Shanxi University of Finance and Economics

The supply chain plays a vital role in global trade and economic development. This paper reviews the research on the resilience of the apparel supply chain during the COVID-19 pandemic and concludes with four key findings. First, prior to the pandemic, the supply chain operated within a complex global framework characterized by clear regional specialization, but it revealed vulnerabilities due to over-reliance on specific regions, lack of transparency, and inadequate inventory systems. Second, academic focus on efficiency often overlooked disaster preparedness, leaving industries ill-equipped for crises. Third, during the pandemic, the supply chain encountered significant disruptions, including factory closures, fragmented supply routes, and distribution challenges that affected production levels. Small and medium-sized enterprises (SMEs) faced heightened difficulties, while larger brands had to adapt. Fourth, resilience during this period led to transformative changes, with swifter responses and an increased reliance on strategies aimed at enhancing supply chain flexibility. Finally, future research must focus on developing predictive disruption models, integrating digital technologies for smarter supply chains, and promoting sustainable practices that align with environmental and ethical standards. By fostering these initiatives, a more resilient and sustainable apparel supply chain can be established, better equipping the industry to face future uncertainties while achieving economic, environmental, and social benefits.

Building upon recent developments in production function identification and decomposition methods, this paper investigates the sources of output and productivity growth among China’s listed manufacturing companies from 2000 to 2022. While previous studies on China’s manufacturing have predominantly focused on the period preceding 2007, our study extends the analysis to a broader timeframe and divide it into four sub-periods to accommodate diverse economic conditions and varying growth rates. We provide new insights into the Chinese economy during a period marked by gradual economic transformation. Specifically, we first decompose industry output growth into factor deepening and firm productivity progress within each sub-period. To account for heterogeneity across firms in terms of production technology and sources of growth, we employ a nonparametric production function and decompose firm output growth at both the mean and different quantiles of the output distribution. We find that increased materials usage and productivity growth are primary growth drivers. However, the contribution of productivity experiences a significant decline, particularly in recent years and among median-sized and large firms. Furthermore, we examine China’s industry aggregate productivity growth and its origins among state-invested, foreign-invested, and domestic private firms. Our findings suggest that reforms among state firms are the largest contributor to industry productivity growth before the 2008 financial crisis, whereas productivity progress of domestic private firms emerges as the sole significant driver in recent years. Additionally, there is no evidence of improvements in output reallocation efficiency within China’s manufacturing sector throughout our sample period.

Objective This study aims to discuss anxiety in mediating role between bullying victimization and adolescent internet addiction, and the moderating role of family support between bullying victimization and adolescent anxiety. Methods A cross-sectional study was conducted in 5 provinces of China by convenience sampling from February to March 2024. A total of 1395 participants (599 boys and 796 girls) with an average age of 15.86 ± 0.74 years were included in the final analysis. Subjective data on bullying victimization, internet addiction, anxiety, and family support were collected and analyzed. A moderated mediation model was constructed. Results After controlling for age and gender, bullying victimization was found to be a significant predictor of internet addiction (β = 0.130, p < 0.001). Anxiety has a complete mediating effect between bullying victimization and adolescent internet addiction. Specifically, bullying victimization significantly predicted adolescent anxiety (β = 0.264, p < 0.001). anxiety significantly predicted adolescent internet addiction (β = 0.417, p < 0.001). Family support alleviated the relationship between bullying victimization and anxiety (β= -0.032, p < 0.05). Conclusions Bullying victimization can predict internet addiction through anxiety in adolescents, and family support can alleviate the predictive relationship between bullying victimization and adolescent anxiety. It is suggested that guardians should provide adequate support to adolescent bullying victimization in order to reduce the negative impact of bullying victimization on adolescents and prevent the occurrence of internet addiction.

