Search

In practical assembly enterprises, customization and rush orders lead to an uncertain demand environment. This situation requires managers and researchers to configure an assembly line that increases production efficiency and robustness. Hence, this work addresses cost-oriented mixed-model multimanned assembly line balancing under uncertain demand, and presents a new robust mixed-integer linear programming model to minimize the production and penalty costs simultaneously. In addition, a reinforcement learning-based multiobjective evolutionary algorithm (MOEA) is designed to tackle the problem. The algorithm includes a priority-based solution representation and a new task-worker-sequence decoding that considers robustness processing and idle time reductions. Five crossover and three mutation operators are proposed. The $Q$ -learning-based strategy determines the crossover and mutation operator at each iteration to effectively obtain Pareto sets of solutions. Finally, a time-based probability-adaptive strategy is designed to effectively coordinate the crossover and mutation operators. The experimental study, based on 269 benchmark instances, demonstrates that the proposal outperforms 11 competitive MOEAs and a previous single-objective approach to the problem. The managerial insights from the results as well as the limitations of the algorithm are also highlighted.

Existing algorithms of dish recognition mainly focus on accuracy with predefined classes, thus limiting their application scope. In this paper, we propose a practical two-stage dish recognition framework (DRNet) that yields a tradeoff between speed and accuracy while adapting to the variation in class numbers. In the first stage, we build an arbitrary-oriented dish detector (AODD) to localize dish position, which can effectively alleviate the impact of background noise and pose variations. In the second stage, we propose a dish reidentifier (DReID) to recognize the registered dishes to handle uncertain categories. To further improve the accuracy of DRNet, we design an attribute recognition (AR) module to predict the attributes of dishes. The attributes are used as auxiliary information to enhance the discriminative ability of DRNet. Moreover, pruning and quantization are processed on our model to be deployed in embedded environments. Finally, to facilitate the study of dish recognition, a well-annotated dataset is established. Our AODD, DReID, AR, and DRNet run at about 14, 25, 16, and 5 fps on the hardware RKNN 3399 pro, respectively.

ABSTRACTHigh viability and further adipogenic differentiation of adipose-derived stem cells (ADSCs) are fundamental for engraftment and growth of the transplanted adipose tissue. It has been demonstrated that extracellular matrix (ECM) regulates cell proliferation and differentiation by interacting with ERK1/2 signalling pathway. In this study, we prepared autologous decellularized extracellular matrix (d-ECM) and explored its effect on the proliferation and adipogenic ability of ADSCs in low serum culture. We found that 2% foetal bovine serum (FBS) in growth medium inhibited cell viability and DNA replication, and decreased mRNA and protein levels of PPARγ and C/EPBα compared with 10% FBS. Correspondingly, after 14-days adipogenic induction, cells cultured in 2% FBS possessed lower efficiency of adipogenesis and expressed less adipocyte differentiation markers ADIPOQ and aP2. On the contrary, the d-ECM-coated substrate continuously promoted the expression of PPARγ, and regulated the phosphorylation of ERK1/2 in different manners during differentiation. Pretreatment with ERK1/2 inhibitor PD98059 neutralized the effects of d-ECM, which suggested d-ECM might regulate the adipogenesis of ADSCs through ERK1/2-PPARγ pathway. In addition, d-ECM was revealed to regulate the transcription and expression of stemness-associated genes, such as OCT4, NANOG and SOX2, in the undifferentiated ADSCs, which might be related to the initiation of differentiation.

The transportation sector will be critical to the successful pursuit of the carbon neutrality goal in China. Electric vehicles (EVs) have seemed to be an important solution, for reducing carbon emissions, and residents’ support will be essential to achieve the extensive use of EVs. Thus, this study aimed to explore Macao residents’ willingness to buy (WTB) and pay for (WTP) EVs through 406 questionnaires, and to determine the influencing factors. The results showed that convenience, actual cost and environmental benefits are the consideration factors for residents to purchase EVs. It was found that 90.14% of respondents were willing to buy an EV. For WTP, however, the proportion fell to 56.71%. The environmental benefits, battery range, charging convenience and safety of EVs, and consumer income are all positively correlated with respondents’ WTB. For their WTP, potential pollution from fuel vehicles, driving frequency and environmental benefits of EVs were more important. Using the contingent valuation method (CVM), it was estimated that respondents’ WTP value is about 15041.10 MOP (much lower than market value gap: 50000 MOP). In summary, the existing policies in Macao are insufficient to guide and improve the residents’ WTB and WTP. In the future, the government should strengthen publicity, especially to young consumers, and give EV owners some privileges, to stimulate residents’ WTB and WTP. In addition, in the context of carbon neutrality, more attention should be paid to cleaner power generation in Macao.