Generative AI technology, represented by ChatGPT, has sparked profound changes in a variety of fields, and undoubtedly generative AI will drive continuous reforms and innovation in the business and experience of the travel and hospitality sectors. This article mainly explores the application of generative AI in the tourism industry based on existing literature, analyzes the constraints that may exist when it is applied in the field of tourism and hospitality, and explores future directions and opportunities for combining tourism, hospitality, and generative AI. This paper hopes to inspire scholars and practitioners to apply generative AI to tourism services, marketing and management, to explore smarter, more humane and more convenient tourism development modes and new business models, achieving high-quality development of tourism.

With the gradual advancement of digital transformation in the tourism industry, exploiting implicit information of tourism demand for prediction has gradually become mainstream in this research field. Among these works, the research on unidirectional implicit information is well-developed, whereas studies on interactive implicit information are scarce. Therefore, to further enhance the performance of tourism forecasting, this study employs a LangChain-based interactive context sentiment analysis model in the realm of feature engineering. By incorporating the sentiment tendencies found in online tourism reviews into tourism demand forecasting research, the model’s inferential capabilities are significantly improved. In terms of model processing, a new tourism demand prediction fusion model, EMD-STGCN-GRU-LSTM-Transformer (abbreviated as EST-Net), has been developed to address the unique spatio-temporal characteristics and imbalance of tourism data, thereby enhancing the model’s ability to accurately extract spatio-temporal sequences. Additionally, the PCA method is utilized to aggregate multiple key indicators of sentiment attention and natural environmental factors, constructing a tourism prediction indicator system to further correct the overall framework bias.

The development of cross-regional medical treatment has always been a people’s livelihood security issue in China, which is also a significant footnote of common prosperity in the healthcare security undertaking. Based on the PESCT model, this study constructs an evaluation index system for the development level of cross-regional medical treatment. Using the TOPSIS method and in terms of 31 provinces data, it objectively evaluates the development level of cross-regional medical treatment from a national perspective, explores the characteristics of regional differences. Moreover, the Moran index is applied to conduct an in-depth investigation of its spatial correlation as well as the characteristics of spatio-temporal evolution. An empirical analysis is also carried out to study the influence mechanism of various influencing factors on the development level. The study finds that there are significant differences in the development level among regions in China, and a spatial agglomeration effect has emerged during the period from 2018 to 2022. Policy systems, economic conditions, social development, health concepts, and transportation conditions exert varying degrees of influence on the high-quality development level. Finally, corresponding suggestions for improving the high-quality development level of cross-regional medical treatment in China are put forward from five dimensions.

This study investigates the ex-ante information on initial public offering (IPO) underpricing in Malaysia using multiple machine learning techniques. The paper analyzes a sample of 350 fixed-price IPOs from 2004 to 2021, applying five machine learning models: artificial neural networks, random forest, gradient boosting, extra trees, and linear regression. The results indicate that random forests demonstrates superior performance, with a test $R^{2}$ of 0.2292, a CV $R^{2}$ mean of 0.529, and a CV $R^{2}$standard deviation of 0.1293, indicating moderate but reliable predictive power suitable for noisy financial data, such as IPO underpricing, where feature importance insights are more valuable than precise predictions. To reconcile and aggregate different outcomes from these multiple models, we implemented a voting algorithm to identify robust and reliable feature ranking for ex-ante determinants of IPO underpricing. Among the features, investor demand and divergence of opinions consistently emerged as the top two influential predictors of IPO underpricing, highlighting the key role investor sentiment and information asymmetry play in determining IPO pricing. These findings offer insights to investors, issuers, and policymakers, enabling a deeper understanding and effective management of the ex-ante drivers of underpricing in fixed-price IPOs, which lack market-based price discovery.

Financial holding companies (FHCs) in China leverage equity control to enhance operational efficiency and synergies, yet excessive equity concentration often undermines these benefits. This study investigates the impact of equity structure —specifically concentration and balance — on the performance of 17 A-share listed Chinese FHCs from 2010 to 2022, using data from the CSMAR database. Empirical results reveal an inverted U-shaped relationship between equity concentration and performance, with moderate concentration optimizing decision-making efficiency, while excessive levels risk power abuse. Equity balance, however, negatively affects performance by fostering power struggles and delaying decisions. These findings underscore the need for a balanced equity structure in Chinese FHCs. Policy recommendations include listing parent companies to diversify equity, keeping subsidiaries unlisted with concentrated ownership for synergy, strengthening regulation, and encouraging small shareholder participation to enhance governance and stability.