The Loess Plateau in northwest China features fragmented terrain and is prone to landslides. However, the complex environment of the Loess Plateau, combined with the inherent limitations of convolutional neural networks (CNNs), often results in false positives and missed detection for deep learning models based on CNNs when identifying landslides from high-resolution remote sensing images. To deal with this challenge, our research introduced a CNN–transformer hybrid network. Specifically, we first constructed a database consisting of 1500 loess landslides and non-landslide samples. Subsequently, we proposed a neural network architecture that employs a CNN–transformer hybrid as an encoder, with the ability to extract high-dimensional, local-scale features using CNNs and global-scale features using a multi-scale lightweight transformer module, thereby enabling the automatic identification of landslides. The results demonstrate that this model can effectively detect loess landslides in such complex environments. Compared to approaches based on CNNs or transformers, such as U-Net, HCNet and TransUNet, our proposed model achieved greater accuracy, with an improvement of at least 3.81% in the F1-score. This study contributes to the automatic and intelligent identification of landslide locations and ranges on the Loess Plateau, which has significant practicality in terms of landslide investigation, risk assessment, disaster management, and related fields.

Since the 20th century, China's national economy has continued to improve, the people's quality of life has taken a leap forward, coffee has become a popular consumption, and the coffee market has shown unprecedented potential. In the face of such a large and dynamic market, many local coffee brands have emerged in an attempt to seize the market share. However, Luckin Coffee, with its unique business management model and innovative marketing strategies, has successfully stood out in the fiercely competitive local coffee market and gradually established its leadership position. This paper adopts a case study approach to analyse in depth the business strategies and marketing strategies of Luckin Coffee in recent years. By comparatively examining the differences between Luckin and other brands, we are able to summarise the key factors of its success. These factors include, but are not limited to: efficient management system, innovative business model, strong marketing promotion, and keen insight into market trends. In addition, this paper discusses the challenges faced by Luckin Coffee, such as how to cope with increasing market competition, how to maintain customer loyalty, and how to adapt to changing consumer demands. In response to these challenges, this paper proposes some specific solutions, aiming to help Luckin Coffee further improve its business strategies and promote its long-term development.

Credit cards, as an effective instrument to address household economic risks, play an important role in household economic behavior and decision-making. This paper is based on data from the 2015–2021 China Household Finance Survey (CHFS) and investigates the impact of credit card usage on household financial vulnerability. The research results indicate that the use of credit cards significantly reduces household financial vulnerability, which is achieved by alleviating risks of income, unemployment, and health. Heterogeneity analysis shows that the impact of using credit cards is more pronounced for households with lower education levels, those without any members working in the public sector, and those without critical illness insurance. Furthermore, the use of credit cards can alleviate the negative impacts of natural disasters and critical illnesses on household finances, greatly enhancing the ability of households to withstand risks.

China has entered a "new normal" characterized by high-quality development driven by digital intelligence. The 2024 Government Work Report emphasizes that the logistics industry is gradually forming a new green logistics ecosystem through technological innovation and industrial restructuring, laying the groundwork for a green, low-carbon supply chain. The role of national logistics hubs in urban logistics aggregation and radiation is continuously enhancing. However, logistics enterprises remain at the initial stage of environmentally sustainable development. Therefore, it is essential and practically significant to construct a sustainable development assessment system under the context of digital intelligence and dual carbon goals. This study focuses on the construction of a logistics assessment index system, utilizing the Triple Bottom Line (TBL) principle to conduct a comprehensive analysis of logistics processes from social, economic, and environmental dimensions. It investigates green management strategies for enterprises and proposes actionable suggestions to address issues related to an excessive focus on short-term financial targets and an incomplete performance evaluation index system. This framework aims to adapt to the demands of intelligent and flexible upgrades, thereby promoting the long-term sustainable development of logistics enterprises.

