Modern cloud-native applications increasingly adopt microservice architecture (MSA) to achieve modularity, independent deployment, and scalability. While stateless microservices have been extensively studied in the context of autoscaling, stateful microservices (SMS) present unique challenges due to their dependency on persistent state, session continuity, and data locality. Autoscaling SMS under variable load and traffic uncertainty requires sophisticated mechanisms that transcend conventional reactive approaches. This paper provides a comprehensive review of existing methodologies for autoscaling SMS, encompassing reactive, proactive, and hybrid scaling strategies. We examine the role of machine learning (ML) and deep learning (DL) in traffic forecasting and workload prediction, the management of distributed state during scaling events, and the integration of service mesh technologies to mitigate traffic uncertainty. Key challenges including cold-start latency, state migration overhead, and quality of service (QoS) degradation during scaling transitions are discussed in depth. We further review benchmark frameworks and evaluation methodologies used to assess autoscaling systems. The paper concludes by identifying critical open research problems and future directions in this rapidly evolving domain.

Cardiovascular disease has become a major global public health concern due to its high mortality rate. As a core non-invasive diagnostic tool, electrocardiogram (ECG) automatic classification is critical for the early screening of heart diseases. To address the problems of low efficiency in manual diagnosis, the inability of single deep learning models to simultaneously capture morphological and temporal features in ECG classification, and insufficient robustness under class-imbalanced scenarios, this paper proposes a deep ensemble learning algorithm named CBR-Stacking. The algorithm uses 1D-CNN, CNN-BiLSTM, and ResNet1D as heterogeneous base models to capture local morphological features, local-temporal fused features, and deep residual features of ECG signals, respectively. Following the Stacking ensemble strategy, the outputs of base models are taken as meta-features and fed into a logistic regression meta-classifier to obtain the final classification decision. Meanwhile, Z-score normalization and class weight method are adopted for data preprocessing to alleviate amplitude differences and class imbalance. Experimental results on the PTB myocardial infarction dataset and MIT-BIH arrhythmia dataset show that the CBR-Stacking model achieves accuracies of 99.55% and 98.86% in binary classification and five-class classification tasks, respectively, outperforming single base models and traditional machine learning methods in all evaluation metrics. Moreover, the time consumption of the model ensemble stage accounts for less than 0.1%, ensuring stable running efficiency. The proposed model effectively integrates the complementary advantages of multi-architecture deep learning models, improving the accuracy, robustness, and generalization ability of ECG classification, and provides an efficient and reliable solution for intelligent auxiliary diagnosis of electrocardiograms.

With the rapid development of digital economy and the in-depth popularization of e-commerce, customer personalization has become a core competitive factor for e-commerce platforms to improve user stickiness and transaction conversion rates. Artificial Intelligence (AI), as a disruptive technological force, is reshaping the theoretical framework and practical path of e-commerce customer personalization strategies with its capabilities of massive data analysis, deep feature mining and intelligent decision-making. This paper explores the multi-dimensional impact of AI on e-commerce customer personalization strategies: first, it analyzes the inherent demand for AI-driven transformation of personalization strategies in the current e-commerce development stage, and sorts out the core AI technologies applied in the field of customer personalization. Second, it constructs the implementation framework of AI-based e-commerce customer personalization strategies, and expounds the optimization paths of personalized recommendation, personalized marketing and personalized service supported by AI. Finally, it discusses the practical challenges faced by the application of AI in e-commerce personalization, such as data privacy risks and algorithmic bias, and proposes targeted solution strategies and future research directions. This paper provides a systematic theoretical reference and practical implementation scheme for e-commerce enterprises to optimize customer personalization strategies with the help of AI technology, and has important guiding significance for the high-quality development of the e-commerce industry.

This essay explores whether photochemical film is truly making a comeback in an era dominated by digital workflows and rapidly advancing AI image generation. Instead of solely focusing on market share, the essay compares film, digital, and AI across three key dimensions: their treatment of time, their connection to the body, and their modes of control. Drawing on Gilles Deleuze's distinction between the movement-image and the time-image, the essay argues that film's grain, scratches, and mechanical imperfections disrupt smooth sensory-motor flow, creating spaces where time becomes directly perceivable. Jennifer Barker's concept of the "tactile eye" and Shane Denson's theory of discorrelated images further illustrate how photochemical images engage viewers on multiple levels, including the skin, muscles, and viscera. In contrast, digital optimization and AI prediction increasingly bypass embodied perception in favor of computational abstraction. A comparative analysis of three Manhattan skyline images—a Kodak Gold 200 negative, an iPhone RAW file, and a DALL•E-generated skyline—highlights these differences in terms of texture, depth, and the uncanny cleanliness of AI's prediction-image. While film may not regain its industrial dominance, it is emerging as a privileged medium of tactile resistance. It offers a space where time leaves its mark, where images remain partially uncontrollable, and where viewers can still sense their own existence transcending the realm of data within a feedback loop.

By deploying smoke-filled decoys to specific locations, enemy missiles can be disrupted, offering advantages such as low cost and high cost-effectiveness. This paper establishes effective shielding conditions by ensuring the distance from the point to the line segment is less than the sphere's radius. It defines decision variables such as missile coordinates, drone speed, and direction, with the objective function being to maximize shielding duration. Constraints include time, speed, and direction angle. To solve the multi-objective model, the particle swarm optimization algorithm and cross-entropy method are applied based on different target requirements to determine the global optimal solution. Data validation confirms that the proposed model and solution method accurately calculate effective concealment duration, enabling the establishment of optimal smoke decoy deployment strategies.

