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Between 1840s and 1940s, 27 occidental concessions have been created in several cities in China which represented difficult signs and memories for Chinese. Nowadays, these territories are experiencing a joint phenomenon of heritagization and tourismification which makes them experimental theaters for modern urban life and identity. Taking the former concessions of Tianjin as place study, the purpose of this study is to analyze the role of the heritage and tourism in the former concessions in city branding and more specifically the actors, approaches and products of this phenomenon.

This research draws on the comparison and analysis of two place studies in China. The authors base their analysis on semi-structured interviews in Chinese with previously identified stakeholders. In all, 20 individuals, including developers, public authority representatives, business owners, academics and conservation association members, were interviewed. This research was completed, updated and triangulated by content analysis of Web-based materials; official documents such as urban plans, guidelines and urban and tourism strategies collected during the fieldwork, as well as non-intrusive spatial observations of the concession and its various developments.

The results of this study show that the heritage in the former concessions has become an attractive tool for the city branding through tourism development, often led by the public actors with the participation of private entrepreneurs.

This study looks at the hybrid dimensions of the former concessions in China. It provides a better understanding of the co-action of heritage and tourism in the processes of territorial rehabilitation, which contributes to both the practitioners and researchers in this domain.

A fiber Bragg grating (FBG) sensor was designed to measure the door gap of automobile bodies.

The gap sensor was designed through a combination of the sliding wedge and cantilever beam, involving a magnetic force installation and arc structure of the force transmission point. Moreover, the sliding block adopted an anti-magnetic and wear-resistant material and the temperature compensation of the two FBGs was conducted. The magnetic force and contact stress of the sensor were examined to ensure that the sensor exhibited a certain magnetic attraction force and fatigue life. The performance of the gap sensor was examined experimentally.

The sensor could measure gaps with dimensions of 5 mm to 11 mm, with a sensitivity and measurement accuracy of 150.9 pm/mm and 0.0063% F.S., respectively. Moreover, the sensor exhibited a small gap sensitivity, with a repeatability error of 0.15%, anti-creep properties and magnetic interference abilities.

The sensor is compact and easy to install, as well as use for multiple sensor locations, with a maximum size of 43 mm, a mass of 26 g and installation type of magnetic suction. It can be used for high-precision static and dynamic measurements of the door inner clearance with a resolution of 0.013 mm to improve the efficiency of internal clearance on-line analysis and assembly quality inspection.

The garment industry will be one of the major beneficiaries of advances in smart manufacturing, as it is highly labor-intensive and heavily depends on labor force. Manipulating robots in human environments has made great strides in recent years. However, the main research has focused on rigid, solid objects and core capabilities such as grasping, placing remain a challenging problem when dealing with soft textiles. The experimental results indicate that adopting the proposed bionic soft finger will provide garment manufacturers with smart manufacturing capabilities. Then, the purpose of this paper is to utilize the flexibility of the soft finger to transfer fabric layer by layer without damage in garment automation.

In this paper, a new way to separate layer by layer pieces of fabric has been inspired by the rise of soft robotics and their applications in automation. Fabric gripping is accomplished by wiping deformation and pinching the fabric. A single fabric piece is separated from cutting pile by the soft finger in four steps: making an arch by pressing, wiping deformation, grasping and separating, and placing.

The case study demonstrated that the soft finger arrangement for automated grasping of fabric pieces of a garment can be successfully applied to delicate fabric. A combination of cloth shape and weight determines the number of soft fingers. In addition, the soft finger was tested on different types of fabrics to determine its performance and application capabilities. The technology may be used to produce clothing intelligently in the future, such as intelligent stacking, intelligent transportation and intelligent packaging, to increase clothing industry productivity.

An industrial bionic soft finger gripping system is proposed in this paper for application in the field of fabric automatic manipulation. A piece of fabric could be picked up and released layer by layer from a stack by the proposed gripper without creating any damage to it. Soft grippers have the right proportion of softness and rigidity like a human being. A soft finger has a potential affinity for soft materials such as fabrics without damaging either their surface or their properties.

China has showed its eagerness in using its economic strength in the very recent years. Is China going to be a major sanctioning state like the United States or the European Union? This chapter argues that although there have been an increasing number of economic sanctions imposed by China with its expanding national interests and growing diplomatic problems, China will still keep a low profile in using economic sanctions because of the restraining factors such as the WTO rules, inherent problems in its economy, the pursuit of a good reputation and its strategy of peaceful development. Thus the frequency and tactics of using economic sanctions may vary according to its rising economy and changing international situation, but that will go in a very limited way.

The role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM).

In the present study, the authors use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing.

The present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices.

A comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originality.

Optimization algorithms can differ in performance for a specific problem. Hybrid approaches, using this difference, might give a higher performance in many cases. This paper presents a hybrid approach of Genetic Algorithm (GA) and Ant Colony Optimization (ACO) specifically for the Integrated Process Planning and Scheduling (IPPS) problems. GA and ACO have given different performances in different cases of IPPS problems. In some cases, GA has outperformed, and so do ACO in other cases. This hybrid method can be constructed as (I) GA to improve ACO results or (II) ACO to improve GA results. Based on the performances of the algorithm pairs on the given problem scale. This proposed hybrid GA-ACO approach (hAG) runs both GA and ACO simultaneously, and the better performing one is selected as the primary algorithm in the hybrid approach. hAG also avoids convergence by resetting parameters which cause algorithms to converge local optimum points. Moreover, the algorithm can obtain more accurate solutions with avoidance strategy. The new hybrid optimization technique (hAG) merges a GA with a local search strategy based on the interior point method. The efficiency of hAG is demonstrated by solving a constrained multi-objective mathematical test-case. The benchmarking results of the experimental studies with AIS (Artificial Immune System), GA, and ACO indicate that the proposed model has outperformed other non-hybrid algorithms in different scenarios.

Concerning that limited explanation exists examining the function of corporate governance in trust processing within entrepreneurial network development, the purpose of this paper is to explore trust evolution and the role of corporate governance in an entrepreneurial network.

This paper makes an innovative exploration based on the case study of NVC Lighting Holding Limited.

It proposes that in the initial period of network relationship which is based on entrepreneur’s individual social network and embodies sole social network embeddness, entrepreneurial network relies more on affective trust than contractual trust. When stepping into extending period of network relationship which reflects separate embeddedness of social and market network, however, entrepreneurial network has an equal reliance on both affective trust and contractual trust. With further development, when ushering in the phase of maturity which undergoes superimposing embeddedness of both social and market network, entrepreneur network inclines to rely more heavily on affective trust than contractual trust. During the whole process, it can be found that the reliance of entrepreneurial network on trust has the tendency to transfer from affective trust to contractual trust. Furthermore, decreasing of equity ratio of founders and strengthening of controlling right heterogeneity in the corporate governance have facilitated the transfer process and the entrepreneurs’ authority has restraining effect on the evolution of the process.

Through case study, this paper presents the trust evolution process in different stages of entrepreneurial network. Another important theoretic contribution of this paper is that it reveals the function of corporate governance in trust processing within entrepreneurial network development.

This paper describes the organic geochemical characteristics and their roles on barium enrichment in the No. 2 Coal from Huanglong Jurassic Coalfield, China. A total of 18 bench samples were taken from Huangling Mine 2. The average content of barium (3701 mg/kg) was about 23 times higher than that of common world coals. Terrestrial higher plants were the main coal-forming parent material. Relying on the parameters of OEP, Pr/Ph and so on, there is little correlation between organic geochemical characteristics and barium enrichment. Therefore, organic material has little influence on the process of coal-forming and the enrichment of barium.

With the fierce competition in the cold chain logistics market, achieving and maintaining excellent customer satisfaction is the key to an enterprise's ability to stand out. This research aims to determine the factors that affect customer satisfaction in cold chain logistics, which helps cold chain logistics enterprises identify the main aspects of the problem. Further, the suggestions are provided for cold chain logistics enterprises to improve customer satisfaction.

This research uses the text mining approach, including topic modeling and sentiment analysis, to analyze the information implicit in customer-generated reviews. First, latent Dirichlet allocation (LDA) model is used to identify the topics that customers focus on. Furthermore, to explore the sentiment polarity of different topics, bi-directional long short-term memory (Bi-LSTM), a type of deep learning model, is adopted to quantify the sentiment score. Last, regression analysis is performed to identify the significant factors that affect positive, neutral and negative sentiment.

The results show that eight topics that customer focus are determined, namely, speed, price, cold chain transportation, package, quality, error handling, service staff and logistics information. Among them, speed, price, transportation and product quality significantly affect customer positive sentiment, and error handling and service staff are significant factors affecting customer neutral and negative sentiment, respectively.

The data of the customer-generated reviews in this research are in Chinese. In the future, multi-lingual research can be conducted to obtain more comprehensive insights.

Prior studies on customer satisfaction in cold chain logistics predominantly used questionnaire method, and the disadvantage of which is that interviewees may fill out the questionnaire arbitrarily, which leads to inaccurate data. For this reason, it is more scientific to discover customer satisfaction from real behavioral data. In response, customer-generated reviews that reflect true emotions are used as the data source for this research.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.