With the rapid advancement of technologies like the Internet, big data, and AI, various apps have impacted the daily lives of the elderly, widening the generational “digital divide.” Adapting apps for elderly users is crucial to addressing this issue. To address this challenge, we first focused on the middle-aged and elderly population, verifying the reliability and validity of the survey results. Then, descriptive statistics were used to analyze user behavior and preferences for the APP aging mode. Finally, ACSI path analysis and the fuzzy-IPA model were applied to assess user satisfaction. The key findings are as follows: (1) The APP aging mode is quite popular; (2) middle-aged and elderly users hesitate to use the aging mode due to “loss of original functions” and “secondary interface layout and font adjustments”; (3) better user experience in the aging mode leads to higher satisfaction, whereas higher initial expectations lead to lower satisfaction; (4) four aspects-“simple operation,” “ease of learning,” “understanding of function descriptions,” and “effective help system”-have high importance but low satisfaction levels. Overall, middle-aged and elderly users find the aging mode satisfactory but with room for improvement.

To enhance the level of emergency supplies deployment during earthquake disaster, this study focuses on emergency logistics in China. An integrated two-stage optimization framework is adopted to incorporate demand and time satisfaction indicators into the supply allocation and route optimization models, respectively. Firstly, historical data and seismic monitoring information are used to estimate the number of people affected and to forecast the need for emergency supplies; Secondly, the concept of psychological risk perception and the degree of urgency of requirements are introduced. Based on the modified prospect theory framework, this article replaces the sufficiency and shortage of demand with gains and losses to optimize the resource allocation policy. Thirdly, Particle Swarm Optimization (PSO) is used to improve the Sparrow Search Algorithm (SSA) for further model solving. The results of the study show that the two-stage optimization framework can significantly improve the rescue efficiency and rationality of resource allocation, and achieve the goal of prioritising the distribution of emergency supplies to regions with high urgency; In addition, the results of the sensitivity analysis indicate that it is crucial to determine the optimal proportion of the total amount of supplies, and the validation shows that the overall operational efficiency of PSO- SSA is higher, which provides a more reliable approach to dealing with similar emergency problems.

In the current manufacturing environment, enterprises are facing complex decision-making processes and challenges brought about by market changes. Traditional production scheduling and management methods are often insufficient to cope with these uncertainties, so it is urgent to introduce fuzzy hybrid technology to improve the accuracy of decision-making and operational efficiency. This article studies fuzzy process planning and production scheduling models and optimizes the production efficiency of manufacturing systems through the evaluation of fuzzy parameters. We use fuzzy clustering analysis method to evaluate and optimize production resource allocation. Finally, based on system requirement analysis, the overall architecture of the intelligent manufacturing execution system was designed, covering decision-making domain models and system performance testing, to effectively implement production scheduling and management strategies. Research has shown that the fuzzy hybrid production scheduling method significantly improves the flexibility and resource utilization of the production process. Through the fuzzy computing model, the efficiency of the manufacturing system has been effectively optimized, and the performance test results of the intelligent manufacturing execution system show that it can support efficient decision-making and operational management in practical applications. Therefore, fuzzy hybrid technology provides an innovative solution for decision-making and operation in the manufacturing industry, which can effectively cope with uncertainty and improve production efficiency and resource utilization.

Knowledge graph (KG) with enriched items’ related information has been widely used to alleviate the data sparsity and cold-start problems in recommender systems. However, the noise in KG that is irrelevant to a recommendation task may mislead the decision outcomes. The existing research predominantly employs data-driven modeling which uncovers the underlying patterns of the model by mining correlations within the data. This learning paradigm that lacks causality may lead to spurious associations and limit the robustness of recommendation results. To tackle this problem, this paper proposes a novel framework called knowledge graph denoising-based causal recommendation (KGDCR). In this framework, we fully combine the advantages of data-driven and model-driven modeling, and introduce a causality-driven recommendation mechanism based on causal inference. This mechanism enhances the robustness of the model by identifying the causal relationships between user behaviors and recommendation decisions. Specifically, we leverage graph attention neural networks to aggregate semantic information from the KG. Furthermore, the KGDCR captures personalized user preferences at a fine granularity by intervening in the noise. Then, we formulate a cross-view-constrained optimization problem to guide the recommendation model towards stable prediction. Experimental results demonstrate that the denoising performance and robustness of the KGDCR outperform the existing methods.

