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This paper aims to examine the unique Chinese context by analyzing the city labels (e.g. smart city and eco city) used by Chinese local governments at or above the provincial capital level to represent themselves (adopted city labels) and the developmental pathways they actually pursued (adopted developmental pathways).

The authors compared the city brand choices to those anticipated based on their geographic and economic contexts (predicted city labels and developmental pathways) as well as the directives outlined in national planning documents (imposed city labels and developmental pathways). The authors identified ten main categories of city labels used to designate themselves and establish the frequency of their use based on municipal plan documents, economic and geographic data and national plan documents and policy reports, respectively.

The authors discovered that both local economic development and geographic factors, as well as top-down administrative influences, significantly impact city branding strategies in the 38 Chinese cities studied. When these models fall short in predicting adopted city labels and pathways, it is often because cities favor a service-oriented reputation over a manufacturing-focused one, and they prefer diverse, multifaceted industrial images to uniform ones.

The originality and value of this paper lie in its contribution to the academic literature on city branding by developing a predictive model for brand development at the municipal level, with explicit attention to the national-local nexus. The paper’s approach differs from existing research in the first cluster of city branding by not addressing issues of stakeholder involvement or adoption and implementation processes. Additionally, the paper’s focus on the political power dynamics at the national level and urban governance details at the municipal level provides a unique perspective on the topic. Overall, this paper provides a valuable contribution to the field of city branding by expanding the understanding of brand development and its impact on the socioeconomic environment.

The purpose of this paper is to provide a modified welding image feature extraction algorithm for rotating arc narrow gap metal active-gas welding (MAG) welding, which is significant for improving the accuracy and reliability of the welding process.

An infrared charge-coupled device (CCD) camera was utilized to obtain the welding image by passive vision. The left/right arc position was used as a triggering signal to capture the image when the arc is approaching left/right sidewall. Comparing with the conventional method, the authors’ sidewall detection method reduces the interference from arc; the median filter removes the welding spatter; and the size of the arc area was verified to reduce the reflection from welding pool. In addition, the frame loss was also considered in the authors’ method.

The modified welding image feature extraction method improves the accuracy and reliability of sidewall edge and arc position detection.

The algorithm can be applied to welding seam tracking and penetration control in rotating or swing arc narrow gap welding.

The modified welding image feature extraction method is robust to typical interference and, thus, can improve the accuracy and reliability of the detection of sidewall edge and arc position.

Enlightened by the theoretical framework of adapted protection motivation, this study aims to explore and assess the viability and resilience of hospitality consumption in the ongoing Covid-19 era as embodied in the activity of staycation, which is gaining popularity as a rare escape from the hemming in of the pandemic.

This study collects data from staycation guests in Hong Kong, which at the time was under semi-lockdown imposing compulsory quarantine for inbound visitors. The data was analyzed through structural equation modeling (SEM).

It is revealed by the study results that staycation experiences in Hong Kong are underpinned by a full mediating effect between place attachment and experience quality is performed by sense of presence, together with consolidation of experience quality and psychological detachment as tenable mediators in the research model. Aside from the utilitarian and hedonic values, hospitality and tourism consumption have been engrained with profound socio-cultural implications congenial to the collective identities, recollection and contemplation of a civilized society, with the Covid-19 era and the foreseeable future expected to be no exception.

The results of this study can serve as reference regarding better planning and development of the staycation product as effective responses against the grave repercussions of the Covid-19 pandemic by hotel and hospitality practitioners and destination marketers and managers at large. In particular, the pandemic has inadvertently availed the opportunity for the destinationalization of the hotel and hospitality sector, with rich implications for industrial consolidations and coordination with destination authorities.

The holistic structural research model derived and empirically examined entails major antecedents and consequences of the experience quality of staycation guests in locked-down Hong Kong, with the incorporation of the variables of place attachment and extended conceptualization of sense of presence accounting for the efficacy factors of the staycation takers in terms of locality and recollection appraisals, respectively. This study enriches theoretical articulations on staycation as the new normal of hospitality consumption in the lingering pandemic era.

This paper aims to investigate the contents of heavy metals Cd, Cr, Ni, Cu, Pb and Zn in the soil of vegetable production bases in the Yongnian County of Hebei Province by random sampling.