This paper applies the WSR systems methodology to explore how the quality monitoring and evaluation system in regional elementary education management acts as a carrier of value orientation and promotes high-quality development through feedback control. Based on the static Wuli (physical), Shili (practical), and Renli (human) elements, a time factor is introduced by adding a feedback control mechanism between cycles, creating a continuous dynamic closed-loop model for improving education quality. This ensures alignment with the system’s value and overall goals. Using student development data from the regional quality monitoring platform, the study implements personnel and distribution reforms, breaking system equilibrium and aligning individual and organizational goals. Based on data from 21 high schools in J City, H Province (2008–2019), the difference-in-differences (DiD) method is used to analyze the system’s impact on education quality. The results show a significant positive effect, with conclusions remaining robust after stability tests. This study enriches WSR’s application in elementary education, fills a research gap on policy effects, and offers practical insights for education managers.

In recent years, extreme weather events and pest/disease issues have made the resilience of the Agri-food supply chain a focus of social concern. Enterprises typically adopt two primary strategies to enhance the supply chain’s resilience, namely maintaining high inventory levels and improving logistics timeliness. The former, particularly through the implementation of the safety stock strategy, appears more feasible in the short term but incurs significant costs, especially for Agri-food. Therefore, striking a balance between resilience and cost efficiency is essential. This paper proposes a system dynamics model to collaboratively optimize resilience and holding costs in a three-level Agri-food supply chain. Using demand fulfillment rate as a resilience indicator, six simulation scenarios with varying inventory and transportation time configurations are designed. The dynamic impacts of these factors on both costs and resilience are analyzed. Optimization is performed using the Powell hill climbing algorithm in Vensim^® DSS to adjust the safety stock strategy. Results show that: Reducing distributors’ transport time enhances resilience more, but at higher costs; increasing the inventory levels of retailers and distributors is more effective in improving resilience, though also accompanied by increased costs; Collaborative optimization among supply chain members can maximize both resilience and cost efficiency.

With the rapid advancement of the Internet of Things, the generation and sharing of massive data have become a significant trend. However, the pervasive free-riding behavior among stakeholders has adversely impacted data quality. To address this issue, this study employs tripartite evolutionary game theory to construct a decision-making model involving data providers, data brokers, and regulators. This model depicts the strategic choices of these three parties in data quality management: data providers and brokers may opt for proactive investment or passive free-riding, while regulators may choose between stringent oversight or routine inspections. By constructing replicator dynamics equations, employing Jacobian matrix analysis to examine equilibrium points, and combining numerical simulations with sensitivity analysis, this paper explores the evolution of stakeholder strategies and the impact of key parameters on system stability. Results indicate that free-riding behavior significantly compromises data quality. When the net benefit of active quality control falls below the free-riding payoff, the system converges toward a fully passive equilibrium point (0, 0). However, appropriately calibrated incentive and penalty mechanisms can effectively promote active behavior. This study provides a quantitative foundation for understanding multi-agent interactions in data sharing and offers actionable strategic insights for optimizing IoT data sharing mechanisms.

The development of the new energy vehicle (NEV) industry is pivotal in addressing China’s energy security challenges and restructuring its automotive sector. It is a critical measure for achieving China’s carbon neutrality goals. Accurately analyzing and forecasting NEV sales volumes carries significant implications for industry planning and policy formulation. This paper proposes an integrated modeling framework that combines variable selection techniques with the XGBoost algorithm to forecast NEV sales in China. We compare three distinct variable selection methods and evaluate their performance against commonly used benchmark forecasting models. The prediction performance is assessed using two metrics: root mean squared error (RMSE) and mean absolute percentage error (MAPE). Our empirical results demonstrate that the variable selection-XGBoost integrated model outperforms both univariate models and models that do not incorporate core factor extraction, showing superior accuracy in both in-sample and out-of-sample predictions. Among the variable selection-XGBoost models, the SSL-XGBoost model yields the best performance, followed by GLMNET-XGBoost and LARS-XGBoost. The SSL method selects the greatest number of core variables. Cross-validation results indicate that integrating XGBoost with variable selection significantly reduces prediction errors. The variable selection-XGBoost integrated model surpasses traditional models in terms of both accuracy and reliability.