Addressing the dynamic changes in the carbon footprint (CF) of the dairy industry to meet the diverse demands of different countries and achieve the global net-zero emission target presents a significant challenge. This study integrates a systematic scoping of CF and reduction strategies, with a policy analysis utilizing Term Frequency–Inverse Document Frequency (TF-IDF) statistics to provide a comprehensive assessment of current efforts to mitigate CF in the dairy industry. Across 29 countries, the CF of dairy production ranges from 0.57 kg CO₂-eq/kg FPCM (Norway) to 5.85 kg CO₂-eq/kg FPCM (Tanzania). Mitigation strategies are implemented across the entire dairy supply chain, with a primary focus on the milk production stage. Among the six countries analyzed over a 10-year period, New Zealand demonstrated the highest policy effectiveness, largely due to measures targeting feeding practices and dairy cattle breeding. This article offers an in-depth evaluation of CF reduction in the dairy sector, integrating environmental, technological, and policy dimensions.

Light field (LF) depth estimation is a key task with numerous practical applications. However, achieving high‐precision depth estimation in challenging scenarios, such as occlusions and detailed regions (e.g. fine structures and edges), remains a significant challenge. To address this problem, the authors propose a LF depth estimation network based on multi‐region selection and guided optimisation. Firstly, we construct a multi‐region disparity selection module based on angular patch, which selects specific regions for generating angular patch, achieving representative sub‐angular patch by balancing different regions. Secondly, different from traditional guided deformable convolution, the guided optimisation leverages colour prior information to learn the aggregation of sampling points, which enhances the deformable convolution ability by learning deformation parameters and fitting irregular windows. Finally, to achieve high‐precision LF depth estimation, the authors have developed a network architecture based on the proposed multi‐region disparity selection and guided optimisation module. Experiments demonstrate the effectiveness of network on the HCInew dataset, especially in handling occlusions and detailed regions.

In the 21st century, global logistics companies have launched a huge wave of mergers and acquisitions (M&A), including many international logistics giants launching crazy M&A to adapt to the expansion strategy of globalization. The cross-border logistics market is an extremely attractive cake in the logistics market, but the express delivery business in many low tier cities in many countries has long been dominated by many local enterprises, and its market has not been fully developed and does not meet international standards. The world has experienced five waves of M&A, and now this trend is affecting the development of DHL and has a significant impact on the evolution of the logistics industry. This article introduces the background of the topic selection, research objectives and significance, as well as the examination of whether M&A are necessary. This article uses the ARIMA-GARCH model to predict DHL's future stock price, and confirms the relationship and impact between its stock price and M&A behavior by comparing actual data. Based on the company's stock liquidity, the M&A effect is evaluated. The research results indicate that M&A can enable companies to quickly acquire the resources needed for development, generate synergies through integration, establish core competitiveness, and gain competitive advantages.

The explosive fire of the black myth Wukong is not only its technical production, but also its stunning location. This paper is going to explore six of these tourist venues and Datong, a city full of tourism consumption potential. The relationship between tourism and urban development will be explored through practical cases. This paper will refer to national policies and discuss related topics of tourism and urban transformation. And put forward ways to expand the international market. Datong has the most locations for the black myth of Wukong with six. At the same time, in the process of the transformation of Datong from an energy-based economic city, tourism needs to publicize Datong in the process of playing its advantages in promoting economic growth. This paper not only analyzes the benefits in other similar Internet celebrity tourism cities, but also analyzes the new opportunities in the continuous improvement of the transportation network.

