Search Results (42)

In the process of Chinese-style modernisation, the socially and culturally coordinated development of cities in the Yangtze River Economic Belt is important for promoting regional coordinated development, enhancing the balance of public services, and strengthening cultural soft power. This study used quantitative methods, including the construction of an indicator system, spatial correlation analysis, and Zipf’s rank-size rule, on data from 2011 to 2021 to analyse the capacity for coordinated social and cultural development and assessed the spatial distribution characteristics of the Yangtze River Economic Belt. The study found that the overall level of social and cultural coordination among the cities in the Yangtze River Economic Belt steadily improved; however, significant regional disparities still exist, particularly in areas such as social security and cultural integration. Spatially, a “high in the east, low in the west” pattern is observed, with the Yangtze River Delta city cluster leading development, the midstream cluster playing a supportive role, and the Chengdu–Chongqing city cluster showing significant internal disparities. Core cities such as Shanghai, Hangzhou, Wuhan, and Chengdu demonstrated driving effects in areas such as culture, education, and healthcare; however, some peripheral cities remain underdeveloped. This study suggests the need to enhance the development of the Yangtze River’s culture, promote the development of cultural industry clusters, foster the integration of various business models, leverage scientific and educational resources, optimise the cultural consumption market, and achieve the coordinated development of the social and cultural sectors, thereby enabling the Yangtze River Economic Belt to play a greater role in Chinese-style modernisation. Full article

Customer complaints play an important role in the adjustment of business operations and improvement of services, particularly in the aviation industry. However, extracting adequate textual features to perform a multi-label classification of complaints remains a difficult problem. Current multi-label classification methods applied to complaint texts have not been able to fully utilize complaint information, and little research has been performed on complaint classification in the aviation industry. Therefore, to solve the problems of insufficient text feature extraction and the insufficient learning of inter-feature relationships, we constructed a multi-label classification model (MAG, or multi-feature attention gradient boosting decision tree classifier) for civil aviation service quality complaint texts. This model incorporates multiple features and attention mechanisms to improve the classification accuracy. First, the BERT (Bidirectional Encoder Representations from Transformers) model and attention mechanisms are used to represent the semantic and label features of the text. Then, the Text-CNN (a convolutional neural network) and BiLSTM (bidirectional long short-term memory) multi-channel feature extraction networks are used to extract the local and global features of the complaint text, respectively. Subsequently, a co-attention mechanism is used to learn the relationship between the local and global features. Finally, the travelers’ complaint texts are accurately classified by integrating the base classifiers. The results show that our proposed model improves the multi-label classification accuracy, outperforming other modern algorithms. We demonstrate how the label feature representation based on association rules and the multi-channel feature extraction network can enrich textual information and more fully extract features. Overall, the co-attention mechanism can effectively learn the relationships between text features, thereby improving the classification accuracy of the model and enabling better identification of travelers’ complaints. This study not only effectively extracted text features by integrating multiple features and attention mechanisms, but also constructed a targeted feature word set for complaint texts based on the domain-specific characteristics of the civil aviation industry. Furthermore, by iterating the basic classifier using a multi-label classification model, a classifier with higher accuracy was successfully obtained, providing strong technical support and new practical paths for improving the civil aviation service quality and complaint management. Full article

Data center architecture plays an important role in effective server management network systems. Load balancing is one such data architecture used to efficiently distribute network traffic to the server. In this paper, we incorporated the load-balancing technique used in cloud computing with power business intelligence (BI) and cloud load based on the queueing theoretic approach. This model examines a bulk arrival and batch service queueing system, incorporating server overloading and underloading based on the queue length. In a batch service system, customers are served in groups following a general bulk service rule with the server operating between the minimum value $\mathit{‘}\mathit{a}\mathit{’}$ and the maximum value $\mathit{‘}b\mathit{’}$. But in certain situations, maintaining the same extreme values of the server is difficult, and it needs to be changed according to the service request. In this paper, server load balancing is introduced for a batch service queueing model, which is the capacity of the server that can be adjusted, either increased or decreased, based upon the service request by the customer. On service completion, if the service request is not enough to start any of the services, the server will be assigned to perform a secondary job (vacation). After vacation completion based upon the service request, the server will start regular service, overload or underload. Cloud computing using power BI can be analyzed based on server load balancing. The function that determines the probability of the queue size at any given time is derived for the specified queueing model using the supplementary variable technique with the remaining time as the supplementary variable. Additionally, various system characteristics are calculated and illustrated with suitable numerical examples. Full article

