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Place quality branding has been identified as an innovative practice that promotes high-quality product development. However, a lack of sufficient theoretical research on practical cases, which presents a major challenge for place quality branding. This study constructs a multiple mediation model to investigate the mechanisms and pathways of place quality brands to improve product quality based on the perspective of advanced standards leading technological innovation. Partial least-squares structural equation modeling reveals significant direct effects of place quality brands on product quality, as well as indirect effects of place quality brands on product quality that are mediated by either advanced standards or technological innovation, or through multiple mediation pathways involving both advanced standards and technological innovation. This study enriches the connotation of place quality brand and provides decision-making guidance for governments and firms to more effectively develop place quality brands. Government can cultivate place quality brands around advanced standards and technological innovation. Meanwhile, firms can enhance their ability to apply advanced standards, better identify and grasp innovation opportunities. [ABSTRACT FROM AUTHOR]

Cd contamination, especially in farmland soil, can pose serious threats to human health as well as ecological security. Stabilization is an important strategy for agricultural soil Cd remediation. In this study, a Cd-resistant strain (Cupriavidus B-7) was isolated and loaded onto cow manure (CDB), rice straw (RSB) and pine wood biochar (PB) to investigate its effects on Cd stabilization by a 60-day pot experiment. Results indicated that the Cupriavidus B-7-loaded biochar (labelled as CDBB, PBB and RSBB) reduced the CaCl2-extractable Cd by 43.06–59.78%, which was significantly superior to individual applications of Cupriavidus B-7 and biochar. Likewise, the soil physicochemical properties, urease, catalase and phosphatase activities were improved, indicating improved soil health. Consequently, dry weights of pakchoi's shoot and root were increased by 938.9–1230.9% and 149.1–281.2%, respectively, by applying CDBB, PBB and RSBB. Meanwhile, the Cd accumulation in pakchoi shoots decreased by 38.06–50.75%. Notably, the RSBB exhibited an optimal performance on pakchoi growth promotion and Cd accumulation alleviation. The structural equation model indicated the synergistic effect on pakchoi growth promotion and Cd accumulation decreased between biochar and Cupriavidus B-7. Our research provides some new insights into the development of strategies for green and sustainable remediation of Cd-contaminated soil. Highlights: A Cd resistant Cupriavidus B-7 (B-7) was isolated from copper mining soil Biochar, B-7 and their conbination were used to remediate Cd polluted soil Biochar loaded B-7 outperformed individual treatments for Cd stabilization Biochar loaded B-7 alleivated the Cd uptake and promoted pakchoi growth [ABSTRACT FROM AUTHOR]

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]

Data imbalance is a common problem in classification tasks. The Mahalanobis-Taguchi system (MTS) has proven to be promising due to its lack of requirements for data distribution. The MTS is a binary classifier. However, multi-classification problems are more common in real life and the diversity of categories may further aggravate the difficulty of classifying imbalanced data. Imbalanced multi-classification has become an important research topic. To improve the performance of MTS in imbalanced multi-classification, we propose an algorithm called optimized binary tree MTS (Optimized BT-MTS). Mahalanobis space (MS) construction, feature selection, and threshold determination are incorporated in a unified classification framework, and joint optimization is carried out according to the principles of maximizing separability, signal-to-noise ratio, dimensionality reduction, and minimizing misclassification cost. Experimental results on several datasets show that the method can significantly reduce the overall misclassification cost and improve the performance of imbalanced data multi-classification. [ABSTRACT FROM AUTHOR]

Purpose: Different amendments have diverse adsorption capacity for heavy metals. The objectives of this study were to evaluate adsorption efficiency of different kinds of amendments for heavy metals and to provide theoretical guidance for alleviation of heavy metals. Materials and methods: The indoor oscillating experiments were carried out to investigate the adsorption efficiency of five amendments (i.e., lime, illite ilerite, ground phosphate rock, chitin, and corn straw charcoal) for adsorption capacity of heavy metals (i.e., Zn, Pb, and Cd). Results and discussion: The adsorption efficiency of lime for Zn and Cd was 99.64% and 99.99% followed by chitin with adsorption efficiency of 74.94% and 81.63% respectively. The adsorption efficiency of amendments for Pb, Zn, and Cd was increased gradually with adsorption time. The adsorption efficiency has gradually reached a stable stage after certain time. In addition to adsorption of Pb by corn straw charcoal, the other four materials (i.e., lime, illite ilerite, ground phosphate rock, and chitin) have reached equilibrium when pH value was 3. The adsorption efficiency of lime (1%) for Zn was highest (99.98%). The adsorption efficiency of other materials for Pb was enhanced with increase in dosage except for chitin; however, the unit adsorption capacity was not decreased. Conclusions: It is concluded that adsorption efficiency of lime and chitin for heavy metals such as Zn, Pb, and Cd was more than illite ilerite, ground phosphate rock, and corn straw charcoal, which can be considered as an active sorption materials. [ABSTRACT FROM AUTHOR]