Managing the human–nature relationship is key to facilitating the sustainable development of cities. The coupled coordination relationship between ecological civilization construction and urban green development and influence of spatio-temporal heterogeneity has been insufficiently studied. We used the coupled coordination degree model (CCDM) and spatio-temporal weighted model (GTWR) to analyze the relationship and heterogeneity between ecological civilization construction and UGD and ECC in each city in the Yangtze River Delta region from 2010 to 2019. The results show that: (1) UGD and ECC coordination levels fluctuated more from 2010 until 2019. There was a transition from lagging UGD and ECC to lagging ecological civilization construction and a decreasing degree of coupling coordination in the Yangtze River Delta region from east to west from near imbalance to primary coordination. (2) The Yangtze River Delta’s negative UGD and ECC effect was concentrated in northwest inland cities; the positive UGD and ECC effect was concentrated in southeast coastal cities. Thus, UGD and ECC and ecological civilization construction complement each other. This study provides a scientific basis for analyzing the coordination between ecological civilization construction and UGD and ECC and provides practical guidance for formulating and implementing urban high-quality development countermeasures. Full article

The service experience in fitness centres is important for understanding how users perceive and value the quality of the service. The service experience in fitness centres is constructed from the expectations and needs of the users and the capacity of the centre to satisfy them. This paper aims to adapt and validate the consumer experience quality (EXQ) scale (Klaus and Maklan, 2012) that analyses consumer experience in the context of fitness centres. This research was carried out in a sports centre in Spain with a sample of 413 users (52% male, 48% female) and an average age of 36.5 years. A CFA was carried out to check the fit of the model and then to check the reliability and validity of the scale, as well as the correlations with other factors. It can be seen that after different steps, the model shows good fitting as well as good reliability and validity values. The research also shows that this scale significantly predicts the satisfaction and future intentions of the service users. Therefore, managers of sports centres should consider the perceptions and positive experiences of their users in order to improve the viability of their service. Full article

With the increasing number of foreigners residing in Korea, there is a need for further research on medical service satisfaction for this demographic. Therefore, this study aimed to analyze the impact of medical service quality on customer satisfaction and revisit intention of foreigners in Korea during the COVID-19 pandemic. An online survey was conducted from 15 March to 15 May 2022 to gather data from foreign residents in Korea. A total of 201 questionnaires were analyzed using IBM SPSS Statistics 26.0 and Smart PLS3.0 for empirical analysis. The results of the study demonstrate that reliability, empathy, and COVID-19 regulations (excluding responsiveness, assurance, and tangibles) positively impact customer satisfaction with medical services. Additionally, customer satisfaction with medical services positively affects revisit intention. Furthermore, variables, such as nationality and medical department, show average differences. These findings suggest that hospitals should focus on COVID-19 prevention and the quality of medical services, while also taking into account unique characteristics, such as nationality and medical department. This study provides essential reference data for medical institutions exposed to infinite competition, informing management strategies to increase customer satisfaction and revisit intention during COVID-19. Full article

With the increasing prevalence of remote work, understanding how it impacts employee perception, psychological safety, and job performance is critical for organisations. This study aims to investigate the relationships among these variables using a cross-sectional quantitative design and a questionnaire consisting of three scales: the Worktango employee sentiment around remote work survey, the Worktango psychological health and safety survey, and Goodman and Svyantek’s performance scale. Our sample included 857 participants, both managers and non-managers, from a large insurance company. Our first two hypotheses were confirmed using non-parametric Kruskal–Wallis tests: employee sentiment around remote work as part of hybrid work is more favourable in non-sales fields and among employees who actually work remotely more often. Moreover, we found that psychological safety moderates the relationship between employee sentiment around remote work and work performance. Specifically, we observed that the positive relationship between employee sentiment around remote work and work performance is stronger when psychological safety is high. Overall, our findings contribute to the understanding of how remote work is perceived by employees and its relationship and impact on their psychological safety and job performance. These insights can help organisations develop effective policies and practices for remote work that support their employees’ well-being and performance. Full article

