This study proposes a novel hybrid deep learning framework that integrates a Large Language Model (LLM) with a Transformer architecture for stock price forecasting. The research addresses a critical theoretical gap in existing approaches that empirically combine textual and numerical data without a formal understanding of their interaction mechanisms. We conceptualize a prompt-based LLM as a mathematically defined signal generator, capable of extracting directional market sentiment and an associated confidence score from financial news. These signals are then dynamically fused with structured historical price features through a noise-robust gating mechanism, enabling the Transformer to adaptively weigh semantic and quantitative information. Empirical evaluations demonstrate that the proposed Hybrid LLM–Transformer model demonstrates promising improvement over a Vanilla Transformer baseline, reducing the Root Mean Squared Error (RMSE) by 5.28% (p = 0.003). Moreover, ablation and robustness analyses confirm the model’s stability under noisy conditions and its capacity to maintain interpretability through confidence-weighted attention. The findings contribute to theoretical exploration of LLM–Transformer interactions, offering a step toward explainable, noise-resilient, and semantically enriched financial forecasting systems.

Studies have established that an ideal environment is critical for maximizing a student's learning potential. For educators, monitoring student behavior, engagement, and psychological state is essential for ensuring effective instruction. This paper introduces an automated learning analytics system designed to assist teachers by analyzing these parameters and providing actionable feedback. The system utilizes multiple cameras in conjunction with deep learning and computer vision to record and analyze classroom sessions, assessing student movement, gestures, and posture to generate summary reports. The framework integrates several high-performance models to achieve this. For facial recognition, the FaceNet technique is employed, demonstrating 99.63% accuracy on frontal profiles from labeled images and 95.12% on the YouTube Faces DB dataset. Concurrently, a Convolutional Neural Network (CNN) is used for expression monitoring, achieving competitive performance in identifying five distinct emotions. Furthermore, body posture is tracked and analyzed using OpenPifPaf, which shows a high-performance index greater than 0.8 on multiple benchmarks, including the CrowdPose and COCO datasets. By combining these technologies, the system provides a robust and accurate solution for automated, real-time student behavior monitoring.

Purpose: This paper analyzes the market response to annual performance projections announcements at the initial listing of A-share telecom companies in China between 2023 and mid-2025. Design and Methodology: A quantitative approach is employed, which is based on an event-study design. Abnormal returns and accumulation abnormal returns are computed in and around announcement dates, and are focused on the. [-1, +1] event windows. Findings: These findings indicate a high level of asymmetric market response. The abnormal returns caused by bad news are larger and more persistent compared to those caused by good news. The abnormal accumulated bad news returns are approximately -3.4% in the [-1, +1] window. It is increased in the face of macroeconomic uncertainty in early 2024. Evidence indicated that there is anticipatory pessimism in that investors expect to receive bad news at least partially in advance of announcements. Implications: The results outline the contribution of the behavioural aspects to the equity market of china. These findings can help policymakers and investors to learn more about how investors behave in the new financial markets and how they respond to the market.

The escalating global prevalence of diabetes renders effective screening for Diabetic Retinopathy (DR) indispensable to prevent irreversible vision loss. Although deep learning models, particularly Convolutional Neural Networks (CNNs), attain diagnostic accuracy comparable to that of human experts, their black-box nature erodes clinical trust. To harmonize accuracy with interpretability, this paper proposes a novel CNN architecture that reformulates DR grading as a regression task. By substituting traditional dense layers with a Global Average Pooling (GAP) layer, our approach substantially reduces model complexity and training time while enabling the generation of Regression Activation Maps (RAMs). These RAMs deliver visual explanations by precisely highlighting the pathological regions that underpin the model's predictions. Evaluated on the Kaggle Diabetic Retinopathy Detection dataset, our model—through the replacement of dense layers with Global Average Pooling—markedly lowers model complexity while delivering diagnostic performance on par with baseline models employing fully-connected layers. The resulting system provides a simpler, more precise, and transparent alternative for automated medical screening, directly associating predictions with clinically relevant features.

The rapid global deployment of solar photovoltaic (PV) technology presents a significant and often overlooked challenge: the effective management of end-of-life (EoL) solar panels. This issue is particularly acute in developing and emerging economies, where established reverse logistics infrastructure is often lacking. A critical limitation in current academic literature is the oversimplified forecasting of EoL waste streams, which fails to account for the dynamic interplay of socio-economic, policy, and environmental variables. To bridge this gap, we propose a novel decision support system (DSS) for the design of a sustainable reverse logistics network. Our system uniquely integrates a hybrid, multi-factorial forecasting model combining a SARIMAX time series approach with a Gradient Boosting Regressor to provide a robust prediction of waste volume. The output of this predictive engine dynamically informs a multi objective, mixed integer linear programming (MILP) model, which optimizes the network design to simultaneously minimize economic costs and environmental impacts. Our findings demonstrate that this integrated framework offers a methodologically more realistic and adaptable approach for strategic planning than existing models, as validated through simulated scenarios. Our study provides an actionable blueprint for policymakers and industry leaders to proactively build a resilient and circular economy for a sustainable energy future.