The environmental quality conditions of these heavy metals were evaluated by both the single quality index and comprehensive quality index methods.

It was concluded that the average heavy metal contents in the soil of protected vegetable fields in the Yongnian County are all under the limit values prescribed by the environmental quality evaluation standard for farmland of greenhouse vegetables production (2007), so the soil’s environmental quality is classified as first class regarding cleanliness. It is fit for the plantation of non-polluted vegetables. It is shown that there is a cumulative trend of heavy metal contents in the soil with age after analysis of a correlation coefficient between heavy metal contents and age in Yongnian’s protected fields and excessive application of fertilizers or organic fertilizers, which is the main factor leading to an increase in heavy metal contents in the soil.

It is suggested that the amount of fertilizer should be properly reduced to prevent the soil’s environmental deterioration, the vegetable planting structure should be improved and the vegetables grown in areas with weaker accumulation of heavy metals should be cultivated.

This paper aims to realize a fully distributed multi-UAV collision detection and avoidance based on deep reinforcement learning (DRL). To deal with the problem of low sample efficiency in DRL and speed up the training. To improve the applicability and reliability of the DRL-based approach in multi-UAV control problems.

In this paper, a fully distributed collision detection and avoidance approach for multi-UAV based on DRL is proposed. A method that integrates human experience into policy training via a human experience-based adviser is proposed. The authors propose a hybrid control method which combines the learning-based policy with traditional model-based control. Extensive experiments including simulations, real flights and comparative experiments are conducted to evaluate the performance of the approach.

A fully distributed multi-UAV collision detection and avoidance method based on DRL is realized. The reward curve shows that the training process when integrating human experience is significantly accelerated and the mean episode reward is higher than the pure DRL method. The experimental results show that the DRL method with human experience integration has a significant improvement than the pure DRL method for multi-UAV collision detection and avoidance. Moreover, the safer flight brought by the hybrid control method has also been validated.

The fully distributed architecture is suitable for large-scale unmanned aerial vehicle (UAV) swarms and real applications. The DRL method with human experience integration has significantly accelerated the training compared to the pure DRL method. The proposed hybrid control strategy makes up for the shortcomings of two-dimensional light detection and ranging and other puzzles in applications.

The purpose of this investigation is to identify, evaluate, integrate and summarize relevant and qualified papers through conducting a systematic literature review (SLR) on the application of recommender systems (RSs) to suggest a scholarly publication venue for researcher's paper.

To identify the relevant papers published up to August 11, 2018, an SLR study on four databases (Scopus, Web of Science, IEEE Xplore and ScienceDirect) was conducted. We pursued the guidelines presented by Kitchenham and Charters (2007) for performing SLRs in software engineering. The papers were analyzed based on data sources, RSs classes, techniques/methods/algorithms, datasets, evaluation methodologies and metrics, as well as future directions.

A total of 32 papers were identified. The most data sources exploited in these papers were textual (title/abstract/keywords) and co-authorship data. The RS classes in the selected papers were almost equally used. DBLP was the main dataset utilized. Cosine similarity, social network analysis (SNA) and term frequency–inverse document frequency (TF–IDF) algorithm were frequently used. In terms of evaluation methodologies, 24 papers applied only offline evaluations. Furthermore, precision, accuracy and recall metrics were the popular performance metrics. In the reviewed papers, “use more datasets” and “new algorithms” were frequently mentioned in the future work part as well as conclusions.

Given that a review study has not been conducted in this area, this paper can provide an insight into the current status in this area and may also contribute to future research in this field.

The purpose of this paper is to plan the layout of the production area of an industrial kitchen of a theme restaurant through the application of the systematic layout planning (SLP) methodology and the establishment of layout indicators with a specific application in the restaurant business.

The authors applied the SLP methodology in the industrial kitchen of a theme restaurant in order to plan and develop layout alternatives. Such layout alternatives were then evaluated according to a set of specific indicators consolidated based on a literature review. Finally, a multi-criteria decision-making tool was used to support the selection of the best layout alternative and suggest to the case study area.

The main results could be highlighted as: first, an area, previously unexplored, of approximately 5.40 m² ended up being used as a productive area for the operations; second, 40 percent reduction in work-in-process by removing the refrigerators of groceries and the freezer from the production area; third, indirect indicators, such as customer satisfaction and the employee’s workplace environment, have increased their performance, since customers were better served and working environment became more conducive to a good employee performance.

