A 70 MPa Type IV hydrogen composite pressure vessel (CPV) was instrumented with embedded Fiber Bragg Grating (FBG) sensors to realize in situ strain monitoring during hydraulic fatigue cycles. FBG arrays were co-wound with carbon fibers during the filament winding process, forming an integrated multi-point sensing network within the composite layers. Hydraulic fatigue tests were conducted under pressure cycling between 2 MPa and 87.5 MPa, reaching 48,000 cycles. The embedded FBG sensors were able to stably record cyclic strain evolution with peak amplitudes of approximately 6000 με in the hoop layer and 3500 με in the helical layer under hydraulic cycling. The hoop layers exhibited gradually decreasing strain amplitudes from the inner to outer regions, while the helical layer maintained stable signal performance. Analysis of fiber survival times indicated that the FBGs embedded in helical layers remained functional throughout the entire test, confirming the long-term monitoring capability under high-pressure oil environments. This study demonstrates a practical embedded-sensing approach compatible with the filament-winding process, providing experimental support for fatigue-life evaluation and in-service safety monitoring of high-pressure hydrogen storage vessels.

We have developed an innovative framework for analyzing online public sentiment, referred to as the Online Public Opinion Situation Assessment (OPOSA) model. This model employs Deep S-parse Autoencoder (DSAE) and Time-Series Modeling Convolutional Neural Networks (TBSMA-CNN) to effectively segment vast populations of online usersąłsuch as those on social media platforms or e-commerce websitesąłinto distinct groups based on shared preferences and evolving sentiment trends. Central to the model's design is a geometry-based feature selection approach that enables the identification of key aspects of public opinion over time. These aspects, which are influenced by a range of temporal and contextual factors, including weakly supervised learning, are captured and iteratively refined to provide a clearer picture of online opinion dynamics. Unlike traditional methods that rely on probabilistic multi-topic distributions, our model uses DSAE to uncover hierarchical representations of opinion data and leverages TBSMA-CNN to track how public sentiment evolves across time. The synergy of DSAE and TBSMA-CNN enhances the model's capacity to represent users' opinions in a hidden feature space, revealing deeper insights into how individual preferences shift over time. To uncover connections between users with similar opinion trajectories, we introduce a graph-based framework. This framework enables the identification of opinion communitiesąłgroups of users bound by shared opinion patterns or trends, such as shifts in stance or collective movements toward particular issues. To ensure that the model's insights are highly personalized, we incorporate a ranking mechanism that recommends content, discussions, or articles that align with the opinion dynamics of each identified community. This helps ensure that recommendations not only resonate with users' current interests but also stay relevant to their changing preferences. We validated the performance of this approach using a large-scale dataset, which included millions of users and their engagement histories. The results highlighted the model's ability to effectively detect and classify distinct opinion communities, offering large-scale, highly personalized content recommendations that are both timely and contextually relevant to each user's evolving preferences and opinions.