This paper aims to understand the significance of energy sufficiency (ES) in passenger transport for the long-term resolution of energy, climate, and sustainable development issues in Lithuania. It computes related indicators, by fixing the passenger-kilometres (pkm) travelled by various modes of transportation and applying a scenario analysis with the MESSAGE model. The findings indicated that the country’s final energy consumption (FEC) in transportation could be reduced by 21.8% by 2050 due to slowing growth rate of distances travelled by passenger car but increasing use of public transport and bicycles. This would result in a decrease in the growth rate of primary energy consumption (PEC) by half (to 0.3% a year), an increase in the use of renewable energy sources (RES) to 67.2% in the PEC structure, savings of oil products by 6.4 TWh, and savings of new electricity generation capacity by 550 MW. Furthermore, 20 MtCO_2eq. in greenhouse gas (GHG) emission reductions could be realised between 2021 and 2050. To take advantage of the potential of ES, the policy measures of passenger car demand containment and a shift to non-motorised and less polluting modes of transportation should be implemented. Furthermore, priority should be given to policy measures that encourage use of public transportation. Full article

Small and medium-sized manufacturing industries can use online reviews to add valuable user requirements, enabling them to iteratively and precisely upgrade their products based on user needs. However, a sustainable, iterative approach to product design requires the integration of a large amount of information about user requirements for accurate selection. Currently, product iterations are primarily focused on developing new solutions or upgrading a few components with little screening to see if the product iterations meet user needs. This leads to a large number of wasted resources and a shortened product lifecycle. To address these challenges, this paper proposes a sustainable iterative research method that mines user needs and provides comprehensive decision making for product design based on online reviews, using probabilistic semantic term sets (PLTS). The proposed method considers the hesitation and uncertainty among evaluating experts regarding indicators, and uses the decision-making trial and evaluation laboratory (DEMATEL) method to analyze the correlations between demand indicators. The DEMATEL correlation function is improved by reconstructing the PLTS acquisition score function and deviance into a DEMATEL correlation function, in the form of exact values using an improved binary semantic approach. This iterative design approach provides accurate feedback on how users feel about the use of product components and ensures that most product components are sustainably recycled. A drone case study is presented to demonstrate the feasibility of this approach. In-depth interviews with experts confirm that this approach is more sustainable and provides a new research methodology for sustainable iterative product design. Full article

Recently, different techniques have been applied to detect, predict, and reduce traffic congestion to improve the quality of transportation system services. Deep learning (DL) is becoming increasingly valuable for solving critiques. DL applications in transportation have been collected in several recently published surveys over the last few years. The existing research has discussed the cloud environment, which does not provide timely traffic forecasts, which is the cause of frequent traffic accidents. Thus, a solid understanding of the difficulties in predicting congestion is required because the transportation system varies widely between non-congested and congested states. This research develops a bi-directional recurrent neural network (BRNN) using Gated Recurrent Units (GRUs) to extract and classify traffic into congested and non-congested. This research uses a bidirectional recurrent neural network to simulate and forecast traffic congestion in smart cities (BRNN). Urban regions worldwide struggle with traffic congestion, and conventional traffic control techniques have failed miserably. This research suggests a data-driven approach employing BRNN for traffic management in smart cities, which uses real-time data from sensors and linked devices to control traffic more efficiently. The primary measures include predicting traffic metrics such as speed, weather, current, and accident probability. Congestion prediction performance has also been improved by extracting more features such as traffic, road, and weather conditions. The proposed model achieved better measures than the existing state-of-the-art methods. This research also explores an overview and analysis of several early initiatives that have shown promising results; moreover, it explores two potential future research approaches to increase the accuracy and efficiency of large-scale motion prediction. Full article

Considering the advantages of 3D printing, intelligent factories and distributed manufacturing, the 3D printing distributed intelligent factory has begun to rise in recent years. However, because the supply chain network of this kind of factory is very complex, coupled with the impact of customized scheduling and environmental constraints on the enterprise, the 3D printing distributed intelligent factory is facing the great challenge of realizing green supply chain networks and optimizing production scheduling at the same time, and thus a theoretical gap appears. This paper studies the hybrid optimization of green supply chain networks and scheduling of the distributed 3D printing intelligent factory. Firstly, according to the green supply chain network architecture of the distributed 3D printing intelligent factory, the cost minimization model is constructed. Secondly, mathematical software is used to solve the model, and the scheduling plan can be worked out. Finally, through the simulation analysis, it is concluded that the influencing factors such as demand, factory size and production capacity complicate the production distribution, and it can be observed that the carbon emission cost has gradually become the main factor affecting the total cost. The study has a reference value for the management decision making of the distributed 3D printing intelligent factory under the background of carbon emissions. Full article