In the context of rapid globalization and technological innovation, this study introduces a novel evaluation standard system designed for the modern service industry. Utilizing the DPSIR (driving forces, pressures, states, impacts, responses) framework, this study categorizes pertinent characteristics to elucidate their roles within the service sector, enhancing understanding of the complex dynamics between environmental factors and human activities. Meanwhile, the application of the entropy weight method significantly reduces subjectivity, thereby improving the reliability and effectiveness of assessment outcomes by quantifying the informational contribution of various indicators. Furthermore, incorporating the technique for order preference by similarity to ideal solution (TOPSIS), analysis fosters a robust index system for assessing the developmental levels of China’s life service industry. This study’s innovative approach and its practical implications mark a significant leap in strategic decision-making and understanding of the modern service industry, offering a novel and adaptable tool for industry analysis.

A novel location-queuing problem for blood collection facilities against MPHEs is studied in this paper. The decision variables to be determined include the opening plan of fixed blood collection rooms, the location of mobile blood collecting vehicles, and the number of service desks within facilities. This problem is formulated as a bi-objective multi-period integer nonlinear programming model, incorporating unique features that distinguish it from previous studies, such as pandemic risk, blood donation behavior, and the heterogeneity of blood collection facilities. The objectives are to minimize the total system cost and maximize donor satisfaction. To solve this problem, an improved multi-objective grey wolf optimization (IMOGWO) algorithm, which incorporates chaotic mapping and adaptive convergence factors, is proposed. Real data from Chongqing, China, is utilized to demonstrate the applicability of the model and the effectiveness of IMOGWO. Using evaluation metrics such as the C metric (CM), number of Pareto frontier (NPF), maximum spread (MS), spacing (SP), mean ideal distance (MID) and computation time (CPU time), numerical experiments demonstrate that the proposed IMOGWO outperforms non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective particle swarm optimization(MOPSO), multi-objective whale optimization (MOWOA), multi-objective chimp optimization (MOChOA), and multi-objective grey wolf optimization (MOGWO).

The level of intelligence in weapon systems and equipment will be one of the key factors determining the victory or defeat of future wars. Methods to incorporate the level of intelligence as an incentive factor into the pricing system of weapons and equipment were explored, which can motivate contractors to strive to improve the level of intelligence in weapons and equipment. Based on the core combat capabilities of weapons and equipment in the context of intelligent warfare, a comprehensive evaluation index system for equipment intelligence level is constructed, which includes 6 primary indicators and 18 secondary indicators. Taking the intelligence index as the intelligence level of equipment, the calculation method of intelligence index is given by using the closeness in TOPSIS. On this basis, the equipment intelligence index is included as the main incentive factor in the equipment incentive pricing model, forming an incentive pricing method based on the level of equipment intelligence. The feasibility of the method was verified through simulated data and compared with the pricing method that only considers cost incentives. The results indicate the method proposed in this paper can obtain differentiated prices according to the market environment, which is more flexible and applicable.

As one of the three pillars of the tourism industry, hotel sales are influenced by a variety of factors. Particularly, with the exponential growth of the internet, user-generated images, text, and data presented by hotels impact hotel sales to varying degrees. This study attempts to explore how different factors affect hotel industry sales. Firstly, it examines review images, text data, and hotel base attribute data; secondly, it employs a deep learning-based approach to analyze the different types of data; finally, it uses random forest to calculate feature importance values and analyzes them based on different star ratings variance. The results show that image-text consistency influences all types of hotel sales. Furthermore, the consistency of image text also affects all hotel sales, and there are differences in the factors influencing sales across hotel types. The findings can be used to provide valuable advice to hotel managers in the sales field.

Enhancing industrial linkage is an effective way to optimize the industrial structure, improve the efficiency of industrial resource allocation, and prompt the high-quality development of the industrial economy; therefore, analysing industrial linkage is highly important. On the basis of the complex network perspective, this paper uses the input-output table of Henan Province from 2017 to explore the topology and association characteristics of the industrial network in Henan Province, and the results show that the industrial network in Henan Province has some small-world nature and scale-free network characteristics; however, it is not completely consistent with the stochastic network model and the BA model, and the stochastic block model is able to fit the data better; however, there are some difficulties in model estimation.

