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The main objective of this research study is to optimize the printing parameters that can be used in the FDM (fusion deposition modeling) production method to obtain the lowest production time and best printing parameter of PLA (polylactic acid) filament with the tensile test. The printing parameter that can be used in FDM machines such as extruder temperature, bed temperature, layer height, printing speed, travel speed, infill, and shell count is taken into account for optimization. In addition, the tensile specimens from ASTM (American Society for Testing and Materials) D638 standard were manufactured by PLA filament with the above-modified printing parameters. The best printing parameters for PLA products were found by the time recorded during production and tensile test results after production. Thus, through this research, one can find the best PLA filament printing parameters and their timing.

With the continuous development of social networks, Weibo has become an essential platform for people to share their opinions and feelings in daily life. Analysis of users’ emotional tendencies can be effectively applied to public opinion control, public opinion surveys, and product recommendations. However, the traditional deep learning algorithm often needs a large amount of data to be retained to obtain a better accuracy when faced with new work tasks. Given this situation, a multiclassification method of microblog negative sentiment based on MAML (model-agnostic metalearning) and BiLSTM (bidirectional extended short-term memory network) is proposed to represent the microblog text word vectorization and the combination of MAML and BiLSTM is constructed. The model of BiLSTM realizes the classification of negative emotions on Weibo and updates the parameters through machine gradient descent; the metalearner in MAML calculates the sum of the losses of multiple pieces of training, performs a second gradient descent, and updates the metalearner parameters. The updated metalearner can quickly iterate when faced with a new Weibo negative sentiment classification task. The experimental results show that compared with the prepopular model, on the Weibo negative sentiment dataset, the precision rate, recall rate, and F1 value are increased by 1.68 percentage points, 2.86 percentage points, and 2.27 percentage points, respectively.

Recently, data integrity for multiagent-based big data environments has been challenging. This paper presents a blockchain-based Merkle DAG structure (M-DAG) for audit data integrity. M-DAG resolves the problem that arises due to the multicopy of a large data volume in a big data environment. It employed Boneh–Lynn–Shacham’s (BSL) signature to verify the integrity of identical multicopy on big data environments. The proposed M-DAG audit mechanism uses a consortium chain algorithm for decentralized traceability and audit to archive reliable data. The evaluation has been carried out for the efficiency of the data integrity audit.