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The incessant evolution of online platforms has ushered in a multitude of shopping modalities. Within the food industry, however, assessing the delectability of meals can only be tentatively determined based on consumer feedback encompassing aspects such as taste, pricing, packaging, service quality, delivery timeliness, hygiene standards, and environmental considerations. Traditional text data mining techniques primarily focus on consumers' emotional traits, disregarding pertinent information pertaining to the online products themselves. In light of these aforementioned issues in current research methodologies, this paper introduces the Bert BiGRU Softmax model combined with multimodal features to enhance the efficacy of sentiment classification in data analysis. Comparative experiments conducted using existing data demonstrate that the accuracy rate of the model employed in this study reaches 90.9%. In comparison to single models or combinations of three models with the highest accuracy rate of 7.7%, the proposed model exhibits superior accuracy and proves to be highly applicable to online reviews. [ABSTRACT FROM AUTHOR]

Compared with traditional place brand, place quality brand is an innovative practice that can promote high-quality development in modern manufacturing. There is, however, a lack of sufficient theoretical research on practical cases, which hampers the development of place quality brand. This study investigates how place quality brand can influence high-quality manufacturing development through an 'advanced standard system + quality certification pattern'. We employ a moderated mediation model that involves place quality brand, advanced standards, absorptive capacity, and four dimensions of high-quality manufacturing development (quality level, economic benefit, green development, and technology innovation). Partial least-squares structural equation modelling reveals that place quality brand has a significant effect on all four dimensions. Advanced standards partially mediate the relationship between place quality brand and high-quality manufacturing development. Absorptive capacity plays a moderating role between place quality brand and advanced standards; it also moderates the indirect effects of place quality brand on the four dimensions, with a stronger mediating relationship under high absorptive capacity. These findings improve our understanding of the relationship between place quality brand and high-quality manufacturing development and can inform government strategies. [ABSTRACT FROM AUTHOR]

Quality characteristics in manufacturing are correlated and do not follow a normal distribution. This study proposes a quality control method for multivariate manufacturing processes that are based on an improved Mahalanobis–Taguchi System (IMTS). The MTS has no data distribution assumptions and identifies anomalies through the Mahalanobis distance (MD). However, a covariance distance can consider the correlation between variables. Further, to address the shortcomings of the MTS in feature selection and threshold determination. A joint optimization model is proposed in this paper. Under this approach, the IMTS is employed to perform composite analyses on multiple quality characteristics and reduce dimensionality to identify abnormalities and the key quality characteristics that lead to anomalies. Further, various models are compared to construct the optimal non-parametric prediction models for each key quality characteristic. Finally, a conceptual model of process parameter optimization is proposed, which improves the Taguchi method to obtain the optimal combination of process parameters and their importance ranking, as the basis for process adjustment. By applying the proposed method, results show that the IMTS has an abnormality identification rate of 99.5%, which is higher than other methods such as MTS, support vector machine (SVM), back propagation neural network (BPNN), fast correlation-based filter solution SVM (FCBF-SVM) and sequential backward selection BPNN (SBS-BPNN). The dimensionality reduction rate is 0.5, which is higher than MTS, SVM, BPNN, and SBS-BPNN methods. The random forest (RF) algorithm is used for accurate predictions of all five key quality characteristics, the improved Taguchi method guided adjustments to manufacturing processes objectively, effectively, and economically. [ABSTRACT FROM AUTHOR]

To study the alteration of FHIT gene in tongue carcinoma Tca8113 cell line, total RNA of Tca8113 cells was extracted. The transcript of the FHIT gene of the Tca8113 cell line was detected with nest RT-PCR, and DNA was sequenced. The result showed that abnormal transcript (about 247 bp) of FHIT gene was detected in the Tca8113 cell line. The sequence analysis of the aberrant cDNAs revealed deletions of exons 1-8. Therefore, the deletion of the FHIT gene in Tca8113 cell line might support the hypothesis that the FHIT gene alteration is involved in the development of tongue carcinoma.