This study empirically examines the impact of climate policy uncertainty on bank systemic risk and the underlying mechanisms, using unbalanced panel data from 36 banks over 2008–2022. The results indicate that climate policy uncertainty significantly reduces bank systemic risk. This effect operates through three key channels: increasing capital adequacy, reducing leverage, and decreasing lending to high-carbon industries. Furthermore, the risk-reducing impact of climate policy uncertainty is strengthened by greater carbon emission reductions. It is noteworthy that the effect of climate policy uncertainty on bank systemic risk follows a U-shaped pattern. The risk-reducing effect is particularly evident in larger, non-state-owned banks with strong profitability, effective risk management, and higher credit allocation to green sectors.

The debate surrounding the relationship between financial structure and systemic financial risk has been ongoing. This controversy arises from a lack of consideration for national resource endowments, which serve as the foundation for the development of industrial structure. Insufficient research has been conducted on the interplay between financial structure, industrial structure, and systemic financial risk. In light of this, the primary objective of this study is to underscore the crucial role of industrial structure in elucidating the interaction between financial structure and systemic financial risk. The research findings highlight that financial structure indirectly influences systemic financial risk through its impact on industrial structure. There exists significant heterogeneity in the transmission effect of industrial structure, with a more pronounced effect observed in areas characterized by high levels of economic development and belonging to mature clusters. Furthermore, the transmission effect of industrial structure is influenced by efficient market and effective government. The improvement of the efficient market can promote the upgrading of the industrial structure while reducing the level of potential risk. Excessive government intervention will reduce the transmission effect of the industrial structure.

Stunting has a significant impact on children’s health and, in the long term, negatively affects productivity and GDP by 2–3%. Therefore, it is crucial to reduce stunting rates through regional mapping based on their capacity to address stunting and by evaluating relevant indicators. A multi-criteria decision making (MCDM) approach, utilizing principal component analysis (PCA) and Entropy for weighting, and MARCOS, COPRAS, and WASPAS for ranking, can be applied. The weighting results from PCA and Entropy indicate that access to drinking water (C11) and households receiving food assistance (C10) are the largest contributing factors, while the smallest contributors are poverty rate (C7) and Gini ratio (C6). Using PCA weights across all MCDM methods, DKI Jakarta (A11) emerges as the best-performing region, while Papua (A34) ranks the worst. When Entropy weights are applied, DKI Jakarta (A11) ranks first in MARCOS and WASPAS, while South Kalimantan (A22) ranks best in COPRAS. Papua (A34), however, remains the worst performer across all methods. This study concludes that the ranking results from PCA and Entropy weighting methods are identical, showing a strong correlation. This provides policymakers with confidence in assessing each province’s capacity to address stunting, highlighting that Papua (A34) demonstrates relatively poor performance in managing stunting.

Trajectory prediction (TP) is critical for enhancing flight safety and operational reliability in small to medium-sized private and corporate aircraft, which involve complex multiple inputs and multiple outputs. While existing TP methods primarily focus on extracting coupled features, they often neglect the independent features of individual outputs, leading to unsatisfactory learning performance. To address this limitation, this paper proposes a multitask learning-based TP method using bi-directional long short-term memory (Bi-LSTM), consisting of two key components: 1) a multi-source feature fusion part that automatically extracts and integrates coupled evolutionary features across flight modes, and 2) a multitask learning part that mines independent change characteristics of each output. Firstly, the trajectory sequences are categorized into short, medium, and long-period flight modes to better capture temporal dependencies. The coupled characteristics in every flight mode are automatically excavated and integrated via the Bi-LSTM and fully connected network in the multi-source feature fusion part. Secondly, the fusion output is sent into the multitask learning part and every task has a customized model to learn independent evolutionary features of each output. Experimental results on real-world flight trajectories demonstrate the superiority of the proposed method in both one-step and multi-step prediction scenarios, highlighting its ability to leverage both coupled and independent features of flight trajectories.

Amidst the ongoing debate surrounding algorithmic price discrimination, the regulation of price discrimination in the platform economy has once again emerged as a prominent topic of discussion in various spheres. The central focus and challenge of this matter lies in understanding the welfare implications of price discrimination, specifically how it impacts firms, consumers, and society as a whole. By comparing discriminatory pricing with uniform pricing (without price discrimination), we can find the changing laws of firms’ profit, consumer surplus, and social welfare. Based on the complete information static game, we derive the analytical expressions for the profit increment, the consumer surplus increment, and the social welfare increment through a mathematical model. The study focuses on the third-degree price discrimination in two consumer groups of $m$ (any positive integer) firms. Additionally, we prove the characteristics of these increments, whether they are positive, negative, or zero. The research results show that algorithmic price discrimination is beneficial to firms but detrimental to consumers. Its impact on the whole society is conditional and depends on the sum of the marginal costs of the $m$ firms. The study examines this issue through the lens of four core values: consumer rights, social welfare, fair value, and competition protection. Based on the experiences of developed countries, regulatory countermeasures are proposed to address this issue, focusing on protecting personal information and civil rights.