The immigrant guildhall was an important landscape element in the cities of Sichuan during the Qing Dynasty. This study explores the attribute characteristics, spatial distribution characteristics, and spatial layout rules of immigrant guildhalls by examining the Chengdu prefectural city during the Qing Dynasty as an example, using qualitative logical induction, GIS spatial analysis, and spatial syntax. The results show the following. (1) Immigrant guildhalls were civic buildings with a low level of support for the official regime. They had a grand scale, prominent building height, and unique architectural shape. (2) The layout rules for immigrant guildhalls included keeping immigrant guildhalls a certain distance from the dominant administrative, cultural, and educational facilities. Immigrant guildhalls had limited participation in the construction of the main axis of the city but partially participated in the construction of the secondary axis of the city. Immigrant guildhalls were mainly located in areas with well-developed businesses and convenient road transportation. (3) The official adopts two methods, control and utilization, to enable immigrant guildhalls to participate in the construction of the urban landscape order. These findings reveal the characteristics of the layout of immigrant guildhalls in the city during the Qing Dynasty and provide a basis for understanding the construction order of traditional Chinese cities. Full article

Understanding tourist profiles and behaviors during health pandemics is key to better preparedness for unforeseen future outbreaks, particularly for tourism and hospitality businesses. This study develops and applies a novel data analytics methodology to gain insights into the health risk reduction behavior of restaurant diners/patrons during their dining out experiences in a pandemic. The methodology builds on data relating to four constructs (question categories) and measurements (questions and attributes), with the constructs being worry, health risk prevention behavior, health risk reduction behavior, and demographic characteristics. As a unique contribution, the methodology generates a behavioral typology by identifying risk profiles, which are expressed as one- and two-level decision rules. For example, the results highlighted the significance of restaurants’ adherence to cautionary measures and diners’ perception of seclusion. These and other factors enable a multifaceted analysis, typology, and understanding of diners’ risk profiles, offering valuable guidance for developing managerial strategies and skill development programs to promote safer dining experiences during pandemics. Besides yielding novel types of insights through rules, another practical contribution of the research is the development of a public web-based analytics dashboard for interactive insight discovery and decision support. Full article

To achieve sustainable development, it is necessary to consider the business model adjustment of the industry in advance, from the development stage to the mature stage. In China, strategic emerging industries are industries that achieve technological breakthroughs, but such industries often have the characteristics of high investment, high technology, high risk, high returns, and long research and development times. This type of industry relies heavily on national resource support from the exploration period to the development period, but its high-profit characteristics also attract policy bias from the governments of other countries internationally. Therefore, understanding the resource requirements of such industries in different periods in advance will help the government to adjust resource allocation and strategic layout through policy means. This will facilitate the smooth transition of the entire industry from the development period to the mature period, and achieve its overall sustainable development. To assist the government in achieving reasonable predictions for policy adjustments, this study focuses on the biopharmaceutical industry, which is one of the representatives of the strategic emerging industries in China’s Yangtze River Delta. Considering that policies are not used in a single manner, and that the observation period needs to span the development and platform periods of the industry, the traditional Qualitative Comparative Analysis method (QCA) does not consider the analysis of data from multiple periods. Therefore, this study innovatively uses the Multi-Time Qualitative Comparative Analysis method (mtQCA), adding the dimension of time change and exploring the policy configuration logic behind the differences in local industrial innovation performance. Extracting general rules from specific policy configuration patterns is meaningful for a better analysis and resolution of complex, dynamic management issues, which will promote the sustainable development of strategic emerging industries. Full article