Precise electromagnetic field analysis is essential for optimizing the performance of magnetic bearings in high-speed rotating machinery. To overcome the computational inefficiencies associated with Finite Element Analysis (FEA), this paper proposes an exact analytical technique based on the subdomain model for radial magnetic bearings. The magnetic domain is partitioned into stator slot and air gap subregions, where Poisson’s and Laplace’s equations are solved, respectively, under the assumption of infinite permeability of the iron core. By satisfying the continuity conditions of magnetic vector potential and magnetic field intensity at the subregion interfaces, the general solutions for the magnetic field distribution are derived. Additionally, the model accounts for the influence of rotor eccentricity on the air gap flux density. The proposed analytical method is validated through comparison with FEA simulations under various operating conditions, including different bias currents and eccentricity angles. The results demonstrate that the subdomain model achieves high accuracy in predicting flux density while significantly reducing computational time, confirming its viability for the efficient design of active magnetic bearing systems.

Multi-echelon supply chain management faces unprecedented challenges from demand uncertainties, disruption risks, and complex interdependencies across network tiers. Traditional optimization approaches struggle to balance risk mitigation with operational efficiency in dynamic environments where distributional properties of returns significantly impact decision quality. This research proposes an adaptive risk-aware planning framework that leverages Distributional Reinforcement Learning (DRL) to optimize inventory positioning and order decisions across multi-echelon supply networks. Unlike conventional reinforcement learning methods that optimize expected returns, the proposed distributional approach explicitly models the full return distribution, enabling risk-sensitive policies that account for tail risks and variance. The framework incorporates a Quantile Regression Deep Q-Network architecture enhanced with attention mechanisms to capture temporal dependencies and inter-echelon coordination requirements. Experimental validation on benchmark multi-echelon supply chain scenarios demonstrates that the distributional approach achieves 18-24% reduction in total supply chain costs compared to traditional base-stock policies while maintaining service level constraints. The risk-aware policies exhibit superior robustness under demand volatility, reducing inventory variance by 31% and backorder occurrences by 42%. Furthermore, the learned policies demonstrate strong generalization capabilities across different demand distributions and network configurations. This research contributes to both theoretical understanding of risk-aware sequential decision-making in supply chains and provides practitioners with computationally tractable methods for adaptive multi-echelon planning under uncertainty.

Face recognition is a key area in biometrics with broad use in access control, e-commerce, justice, and surveillance. A standard pipeline includes preprocessing, feature extraction, and classification; this paper concentrates on preprocessing. Using MATLAB, I implement histogram equalization, mean/median filtering, smoothing, sharpening, thresholding, edge detection, and homomorphic filtering for illumination compensation. I perform face alignment (rotation, cropping, scaling) and apply geometric and gray-level normalization to produce standardized inputs. Face regions are localized via anatomical landmarks and mathematical morphology. Simulation experiments compare configurations and show that high-quality preprocessing especially illumination normalization, denoising, and alignment improves visual clarity, stabilizes input variation, and boosts downstream localization and recognition accuracy. Overall, a well-designed preprocessing module is essential for robust face recognition in diverse conditions.

In the digital age, the protection and inheritance of red culture resources are undergoing a profound paradigm shift. Currently, a vast amount of red cultural resources has been digitized, forming large-scale "digital collections." However, this collection model, centered on data preservation, easily leads to the dilemmas of "data silos" and "disconnection between teaching and application," failing to meet the advanced demands of new-era research-based learning for experiential, interactive, and contextualized engagement. This article aims to explore the internal mechanism of the transformation of red culture resource databases from "digital collections" to "educational scenes." The study posits that this transformation is not a simple technological iteration but a profound transmutation of practical logic. Based on qualitative analysis and theoretical construction, the article proposes that this transformation relies on three interconnected practical logics: First, the reconstruction logic of data, shifting from fragmented data storage to semantic association based on knowledge graphs, laying the data foundation for scene construction. Second, the construction logic of scenes, moving from static digital presentation to immersive interaction based on Extended Reality (XR) and Artificial Intelligence (AI), achieving a sense of "presence" and "personalization." Third, the empowerment logic of research-based learning, transitioning from unidirectional knowledge infusion to deep participation and value creation covering the entire cycle (pre-, mid-, post-learning), ultimately realizing value identification and internalization. The conclusions of this study attempt to provide a theoretical reference for the digitized "activation" and utilization of red cultural resources and offer a feasible path for model innovation in research-based learning under the context of the "Big Ideological and Political Course."

With the advent of the digital era, Artificial Intelligence (AI) has emerged as a pivotal force reshaping the governance framework of graduate education. Deeply integrated into every aspect of postgraduate training, AI plays a vital role in optimizing educational models. However, this AI-driven transformation faces multiple challenges including data governance, technical ethics, and stakeholder adaptation. To address these issues, innovative approaches are required—such as fostering scenario innovation through technology integration, restructuring collaborative governance models, and establishing data-driven closed-loop systems. These measures will facilitate the digital transformation of graduate education governance structures, enhance governance efficiency, and propel the field toward high-quality development.