The continuous growth in energy demand, shortage of fossil fuels, and global climate change have raised significant attention towards renewable energy. In this paper, firstly, a three-echelon biomass-to-bioenergy supply chain composed of a farmer, collection station and power generation enterprise is developed. Secondly, the optimal decisions for four scenarios are investigated, namely, a decentralized decision-making model, a collaborative decision-making model between the farmer and the collection station, a collaborative decision-making model between the collection station and the power generation enterprise, and a centralized decision-making model. Thirdly, the average tree solution method of cooperative game theory is used to allocate the supply chain profits. Finally, numerical analysis is conducted by taking one biomass energy company as an example to support the results. Our research finds that: 1) In a centralized decision-making scenario, the individual and overall revenues are maximized. 2) For the collection station, allying with the power generation enterprise is more beneficial than allying with the farmer. 3) For the power generation enterprise, forming an alliance with the collection station is greater than decision-making independently.

With the rapid advancement of artificial intelligence technology, hospitals are accelerating their transition into a new era of intelligent construction. To scientifically and accurately assess the level of intelligent services in hospitals, this study introduces the hesitant fuzzy multi-attribute decision-making theory and proposes a hesitant fuzzy VIKOR evaluation model based on entropy weights. First, an evaluation attribute system was established from three dimensions: pre-diagnosis, during diagnosis, and post-diagnosis, and uses the Delphi method to collect the scores of experts on each alternative under each evaluation attribute. Furthermore, since the attribute weights are unknown, in this paper, the hesitant fuzzy entropy weight method is used to construct an attribute weight determination model. Building on these advancements, a hesitant fuzzy VIKOR multi-attribute evaluation model based on entropy weight was further established. Finally, five large public hospitals were selected as the evaluation objects, and the proposed model was used to evaluate their intelligent service levels. Through the comparative analysis of different evaluation methods, the results verified the scientificity and validity of this model. This study offers a novel theoretical framework and practical tool for the comprehensive assessment of hospital intelligent service levels.

Data element circulation trading platforms are pivotal in unlocking the value of data, optimizing resource allocation, and enabling cross-industry data sharing. However, existing platforms face significant limitations in addressing the evolving demands of the data market and the diverse requirements of users, with their architectural frameworks remaining underdeveloped. This study proposes a comprehensive theoretical framework that integrates advanced technologies such as blockchain, non-fungible tokens, and federated learning to enhance the functionality and adaptability of these platforms. A comparative analysis with the Ocean Protocol platform is conducted to elucidate the theoretical contributions and practical implications of the proposed framework. Based on this analysis, the study offers strategic recommendations for advancing the design and implementation of data element circulation trading platforms, providing valuable insights to guide future research and practice in this critical domain.

Under the dual carbon goal and the national energy security strategy, improving the ESG development level of China’s energy enterprises is undoubtedly an important issue at present. Based on relevant theories of ESG development, this study constructs a model of influencing factors of ESG development of energy enterprises in China on the basis of summarizing factors that will have an impact on ESG development of energy enterprises. Then, combined with 501 effective studies obtained by questionnaire survey, it analyzes the interrelationship and action mechanism among various influencing factors. The results show that government factors have the greatest impact on the ESG development level of China’s energy enterprises, followed by enterprises, investors, the third-party rating agencies and financial institutions. The results of this study will play a certain reference role in China’s energy green transformation, ensuring national energy security, and promoting the sustainable development of energy enterprises.

Information leakage often influences manufacturers’ live-streaming strategies for product sales, particularly regarding the choice between self-streaming and internet celebrity-commissioned live streaming. We construct live-streaming supply chain models to investigate manufacturers’ choices of streaming modes and examine how information leakage affects decision-making. We find that, in the absence of information leakage, the selection of the live-streaming mode is primarily driven by the intensity of competition. However, when information leakage is present, factors such as the internet celebrity’s fan effect and the extent of information leakage also play significant roles. Under conditions of low information leakage, manufacturers are more inclined to commission internet celebrities with a strong fan base, whereas in scenarios involving high information leakage, they tend to favour those with a weaker fan effect. These conclusions offer valuable insights for manufacturers seeking to make informed live-streaming decisions while considering the implications of information leakage.