Intelligent Vehicle–Infrastructure Collaboration Systems (i-VICS) put forward higher requirements for the real-time security of dynamic traffic information interaction. It is difficult to ensure the safety of dynamic traffic information interaction by means of traditional static information security. In this study, a method was proposed through machine learning-based lane-changing (LC) behavior recognition and information credibility discrimination, based on the utilization and exploitation of traffic business characteristics. The method consisted of three stages: LC behavior recognition based on Support Vector Machine (SVM), LC speed prediction based on Recurrent Neural Network (RNN), and credibility discrimination of speed information under LC states. Firstly, the labeling rules of vehicle LC behavior and the input/output of each stage model were determined, and the raw NGSIM data were processed to obtain data sets for LC behavior identification and LC speed prediction. Both the SVM classification and RNN prediction models were trained and tested, respectively. Afterwards, a model of credibility discrimination speed information under an LC state was constructed, and the real vehicle speed data were processed for model verification. The results showed that the overall accuracy of vehicle status recognition by the SVM model was 99.18%, and the precision of the RNN model was on the order of magnitude of cm/s. Considering transverse and longitudinal abnormal velocity, the accuracy credibility discrimination of LC velocity was more than 97% in most experimental groups. The model can effectively identify the abnormal speed data of LC vehicles and provide support for the real-time identification of LC vehicle speed information under i-VICS. Full article

Consideration of upstream congestion caused by busy downstream machinery, as well as transportation time between different production stages, is critical for improving production efficiency and reducing energy consumption in process industries. A two-stage hybrid flow shop scheduling problem is studied with the objective of the makespan and the total energy consumption while taking into consideration blocking and transportation restrictions. An adaptive objective selection-based Q-learning algorithm is designed to solve the problem. Nine state characteristics are extracted from real-time information about jobs, machines, and waiting processing queues. As scheduling actions, eight heuristic rules are used, including SPT, FCFS, Johnson, and others. To address the multi-objective optimization problem, an adaptive objective selection strategy based on t-tests is designed for making action decisions. This strategy can determine the optimization objective based on the confidence of the objective function under the current job and machine state, achieving coordinated optimization for multiple objectives. The experimental results indicate that the proposed algorithm, in comparison to Q-learning and the non-dominated sorting genetic algorithm, has shown an average improvement of 4.19% and 22.7% in the makespan, as well as 5.03% and 9.8% in the total energy consumption, respectively. The generated scheduling solutions provide theoretical guidance for production scheduling in process industries such as steel manufacturing. This contributes to helping enterprises reduce blocking and transportation energy consumption between upstream and downstream. Full article

Turnover is a costly and time-consuming expense, especially for service industry businesses. To date, little is known about whether and how emotional labor may activate employee turnover intention in the service industry. In order to solve the above problems and fill the gaps, this study aimed to verify how emotional labor can trigger turnover intention during the COVID-19 pandemic. Based on job characteristics theory and job demands–resources theory, this study examined whether emotional display rules and emotional labor strategies affect turnover intention brought on by emotional exhaustion and job dissatisfaction, with fear of COVID-19 as a moderator. After testing our hypotheses using a sample of 623 individuals from China’s service industry, this study found that emotional display rules (positive and negative display rules) are significantly related to emotional labor strategies (deep acting, expression of naturally felt emotions, and surface acting). In particular, positive display rules have a positive impact on deep acting and the expression of naturally felt emotions and are more closely related to the expression of naturally felt emotions. Negative display rules negatively affect surface acting. Moreover, emotional labor strategies correlate significantly with emotional exhaustion, job satisfaction/dissatisfaction, and subsequent turnover intention. Thus, deep acting and the expression of naturally felt emotions are related to low emotional exhaustion and high job satisfaction, while surface acting is related to high emotional exhaustion and low job satisfaction. Emotional exhaustion has a negative effect on job satisfaction and a positive effect on turnover intention. Job satisfaction significantly weakens turnover intention. In addition, fear of COVID-19 has a moderating effect on the relationship between job satisfaction and turnover intention. The group with a high fear of COVID-19 has higher turnover intention even in job satisfaction situations than the group with a low fear of COVID-19. This work advances emotional labor research by combining two dimensions of emotional display rules and three dimensions of emotional labor strategies into a framework, investigating the mechanism through which emotional labor influences turnover intention, and revealing the moderating effect of fear of COVID-19 in the process. Full article