Most research in layout planning is typically developed in productive industrial areas, and this paper contributes to the understanding the differences of implementing SLP methodology in a food service business, such as restaurant, and proposes the utilization of specific layout indicators.

In the organizational technology acceptance (TA) decision-making process, stakeholders have many divergence opinions. Sometimes, an opposing stakeholder of a decision can stop the whole process of decision-making. In such a case, consensus may take a long time followed by a high risk. The purpose of this study is twofold. First, to find the best model with the least prediction error for the simulation of the consensus process in TA decisions. Second, to investigate the time required for the consensus process to yield the TA decision in different scenarios and to propose solutions to reduce the required time in a case study.

This study uses real-world data containing 1,186 actual observations. Stakeholders are decision-maker agents, and the observations are derived from survey data and used for simulation. Data were obtained from 126 experts in the Iranian rail freight industry. Opinion dynamics theory was used for agent-based simulation of stakeholders’ behavior. The agents interacted over time and their effects on other agents’ opinions were investigated.

The results illustrate an appropriate opinion changing model, a data-gathering method and a simulation scenario for TA consensus. The suitable model was selected after examining the advantages and disadvantages of and comparing the prediction results for different models with the real database of opinions. To reduce the consensus process time, the results suggest gathering the team members and networking with some leaders as advocators. A large number of advocators with high acceptability and continuous exchanging messages with other agents can improve the acceptance rate and have the most significant impact on other stakeholders’ opinions.

To the best of the authors’ knowledge, previous studies simulate individual TA processes. However, there is a difference between the individual TA and the organizational TA. The organizational TA requires the simultaneous decision-making of different stakeholders. In this research, the organizational TA was investigated.

The purpose of this paper is to upgrade the collaborative emergency ability of government in the tier of towns, realizing emergency resource share, emergency cost reduction and emergency efficiency improving. This paper mainly aims to solve the problem of forecasting the natural disaster happening year of every township collaborative region in Fangshan District.

First, classify the townships into five collaborative regions through grey clustering. Second, set up a grey disaster forecast model for the whole Fangshan District according the annals of disaster from 1985 to 2012, and forecast the disaster grade. Third, build a grey disaster forecast model for the collaborative regions after constructing the buffer operators of catastrophic sequence according the annals of disaster from 1949 to 2012.

The authors forecasted the happening year and flood grade of future disaster for the whole Fangshan District. The accurate degrees of both flood and drought year model are greater than 90 per cent. The accurate degree of insects calamity year is a little low, but the relative errors are all lower than 3 per cent in recent continuous three times, so in the whole, it can be used. For the collaborative regions, the authors forecasted the future disaster years of them. The accuracy rate of every model is greater than 90 per cent. The result shows that the forecast errors are acceptable.

In the models, for the purpose of good accuracy, the authors used different initial data. For example, in the forecast model for whole Fangshan District disaster year, the authors used the data from 1985 to 2012, while in the forecast model for the disaster grade of it, the authors used the data from 1949 to 2012. In the disaster year forecast model for collaborative region, the authors also used the data from 1949 to 2012. If the authors can find a model that has high accuracy rate by using all the date information, it will be better.

Township is the most basic level of government organization in China, researching on collaborative emergency in township will do help to take targeted precautions measures against calamity according to the characteristic there. At the same time realizing emergency cost reduction and emergency efficiency improving based on the advantages of emergency resource share, short rescue distance, little effects of communication destruction.

Because of the stochastic occurring of disasters, it is very important to forecast the happening time of disasters accurately. This paper forecasted the natural disaster happening time of Fangshan District through grey catastrophic model, aimed at giving decision support to related department and strengthen the disaster prevention power targetedly.

It is well known that the greater the system, the steadier it is, and the easier to forecast it. Fangshan District, Beijing, is a medium-sized and small system in regional research, while townships are small systems. It is rarely a big challenge for the authors to forecast the disaster years in Fangshan and its collaborative townships. In this paper, the authors used grey system model and Markov transfer matrix in forecasting the disaster years and the disaster grade of flood in Fangshan District. All of them are new trying to using grey system theory in disaster forecast for Fangshan District, Beijing.