Taking three nighttime commercial streets known for their nighttime lighting, with high vitality and large flow of people as representatives, we investigated customers’ perception of lighting and their willingness to approach in the field, and empirically examined the mechanism of the influence of lighting ambience cues on customers’ willingness to approach in nighttime commercial streets by using structural equation modeling (SEM). The results show that task-oriented cues, aesthetic cues and social cues in the lighting atmosphere have a significant impact on customers’ approach intention. Customers’ practical, hedonic and connectedness perceptions play a mediating role, and hedonic perception is the most influential factor. The findings are of great practical significance for optimizing the lighting design of commercial streets, enhancing customer experience and stimulating the vitality of nighttime economy.

The study aims to analyse transformational changes in banking risk management and marketing policy of banks caused by digitalisation. The study addressed examples of successful digital technology implementation in the banking sector around the world and conducted a comparative analysis of the current situation in Kyrgyz banking. The study addresses the process of digital transformation of the banking sector, with a focus on Kyrgyzstan. Digital transformation involves the integration of technologies such as artificial intelligence, big data and blockchain into all aspects of banking operations, leading to significant changes in their functioning and customer experience. The study analysed automating processes, improvement of customer service, enhancement of transaction security and creation of new business models. The study included examples of successful digitalisation in banks around the world, such as the use of artificial intelligence to automate and analyse data, which helps to predict risks and improve customer experience. Based on the analysis, recommendations for banks in Kyrgyzstan were proposed, including investments in IT infrastructure, literacy programmes and enhanced cybersecurity. The results show that digitalisation can significantly increase the accessibility and quality of banking services, improving the overall standard of living of the population.

This research introduces a novel framework that integrates graph convolutional networks (GCNs) with clustering techniques to examine the intricate spatial structure of contemporary service industries. Utilizing 2023 point-of-interest (POI) data from Xi’an, the study extends beyond analyzing single industries to uncover 18 unique multi-industry composite clusters, highlighting significant patterns such as the blending of education with real estate and the merging of business and financial services. Additionally, by employing DBSCAN, the research identifies the high-density core regions within these clusters and their spatial coexistence patterns, pinpointing multifunctional areas. These results contribute to the theory of urban polycentricity and offer data-driven guidance for planners to promote evidence-based zoning, encourage mixed-use development, and enhance functional integration in service-focused urban economies.

This paper investigates an $M/G/1 $ queueing system that integrates preventive maintenance, randomized vacation policy, and Bernoulli feedback mechanism. By applying the law of total probability decomposition and Laplace transform (LT)technique, we derive explicit expressions for the LT of the transient queue length distribution starting from arbitrary initial states. Based on the transient results, the steady-state queue length distribution and the corresponding additional queue length distribution are also obtained. Furthermore, we show that the proposed model reduces to several classical systems under specific parameter settings. Finally, numerical experiments are conducted to compare the proposed model with classical vacation models, to examine the system capacity optimization design under loss constraints, and to illustrate the transient behavior of the system. These findings verify the validity and practical relevance of the considered model.

The research purpose is to contribute to the field of forecasting foreign exchange. This is due to the ever-changing economic conditions under which analysts can observe the significant volatility of exchange rate forecasts, as exchange rate forecasting has been challenging for analysts for many years. Stakeholders (central banks, governments, and investors) will seek to maximize asset returns and minimize risk in their decision-making using exchange rate forecasting. Therefore, this study proposes a new approach from the Black-Scholes model to forecast daily, weekly, monthly, and yearly exchange rates for the domestic currency pair the indonesian rupiah (IDR) to the United States dollar (USD) traded. The Black-Scholes model, originally referred to as a partial differential equation, was changed to an ordinary differential equation, which is used to approximate the numerical solution of the new Black-Scholes equation. The numerical solution used in this research is the fourth-order Runge-Kutta method. This study uses actual exchange rate data for more than one year, and the prediction results show that the proposed methodology can be an effective method for forecasting the exchange rate (IDR/USD). It is indicated by a small error, of less than 5%, and a mean absolute percentage error of less than 2%.

