Quantitative Research on Hotspots and Frontiers of Mass Customization Research

Mass customization (MC) has become an important means for enterprises to gain competitiveness, but there is a lack of systematic analysis of recent research on MC. The study conducts quantitative research on 1018 valid documents in the Web of Science database in the past ten years using bibliometric analysis and the CiteSpace software to understand MC's research hotspots and frontiers. Firstly, the data results show that keywords such as mass customization and design appear the most frequently, representing the high attention paid to them by academia. Secondly, keywords such as big data and information technology have the highest centrality value, indicating their relatively important position in MC. Finally, keywords such as industry 4.0, smart manufacturing and 3d printing are the keywords of recent MC research. This study will provide some reference for researchers to comprehensively understand the hotspots and frontiers of MC research

Spatio-temporal modeling and prediction on travel behaviour analysis

Travel behaviour analysis has been an essential topic since 1970s. Research shows that there is a strong relationship between citizens’ daily movements with activities and travel demand, so analysing human behaviours in different time and locations is quite significant to understand travel demand, and improve the transportation design. Primarily, traditional data collection methods are questionnaires and interviews. However, previous research have clear gaps: first, trip patterns extracted from the small set of data are limited and not convincing enough. This makes traditional human behaviour models inaccurate; second, the travel patterns and activity patterns discovered from previous research are limited. Moreover, previous research often ignored the impact from human activity patterns on their travel behaviours. Nowadays, it becomes feasible to obtain large trajectory data with development in sensing technologies, such as GPS trajectory data. How to use large trajectory data to extract trip patterns, forecast travel behaviours in different dimensions (spatially and temporally), and estimate dynamic travel demands using citizens’ travel behaviours have become urgent research questions to be answered. Focusing on the research questions, my PhD project analyses people’s travel behaviours from three aspects: (1) discovers spatial-temporal patterns of common movements; (2) estimate activity patterns that are not explained in trip records; (3) predict travel demand and city movement status during different periods and locations. In order to carry out the experiments, I have collected a large amount of relevant data, which are taxi trajectory data, trip diaries, house price data, bike sharing data, employment data etc. Three types of methods are employed to conduct my experiments: (i) geography and data analysis methods (Huff model, Geographical Temporal Weighted Regression, Bayesian probabilities, Distance decay function, Monte Carlo simulation, ARIMA etc.); (ii) machine learning methods (K-means Clustering, ANN, SVR, etc.); and (iii) Simulation method (Agent-based modeling and simulation). After a mass of experiments and explorations, my PhD project hastravel behaviours. The main conclusions of my PhD studies are as follows: (1) passengers have very different travel behaviours in different time and locations, which are largely related to the amount of their spare time and their income; (2) passengers’ activity can be highly inferred from their trip information (travel time, drop-off time, and drop-off location); (3) whether a trip has return trip is closely related to passengers’ travel time; (4) citizens’ daily routine, activity schedule, and trip numbers have strong regularities. In addition to the academic contributions, my research results have potential managerial contributions. For example, the model from Chapter 4 could be used to determine the locations of new infrastructure within specified conditions. The model from Chapter 5 could be used for targeted advertising. The forecasting model from Chapter 6 can be used to forecast travel demands, and then assist the department of municipal transport administration plan the scale of road construction, and the number of traffic lights

Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images

Land-use classification using remote sensing images covers a wide range of applications. With more detailed spatial and textural information provided in very high resolution (VHR) remote sensing images, a greater range of objects and spatial patterns can be observed than ever before. This offers us a new opportunity for advancing the performance of land-use classification. In this paper, we first introduce an effective midlevel visual elements-oriented land-use classification method based on “partlets,” which are a library of pretrained part detectors used for midlevel visual elements discovery. Taking advantage of midlevel visual elements rather than low-level image features, a partlets-based method represents images by computing their responses to a large number of part detectors. As the number of part detectors grows, a main obstacle to the broader application of this method is its computational cost. To address this problem, we next propose a novel framework to train coarse-to-fine shared intermediate representations, which are termed “sparselets,” from a large number of pretrained part detectors. This is achieved by building a single-hidden-layer autoencoder and a single-hidden-layer neural network with an L0-norm sparsity constraint, respectively. Comprehensive evaluations on a publicly available 21-class VHR land-use data set and comparisons with state-of-the-art approaches demonstrate the effectiveness and superiority of this paper

Nitrification and denitrification processes in a zero-water exchange aquaculture system: characteristics of the microbial community and potential rates

​The zero-water exchange aquaculture has been identified as a promising method of farming to decrease the environment pressure of aquaculture and to increase profits. However, the ecological functions (e.g., nitrogen removal) and microbial biodiversity of the zero-water exchange pond aquaculture system are relatively understudied. In the present study, the zero-water exchange pond aquaculture system were constructed to investigated the microbial communities, sediment potential nitrification and denitrification production rates. And five functional genes (AOA amoA, AOB amoA, nirS, nosZ, and hzsB) were used to quantify the abundance of nitrifying and denitrifying microorganisms. The results showed that the sediment of the system had excellent potential nitrification-denitrification performance, with potential nitrification and denitrification rate were 149.77-1024.44 ng N g−1 h−1 and 48.32-145.01 ng N g−1 h−1, respectively. The absolute copy numbers of nitrogen functional genes and total bacterial 16S rRNA were 1.59×105-1.39×109 and 1.55×1010-2.55×1010copies g−1, respectively, with the dominant phyla, i.e., Proteobacteria, Actinobacteriota, Chloroflexi, Cyanobacteria, and Firmicutes. The relative abundances of the genera related to nitrification and denitrification, varied from 0.01% to 0.79% and from 0.01% to 15.54%, respectively. The potential nitrification rate was positively related to the sediment TOC concentration; and the potential denitrification rate had a positive correlation with sediment nitrate concentration. The genera Bacillus positively correlated with sediment NO3‐-N concentration, whereas Flavobacterium and Shewanella positively correlated with sediment NH4+-N concentration, which could be the functional bacteria for nitrogen removal. These findings may shed light on quantitative molecular mechanisms for nitrogen removal in zero-water exchange ponds, providing a sustainable solution to nitrogen pollution problem in the freshwater aquaculture ecosystems