Web applications and online business transactions have grown tremendously in recent years. As a result, cyberattacks have become a major threat to the digital services that are essential for our society. To minimize the risks of cyberattacks, many countermeasures are deployed on computing nodes and network devices. One such countermeasure is the firewall, which is designed with two main architectural approaches: software running on standard or embedded computers, or hardware specially designed for the purpose, such as (Application Specific Integrated Circuits) ASICs. Software-based firewalls offer high flexibility and can be easily ported to upgradable hardware, but they cannot handle high data rates. On the other hand, hardware-based firewalls can process data at very high speeds, but are expensive and difficult to update, resulting in a short lifespan. To address these issues, we explored the use of an (Field-Programmable Gate Array) FPGA architecture, which offers low latency and high-throughput characteristics along with easy upgradability, making it a more balanced alternative to other programmable systems, like (Graphics Processor Unit) GPUs or microcontrollers. In this paper, we presented a packet sniffer designed on the FPGA development board KC705 produced by Xilinx, which can analyze Ethernet frames, check the frame fields against a set of user-defined rules, and calculate statistics of the received Ethernet frames over time. The system has a data transfer rate of 1 Gbit/s (with preliminary results of increased data rates to 10 Gbit/s) and has been successfully tested with both ad hoc-generated Ethernet frames and real web traffic by connecting the packet sniffer to the internet. Full article

Time-sensitive networks enable the high-quality mixed transmission of various types of business flows. However, the Time-Aware Scheduler mechanism fails to address the issue of interference in data flows with the same priority. This paper conducts an in-depth analysis of the store-and-forward mechanism in switches, combining it with the characteristics of critical GOOSE and SV-type flows in substations. By introducing methods such as setting offsets and allocating redundant time slots to the data flow of the transmitter in the TAS scheduling mechanism, all factors that cause conflicting interference to the data flow transmission in the TSN network are solved, and the uncertain queuing delay is eliminated. The proposed scheduling algorithm, compared to the TAS scheduling algorithm of the FIFO rule, achieves a maximum reduction of 34.35% in the transmission delay of critical business flows, while the jitter is controlled below 10 μs. Compared to the strict priority algorithm, it reduces the transmission delay by 40.26% while maintaining the standard deviation of delay within 1.59%. The maximum transmission delay and the minimum transmission delay of the data flow scheduled in this paper are between the theoretical boundary values without queuing delay, which satisfies the deterministic transmission of critical business flows under high load conditions, and provides support for future substation integrated networking and high load applications. Full article

The purpose of this study was to combine simulations and experiments in order to present the first stage of construction in product lifecycle management. Based on the simplification of casting models, the relationship between the filling and solidification characteristics, casting methods, and geometrical classifications of aluminum alloy precision casting products was investigated. By rearranging and summarizing the data, the casting models could be digitally managed; moreover, the digitized data could be used as the basis for intelligent processes in further developments. The simulations calculated and analyzed the casting speeds, defect locations, material densities, and critical fraction of a solid A356 aluminum–silicon alloy; the actual casting was carried out and samples were taken for metallographic observation to confirm the simulation results. The part model was simplified with four basic geometric shapes: solid cylinder, tubular, block rectangle, and thin-shell rectangle. The 150 casting models were summarized using 37 combinations, which were further classified into five main categories to match the casting method: solid cylindrical, tubular, and thin-shell rectangular for side casting, and discoidal and plate rectangular for bottom casting. File-compression rates of up to 75% were achieved after classification and archiving, and data integrity was maintained. Finally, model training using random forest classification resulted in an 88.8% accuracy when predicting the casting method. This research is based on the practical issues raised by business owners and R&D engineers, and a solution was obtained. From the perspective of product lifecycle management, the results of this study show the consistency and uniformity of product design rules, as well as the reusability of product process planning, which can be integrated with carbon emissions trading and carbon taxes to save energy and achieve corporate sustainability. Full article

