As ideological and political education strengthens in institutions of higher education, there is a growing need for automated methods that identify emotions and ideological stances in student comments. Existing approaches have focused on sentiment polarity and have struggled with multidimensional emotions, sentiment–ideology interactions, and indirect or metaphorical expressions, resulting in limited granularity and interpretability. To address these challenges, this study proposes an Aspect-level Coupled Reflective Network (ACR-Net) for the joint modeling of sentiment and ideology. ACR-Net extracts aspect semantics using large language models, builds fused representations through a module coupling ideology with emotion, and incorporates a sentiment weighted network and reflective calibration mechanism to capture local cues and handle implicit expressions. Experiments on real ideological and political student comments demonstrated that ACR-Net surpasses existing methods in accuracy, interpretability, and generalization. Our code is available at https://github.com/jialekang/Aspect-level-Coupled-Reflective-Network.git.

Accurate demand forecasting enables efficient supply chain management. However, two major challenges persist: (a) insufficient modeling of external conditional variables and poor capture of complex multimodal demand distributions and (b) conditional information that is fused only once at the system input, causing information decay and reduced responsiveness to event-driven shocks. To this end, the authors propose a dual-phase conditional diffusion model (DP-CDM) in which a reverse sliding diffusion along the temporal axis exploits temporal continuity to build an autoregressive mechanism, enhancing sequence modeling and avoiding structural misalignment. In addition, noise-degradation diffusion enriches multimodal probabilistic representations and improves robustness to external disturbances. A conditional embedding module aligns multimodal features by aggregating local histories, global trends, and SHapley Additive exPlanations (SHAP)-quantified external factors, which are injected throughout the denoising processes. A 3.7% improvement in fitting performance showed the effectiveness of this model in capturing event-driven demand dynamics.

Big data settings are currently evolving from classical systems that focus on supporting advanced decision-support processes—as applied to many real-life scenarios, which are typically populated by distributed and heterogeneous data sources, such as conventional distributed data warehousing environments—to cooperative information systems. Different data formats contribute to define challenging big data systems, in which the main issue consists in supporting modern big data analytics involving massive amounts of data. As a consequence, a relevant research challenge is how to efficiently integrate, process, and mine such distributed knowledge, which composes the foundations of final big data analytics processes. Starting from these considerations, in this paper the authors propose an online analytical mining-based framework for supporting big data analytics, along with a formal model underlying this framework, called Multi-Resolution Ensemble-Based Model for Advanced Knowledge Discovery in Big Data Warehouses.

This paper introduces interactive-aware multi-objective style transfer network, an innovative framework designed to enhance digital artistic workflows by balancing computational efficiency, creative autonomy, and ethical transparency. By integrating a dual-path network for content preservation and style evolution, meta-learning for rapid style adaptation, and a hybrid evaluation system, interactive-aware multi-objective style transfer network achieves 85.7% style retention across diverse domains while reducing convergence iterations by 19.2%. The framework also employs gradient-weighted class activation mapping to align artificial intelligence, decisions with designer intent, achieving 78% congruence. These advancements address key limitations in opacity, latency, and domain generalization, providing a robust solution for intelligent creative tools. This work is significant for academic researchers and information technology professionals focused on advanced data processing and human-centered design.

To improve photovoltaic (PV) power forecasting under variable meteorological conditions, this paper proposes a hybrid model combining signal decomposition, clustering, and deep learning. An improved complete ensemble empirical mode decomposition with adaptive noise method is used for multi-scale decomposition of meteorological inputs such as temperature, solar radiation, and wind direction. Sample entropy-guided K-means clustering segments signals into high, medium, and low-frequency components, with high-frequency parts further denoised using variational mode decomposition. A convolutional neural network-bidirectional long short-term memory network is then optimized by the crown porcupine optimization algorithm to fine-tune key hyperparameters. Experiments on real PV data show a 20% root mean squared error reduction (to 7.30 kW), demonstrating strong adaptability and robustness for intelligent PV scheduling.

This article introduces an innovative text summarization mechanism designed to tackle the inherent challenges of condensing lengthy and unstructured legal documents in the context of India. The authors' primary aim is to create a system proficient in extracting crucial information from these documents, producing concise summaries akin to those crafted by humans. The proposed methodology frames summarization as a binary classification problem, employing an extractive summarization technique rooted in statistical features and word vectors. The system strategically identifies summary statements from the comprehensive input text section. To automate the summarization process, they leverage various classifiers, including logistic regression, gradient boosting, and neural networks. Through this multifaceted approach, they endeavor to enhance the efficiency and accuracy of legal document summarization, addressing a critical need in the field.

Contrastive learning enables models to learn graph structural information through self-supervised learning in the absence of labels. However, real-world networks often contain both graph structural information and incomplete node attribute information. Based on this, this paper proposes a heterogeneous network text attribute fusion method based on multi-layer semantic relation contrastive learning. Firstly, the heterogeneous network components are reconstructed using semantic and thematic attribute acquisition methods at different levels, obtaining semantic representations of text attributes at various levels of abstraction. Then, the contrastive learning component of the heterogeneous network is employed to maximize the correlation between different views of the heterogeneous network, allowing the two heterogeneous networks to align in this space. This alignment helps to uncover the latent connections between text attribute features across different views, thereby achieving the fusion of information between views.