel framework of Temporality-Dependence Dual-Regularized Matrix Factorization (TDDRMF) by combining matrix factorization with regularization on both workload temporality and microservice dependencies. It models the workload matrix as the product of a microservice dependency matrix W and workload feature matrix X applying matrix factorization, and computes X by temporal regularization and W by low-rank norm regularization as a convex relaxation of rank minimization. To further enhance its adaptability to workload variations in real-time environments, we deploy a dynamic error detection and update mechanism. Experiments on the Alibaba dataset show that TDDRMF achieves 18.5% lower RMSE than TAMF in 10-step prediction, improving the existing matrix factorization methods in accuracy. In comparison with ML-based methods, as TDDRMF uses only 5% of their training data, it requires only a small fraction of their training time. Full article

Missing data introduce uncertainty in data mining, but existing set-valued approaches ignore frequency information. We propose unsupervised attribute reduction algorithms for multiset-valued data to address this gap. First, we define a multiset-valued information system (MSVIS) and establish $\theta$-tolerance relation to form the information granules. Then, $\theta$-information entropy and $\theta$-information amount are introduced as uncertainty measures. Finally, these two UMs are used to design two unsupervised attribute reduction algorithms in an MSVIS. The experimental results demonstrate the superiority of the proposed algorithms, achieving average reductions of 50% in attribute subsets while improving clustering accuracy and outlier detection performance. Parameter analysis further validates the robustness of the framework under varying missing rates. Full article

Image forgery detection, as an essential technique for analyzing image credibility, has experienced significant advancements recently. However, the forgery detection performance remains unsatisfactory in terms of meeting practical requirements. This is partly attributed to the limited availability of pixel-level annotated forgery samples and insufficient utilization of forgery traces. We try to mitigate these issues through three aspects: training data, network design, and training strategy. In the aspect of training data, we introduce iterative self-supervision which helps generate a large collection of pixel-level labeled single or composite forgery samples through one or more rounds of random copy-move, splicing, and inpainting, addressing the insufficient availability of forgery samples. In the aspect of network design, recognizing that characteristic anomalies are generally apparent at the boundary between true and fake regions, often aligning with image edges, we propose a new edge-guided learning module to effectively capture forgery traces at image edges. In the aspect of training strategy, we introduce progressive self-adversarial training, dynamically generating adversarial samples by gradually increasing the frequency and intensity of adversarial actions during training. This increases the detection difficulty, driving the detector to identify forgery traces from harder samples while maintaining a low computational cost. Comprehensive experiments have shown that the proposed method surpasses the leading competing methods, improving image-level forgery identification by 6.6% (from 73.8% to 80.4% on average F1 score) and pixel-level forgery localization by 15.2% (from 59.1% to 74.3% in average F1 score). Full article

The integration of intermittent wind power into modern grids necessitates highly accurate forecasting models to ensure stability and efficiency. To address the limitations of traditional backpropagation (BP) neural networks, such as slow convergence and susceptibility to local optima, this study proposes a novel hybrid framework: the Multi-Strategy Coati Optimization Algorithm (SZCOA)-optimized BP neural network (SZCOA-BP). The SZCOA integrates three innovative strategies—a population position update mechanism for global exploration, an olfactory tracing strategy to evade local optima, and a soft frost search strategy for refined exploitation—to enhance the optimization efficiency and robustness of BP networks. Evaluated on the CEC2017 benchmark, the SZCOA outperformed state-of-the-art algorithms, including ICOA, DBO, and PSO, achieving superior convergence speed and solution accuracy. Applied to a real-world wind power dataset (912 samples from Alibaba Cloud Tianchi), the SZCOA-BP model attained an R² of 94.437% and reduced the MAE to 10.948, significantly surpassing the standard BP model (R²: 81.167%, MAE: 18.891). Comparative analyses with COA-BP, BWO-BP, and other hybrid models further validated its dominance in prediction accuracy and stability. The proposed framework not only advances wind power forecasting but also offers a scalable solution for optimizing complex renewable energy systems, supporting global efforts toward sustainable energy transitions. Full article

Solid-State Drives (SSDs) enabled the implementation of real-time cloud services, with a primary focus on high performance and high availability. SSD failure prediction can improve overall system availability by preventing data loss and service interruption. SSDs employ a built-in SMART (Self-Monitoring, Analysis, and Reporting Technology) system to predict failures when certain operating parameters exceed predefined thresholds. Such univariate SMART-based models can predict a limited set of drive failures. Research in SSD failure prediction is focused on multivariate models, which can exploit the complex interactions between SMART attributes that lead to drive failure in order to detect a much larger set of failures. This paper presents an anomaly detection model, based on the Mahalanobis distance measure, which is used for the failure prediction of SSD drives. The model is able to rank the features according to their influence on failure prediction by using a forward feature selection algorithm. The proposed model is tested on a publicly available Alibaba SSD dataset, where the six highest-ranked SMART features were identified. Using this subset of SMART features, our model was able to detect 64% of failures with 81% accuracy while keeping a high precision of 96%. Full article