The COVID-19 outbreak devastated business operations and the world economy, especially for small and medium-sized enterprises (SMEs). With limited capital, poorer risk tolerance, and difficulty in withstanding prolonged crises, SMEs are more vulnerable to pandemics and face a higher risk of shutdown. This research sought to establish a model response to shutdown risk by investigating two questions: How do you measure SMEs’ shutdown risk due to pandemics? How do SMEs reduce shutdown risk? To the best of our knowledge, existing studies only analyzed the impact of the pandemic on SMEs through statistical surveys and trivial recommendations. Particularly, there is no case study focusing on an elaboration of SMEs’ shutdown risk. We developed a model to reduce cognitive uncertainty and differences in opinion among experts on COVID-19. The model was built by integrating the improved Dempster’s rule of combination and a Bayesian network, where the former is based on the method of weight assignment and matrix analysis. The model was first applied to a representative SME with basic characteristics for survival analysis during the pandemic. The results show that this SME has a probability of 79% on a lower risk of shutdown, 15% on a medium risk of shutdown, and 6% of high risk of shutdown. SMEs solving the capital chain problem and changing external conditions such as market demand are more difficult during a pandemic. Based on the counterfactual elaboration of the inferred results, the probability of occurrence of each risk factor was obtained by simulating the interventions. The most likely causal chain analysis based on counterfactual elaboration revealed that it is simpler to solve employee health problems. For the SMEs in the study, this approach can reduce the probability of being at high risk of shutdown by 16%. The results of the model are consistent with those identified by the SME respondents, which validates the model. Full article

Advanced air mobility (AAM) is a sustainable aviation initiative to deliver cargo and passengers in urban and regional locations by electrified drones. The widespread expectation is that AAM adoption worldwide will help to reduce pollution, reduce transport costs, increase accessibility, and enable a more reliable and resilient supply chain. However, most countries lack regulations that legalize AAM. A fragmented regulatory approach hampers the progress of business prospectors and international organizations concerned with human welfare. Therefore, amidst high uncertainty, knowledge of indicators that can predict the propensity for AAM adoption will help nations and organizations plan for drone use. This research finds predictive indicators by assembling a unique dataset of 36 economic, social, environmental, governance, land use, technology, and transportation indicators for 204 nations. Subsequently, the best of 12 different machine learning models ranks the predictive importance of the indicators. The gross domestic product (GDP) and the regulatory quality index (RQI) developed by the Worldwide Governance Indicators (WGI) project were the two top predictors. Just as importantly, the poor predictors were as follows: the social progress index developed by the Social Progress Imperative, the WGI rule-of-law index, land use characteristics such as rural and urban proportions, borders on open waterways, population density, technology accessibility such as electricity and cell phones, carbon dioxide emission level, aviation traffic, port traffic, tourist arrivals, and roadway fatalities. Full article

Compliance in business processes has become a fundamental requirement given the constant rise in regulatory requirements and competitive pressures that have emerged in recent decades. While in other areas of business process modelling and execution, considerable progress towards automation has been made (e.g., process discovery, executable process models), the interpretation and implementation of compliance requirements is still a highly complex task requiring human effort and time. To increase the level of “mechanization” when implementing regulations in business processes, compliance research seeks to formalize compliance requirements. Formal representations of compliance requirements should, then, be leveraged to design correct process models and, ideally, would also serve for the automated detection of violations. To formally specify compliance requirements, however, multiple process perspectives, such as control flow, data, time and resources, have to be considered. This leads to the challenge of representing such complex constraints which affect different process perspectives. To this end, current approaches in business process compliance make use of a varied set of languages. However, every approach has been devised based on different assumptions and motivating scenarios. In addition, these languages and their presentation usually abstract from real-world requirements which often would imply introducing a substantial amount of domain knowledge and interpretation, thus hampering the evaluation of their expressiveness. This is a serious problem, since comparisons of different formal languages based on real-world compliance requirements are lacking, meaning that users of such languages are not able to make informed decisions about which language to choose. To close this gap and to establish a uniform evaluation basis, we introduce a running example for evaluating the expressiveness and complexity of compliance rule languages. For language selection, we conducted a literature review. Next, we briefly introduce and demonstrate the languages’ grammars and vocabularies based on the representation of a number of legal requirements. In doing so, we pay attention to semantic subtleties which we evaluate by adopting a normative classification framework which differentiates between different deontic assignments. Finally, on top of that, we apply Halstead’s well-known metrics for calculating the relevant characteristics of the different languages in our comparison, such as the volume, difficulty and effort for each language. With this, we are finally able to better understand the lexical complexity of the languages in relation to their expressiveness. In sum, we provide a systematic comparison of different compliance rule languages based on real-world compliance requirements which may inform future users and developers of these languages. Finally, we advocate for a more user-aware development of compliance languages which should consider a trade off between expressiveness, complexity and usability. Full article