Personalized recommendation services have recently been widely provided in online social networks (OSNs). OSNs have large number of users, and users’ information and browsing data are saved in online services due to frequent communication between users. To achievebetter performance, personalized recommendation services need users’ characteristics and behavior information, which brings privacy issues into concern. Therefore, balancing privacy preservation and recommendation results has become the main focus in this field. In this paper, we combine a privacy preserving method with the social network model and make full use of the user’s attribute information in the social network to improve personalized recommendation results based on the privacy-preserving link prediction(PPLP) framework. Additionally, a link prediction algorithm with attribute classification is proposed in this paper, which considers the connections between user attributes and the similarity between users. The improved PPLP was evaluated on Google+ datasets and the results show that it can improve the accuracy of recommendation results while protecting users’ information.

In this paper, we discuss two cases of two-player repeated games in an environment of incomplete information with randomness and cognitive uncertainty, where incomplete information refers to the situation in which the transition probabilities and player payoffs are uncertain. We use robust optimization techniques to handle data uncertainty and determine the optimal solution within this framework. Our contributions are threefold: Firstly, we apply Markov processes to the repeated game model. Secondly, we propose an effective robust optimization method that can handle uncertain data in the context of incomplete information and solve the uncertainty problem in different types of repeated games. Finally, our method is more general than previous methods and can adapt to various types of data, providing decision support for risk-averse players.

Smart manufacturing is a key aspect of current urban sustainability concerns, with urban skills impacting the growth of smart manufacturing. This raises questions for sustainable urban development regarding the polarization of skills between cities. This study investigates the influence of inter-city skills polarization on the wages and employment of workers in smart manufacturing in China by examining social-cognitive skill score data. The regression results show that the social cognitive skill score of the city has a significant positive effect on local manufacturing wages. However, it reduces the number of local manufacturing jobs and the proportion of manufacturing in the industrial structure. Smart manufacturing development policies have a significant impact on local manufacturing employment but do not influence the wage levels of local manufacturing workers. In addition, productivity in the secondary industry may reduce local manufacturing wages and employment. Nevertheless, it has a negative skew when mediating the connection between intercity skill polarization and local manufacturing wages. The study reveals the reasons for workers participating in production during the smart manufacturing era, predicts future wage changes for these workers, and examines the differences in industrial layouts across cities.

We introduce a mathematical model for a voting system called fuzzy threshold voting (FTV) intended for use primarily in cryptocurrency governance systems. The main difference between this voting model and approval voting is that the voting outcome depends not only on the scores of candidates but also on other factors—budgets of the corresponding projects (actually, projects are the candidates). The proposed FTV voting model is considered to be some generalization of the yes-no voting model with expanded features. In addition to building an adequate mathematical model for the new voting system, we also analyze its main properties, such as the optimality (in some sense) of the voting procedure, existence of admissible strategies, advantages of sincere voting, and necessity of secret voting. We demonstrate that the presented FTV system satisfies one of the most important properties of the voting systems—the monotone rule, and prove several statements regarding possible voting strategies.

In the global financial market system, gold, as a special safe-haven asset with monetary, commodity, and financial attributes, exhibits characteristics of non-linear and highly complex price fluctuations. Accurate short-term prediction of gold futures prices can provide investors with important decision-making references. Firstly, this paper takes gold futures prices as the research object and constructs a multi-dimensional prediction factor system, using the SHAP method to screen and conduct interpretability analysis on the prediction factors. Secondly, using the screened prediction factors, a TCN-LSTM model is established to conduct point prediction of gold futures prices and perform ablation experiments. Finally, adaptive bandwidth kernel density estimation is employed to fit the probability density function of point prediction errors and conduct interval prediction as well as interval prediction ablation experiment analysis. The empirical results show that the SHAP method can effectively screen out gold futures price prediction factors with strong explanatory power. Using the screened prediction factors for point prediction and interval prediction significantly improves the accuracy of gold futures price prediction. The ablation experiments of point prediction and interval prediction indicate that the same prediction factor has certain differences in improving the performance of point prediction and interval prediction.