This study aims to understand the current state of food waste in Taiwanese school food catering services and the causal configurations that make school food waste possible, as food waste management has generated considerable concern. Combining document analysis, direct weighing, observation, and semi-structured interviews, a mixed methodology was employed to collect data. In order to comprehend and quantify food waste, the amount of school lunch provided and food waste during a 35-day period were measured, as well as the inefficiency index of lunch food at the two schools. According to this study, the inefficiency index of all dishes offered at Y Senior High School by the same lunch caterer is lower than at X Girls High School. In addition, this study identifies seven factors that contribute to school food waste, comprising meal quality, rigid budget limitation, tracking and feedback system, unforeseen factors, partial eating behavior, environmental awareness, and lack of initiatives for reducing food waste. This research also proposes five strategies to improve the management of contracted catering companies in schools, thereby reducing school lunch waste from supply sources. Taiwan’s experience can serve as a model for countries in comparable situations and academically fills the gaps in the experiences of varied societies. Full article

To meet the national green development trend and realize the sustainable development of human society, the carbon emission in cold-chain distribution is costed. We plan the vehicle distribution path reasonably and optimize the distribution path locally for immediate demand to balance the economic benefits of enterprises and customer satisfaction while reducing the environmental pollution. To minimize distribution cost and maximize customer satisfaction, we design an improved ant colony algorithm to solve the initial distribution path and use the insertion method to solve the immediate customer demand. Taking the actual data of enterprise M as an example, we obtain the optimal distribution path using MATLAB software and optimize the distribution path locally according to the immediate customer demand. The results show that the proposed model and the designed algorithm are practical in satisfying the sustainable development of cold-chain logistics in China. Full article

Orthotropic steel deck (OSD) structures are widely used in the bridge deck system of rail transit bridges. Reducing the amplitude of the stress intensity factor is the most effective method to improve the fatigue life of OSD structures. In order to explore the fatigue crack propagation of the OSD structure and the factors affecting the amplitude of the structural stress intensity factor, linear elastic fracture mechanics and Paris’ law is used for theoretical support in this paper. Firstly, a cable-stayed bridge of urban rail transit is taken as the research object, a full-scale segment model of the OSD structure is designed and static and fatigue tests are carried out. Based on the test data, the fatigue life of the structure is simulated and predicted. Finally, ABAQUS and Franc3D are used to analyze the influence of parameters, such as U-rib thickness, roof thickness and diaphragm thickness, of the OSD structure on the amplitude of the stress intensity factor. The test and FEM analysis results show that the thickness of diaphragm and the height of the U-rib have little effect on the fatigue life of the OSD structure, appropriately increasing the thickness of the top plate and U-rib has a positive significance for prolonging the fatigue life of the structure. In addition, it is also of reference value to the application of sustainability and the science of sustainable development. Full article

Elephants are one of the largest animals on earth and are found in forests, grasslands and savannahs in the tropical and subtropical regions of Asia and Africa. A country like India, especially the northeastern region, is covered by deep forests and is home to many elephants. Railroads are an effective and inexpensive means of transporting goods and passengers in this region. Due to poor visibility in the forests, collisions between trains and elephants are increasing day by day. In the last ten years, more than 190 elephants died due to train accidents. The most effective solution to this collision problem is to stop the train immediately. To address this sensitive issue, a solution is needed to detect and monitor elephants near railroad tracks and analyze data from the camera trap near the intersection of elephant corridors and railroad tracks. In this paper, we have developed a fog computing-based framework that not only detects and monitors the elephants but also improves the latency, network utilization and execution time. The fog-enabled elephant monitoring system informs the train control system of the existence of elephants in the corridor and a warning light LED flashes near the train tracks. This system is deployed and simulated in the iFogSim simulator and shows improvements in latency, network utilization, and execution time compared to cloud-based infrastructures. Full article

The regional and complex air pollution problem has become a major bottleneck restricting the sustainable development of regional economies and societies. Constructing a regional collaborative governance network has become a key solution to solving the cross-regional air pollution problem. By performing a social network analysis, this paper analyzes the overall structure, internal characteristics, and evolution trend of the collaborative governance network of regional air pollution by selecting the data samples of the “2 + 26” cities from 2017 to 2021. The study found that the excellent results of air pollution control in Beijing–Tianjin–Hebei and its surrounding areas are due to precise and efficient collaboration among the “2 + 26” cities. The collaborative network formed by “2 + 26” cities based on the joint initiation of severe weather emergency responses is an important measure that can help to effectively control regional air pollution problems. There is a distinct difference in the collaborative pattern in the “2 + 26” cities air pollution collaborative governance model, showing a nested-difference network structure. Full article

River water is the most important water source that people can use. Since the 20th century, human influence on river courses has become increasingly serious. The quantitative analysis of water quality is even more difficult. According to the characteristics of Fenhe water chemistry, pollution time and pollution control factors, the contribution rate of people in the polluted water body is not clear. Therefore, this paper aims to use AI identification methods and technologies to study water pollution and provenance tracing. The combination of major elements, trace elements and stable isotopes was used to study the chemical characteristics, water quality status, and sources of pollution of the Fenhe water in the Fenhe area. Because the water contains a large number of pollution sources, it is difficult to find the source using traditional methods. Using correlation analysis, principal component analysis, multi-factor regression analysis, trend analysis and other methods, the macroelements and trace elements in the water body of the Fenhe River were analyzed. The boron sources in the Fenhe river were qualitatively and quantitatively analyzed using mass spectrometry equilibrium equation. Using the boron isotope value of the river, it showed a spatial variation of upstream (+5.1‰) < middlestream (+8.6‰) < downstream (+9.5‰) in dry season, and showed a spatial variation of upstream (+6.1‰) < downstream (+7.2‰) < middlestream (+9.0‰) in the wet season. The contribution of silicate to B is calculated by subtracting the contribution of other resources from the comprehensive contribution rate. It is found that the contribution of silicate is about 38.8%, 22% in dry season and 49.2%, 17% in wet season. The research results have provided a reliable scientific basis for the protection of water resources and pollution control in the Fenhe River Basin. Therefore, the above research confirms the role of AI identification method in the process of boron isotope geochemistry and provenance tracing of water pollution in river basins. Full article

Natural language processing (NLP) is a field in machine learning that analyses and manipulate huge amounts of data and generates human language. There are a variety of applications of NLP such as sentiment analysis, text summarization, spam filtering, language translation, etc. Since privacy documents are important and legal, they play a vital part in any agreement. These documents are very long, but the important points still have to be read thoroughly. Customers might not have the necessary time or the knowledge to understand all the complexities of a privacy policy document. In this context, this paper proposes an optimal model to summarize the privacy policy in the best possible way. The methodology of text summarization is the process where the summaries from the original huge text are extracted without losing any vital information. Using the proposed idea of a common word reduction process combined with natural language processing algorithms, this paper extracts the sentences in the privacy policy document that hold high weightage and displays them to the customer, and it can save the customer’s time from reading through the entire policy while also providing the customers with only the important lines that they need to know before signing the document. The proposed method uses two different extractive text summarization algorithms, namely LexRank and Kullback Leibler (KL) Summarizer, to summarize the obtained text. According to the results, the summarized sentences obtained via the common word reduction process and text summarization algorithms were more significant than the raw privacy policy text. The introduction of this novel methodology helps to find certain important common words used in a particular sector to a greater depth, thus allowing more in-depth study of a privacy policy. Using the common word reduction process, the sentences were reduced by 14.63%, and by applying extractive NLP algorithms, significant sentences were obtained. The results after applying NLP algorithms showed a 191.52% increase in the repetition of common words in each sentence using the KL summarizer algorithm, while the LexRank algorithm showed a 361.01% increase in the repetition of common words. This implies that common words play a large role in determining a sector’s privacy policies, making our proposed method a real-world solution for environmental sustainability. Full article