Under the background of the rapid development of express industry and building China’s strength in postal, understanding the structure and connection of the current express network is helpful to optimize the layout and construction of the express network. Based on the improved logistics gravity model and social network analysis method, this paper analyzes the structural characteristics and spatial connection of express delivery network in 39 national logistics hub cities. The results show that the express logistics links between hub cities are more intensive, but the overall spatial distribution of express network is unbalanced. In the express delivery network, the status of hub cities is obvious, and the core hub cities have an important influence on the network structure. Some hub cities highly depend on the core hub cities, and the media and driving role played by core hub cities in different regions are different. Shanghai, Jinhua, Shenzhen, Guangzhou, Beijing, Tianjin, Chongqing, Chengdu, Xi’an, Wuhan and Zhengzhou play significant roles in the overall express network, and have strong radiation ability. They are suitable for being a global international postal express logistics hub. Changsha, Nanjing, Ningbo, Yichang, Suzhou and other important hub cities can be preferred as regional international postal express hubs. The hub cities that are not prominent are more suitable as national postal express hubs.

Efficient cross-border tourism flows are a critical dimension of a country’s economic integration into the global economy. This paper introduces an innovative application of the stochastic frontier gravity model (SFGM) to analyze and measure international tourism efficiency. By integrating natural determinants (e.g., geographical distance, economic size, and price indices) with man-made factors (e.g., social, political, economic, and policy preferences), the study provides a comprehensive framework for assessing tourism efficiency relative to theoretical gravity frontier levels. The findings reveal that, while China has achieved approximately 80% of its tourism potential on average, significant inefficiencies persist, particularly across different origin countries and regions. The study highlights the complementary relationship between human and goods flows, emphasizing the importance of cultural proximity and trade intensity in reducing inefficiencies. Furthermore, it demonstrates the robustness of the SFGM framework in capturing the dynamic and uneven patterns of tourism efficiency over time. By addressing gaps in the application of SFGM to tourism research, this paper advances the theoretical and methodological understanding of tourism efficiency and provides actionable policy recommendations for enhancing China’s tourism market integration.

This study develops a systematic framework to evaluate regional service science and technology innovation (SSTI) in China. It aims to address the “manufacturing paradigm” bias in existing evaluations. Drawing on the national innovation system (NIS)theory, a multi-level indicator system is constructed with three dimensions: Innovation input, innovation output, and innovation environment. The analytic hierarchy process (AHP) is applied to determine indicator weights, and the SSTI performance of eight provinces and municipalities in China from 2011 to 2023 is systematically measured and comparatively analyzed. The results reveal a sustained upward trend in the overall level of SSTI. However, a pronounced stratified pattern of “the strong get stronger” persists, reflecting significant and widening regional disparities. Moreover, regional innovation modes exhibit marked structural heterogeneity. These findings advance the operational evaluation of SSTI and provide diagnostic insights for identifying systemic bottlenecks, optimizing resource allocation, and formulating region-specific innovation strategies.

This paper examines the impacts of retailer fairness concern and consumer fairness concern on the profit under a supply-chain framework consisting of a manufacturer and multiple retailers. Firstly, the case in which the manufacturer declares a unified wholesale price under retailer fairness concern is discussed. The results show that the profit of the manufacturer decreases and the total profit of retailers increases under retailer fairness concern. In the meanwhile, an interesting phenomenon is revealed: Despite a constraint of retailer fairness concern is added to the supply chain, the total profit rises rather than falls. Secondly, we consider both retailer fairness concern and consumer fairness concern. In this case, the manufacturer declares a unified wholesale price, and all retailers declare a unified retail price. Compared with the case in which only retailer fairness concern is considered, consumer fairness concern lowers the total profit of all retailers, while the profit of the manufacturer remains unchanged. Because pricing coordination is needed during the unified pricing of retailers, this study puts forward the definition of extreme-value-proportion, and synthetically applies Nash bargaining solution and proportion sharing solution to obtain the profit distribution scheme for the retailer-pricing alliance based on absolute value average and relative value average.

Accurate daily tourism demand forecasting is a fundamental input for effective revenue management and resource allocation but is often hindered by the complex, multi-scale seasonal patterns inherent in high-frequency data. This study addresses this challenge by systematically evaluating advanced decomposition-based forecasting models. We compare the performance of the trigonometric box-cox ARMA trend seasonal (TBATS) model and a hybrid complete ensemble empirical mode decomposition with adaptive noise-neural network (CEEMDAN-NN) model against traditional benchmarks. The findings reveal that both models consistently outperform conventional approaches. Notably, the CEEMDAN-NN hybrid model achieves the highest forecast accuracy, while TBATS offers strong interpretability for complex seasonalities. These results underscore the benefit of integrating decomposition techniques to improve the precision of daily forecasts, thereby supporting data-driven revenue management decisions.