Complexity analysis of multichannel signals provides valuable insight into complex nonlinear dynamic systems. Several multivariate entropy algorithms have been proposed by extending univariate entropy measures. However, there is a lack of multivariate entropy algorithms that simultaneously capture intra- and inter-channel signal variations. In this study, a novel entropy-based approach called multivariate distance dispersion entropy (mvDDE) was proposed. Simulated and real data were used to analyze the performance of the mvDDE algorithm. Using white Gaussian and 1/f noise, we found mvDDE to be more reliable and stable, especially for short signals, compared to multivariate sample entropy (mvSE) and multivariate dispersion entropy (mvDE). In addition, mvDDE was observed to have better anti-noise performance and lower computational cost using signals simulated by the MIX model. Finally, when mvDDE was applied to the complexity analysis of electrocardiogram data from 47 different subjects, significant differences were found between two of the three types of heartbeat signals using mvDDE, and a triple classification accuracy of 80.19% was achieved, outperforming mvSE and mvDE. Analysis of electroencephalogram data from Parkinson's patients and normal subjects was performed using mvDDE, with a classification accuracy of 99.74%. In addition, significant differences were found in the central region with mvDDE, but not with mvSE and mvDE. These results showed that mvDDE exhibited good diagnostic and detection performance. Thus, mvDDE is a valuable method for detecting the nonlinear dynamics and complexity of multivariate signals.

The precision of process monitoring often encounters challenges in determining the exact shift size. Therefore, combined control charts have gained considerable attention because of their excellent speed to detect simultaneously small-to-moderate and large-size shifts. The effectiveness of the applied quality control methods strongly depends on the performance of the measurement system. Measurement error presence contributes significantly negatively toward the performance of the usual control charting schemes. This article proposes novel two-sided combined Shewhart-Cumulative EWMA-sum (Shewhart-CUESUM) control charts designed to efficiently monitor the mean of normally distributed processes. In addition, to address measurement errors, the M-Shewhart-CUESUM chart is proposed, incorporating an additive measurement error model. Evaluation of the charts through Monte-Carlo simulations, considering metrics such as average run length (ARL), extra quadratic loss, relative ARL, and performance comparison index. It is found that the combined Shewhart-CUESUM outperforms than CUESUM chart. The results show that the presence of measurement errors can significantly diminish the charts’ performance, which can be mitigated by utilizing a multiple measurements scheme. Among the different well-established combined charts examined, the M-Shewhart-CUESUM chart shows considerably more sensitive to detecting simultaneously detect small and large size shifts. To employ simulated datasets to illustrate the impact of measurement errors and demonstrate the implications of the proposed charts on process mean shifts.

A group [Formula: see text] is said to be autocapable if there exists a group [Formula: see text] such that [Formula: see text] is isomorphic to the absolute central factor group [Formula: see text] of [Formula: see text]. In this paper, we first prove that if [Formula: see text] is a characteristic subgroup of an autocapable group [Formula: see text], then [Formula: see text] is neither the generalized quaternion group nor the semi-dihedral group. Next, we give the classification of finite groups [Formula: see text] if [Formula: see text] is a 2-group of maximal class.

In the past decade, the financial industry has shown a trend of rapid development, with traditional financial institution models gradually being replaced by a coexistence of traditional and various emerging financial forms. As the operating models in the financial industry undergo corresponding changes, the types and quantity of risks that may exist within the industry continue to increase. The complexity of financial risks is gradually becoming more pronounced, with factors such as changes in laws and regulations, lack of credit assessment, and data loss or leaks in internet financial trading platforms potentially leading directly to financial risks. Employing traditional work models and methods for financial risk management often makes it difficult to achieve the desired risk control outcomes. Currently, big data technology has been widely utilized in the insurance, banking, and other sectors of the financial industry. Major financial institutions continuously innovate the traditional methods of analyzing and processing data information by leveraging big data, transforming the way financial data exists in the industry. Only by actively adapting to the trends of the times and flexibly applying big data technology to practices such as credit assessment and risk alerting can financial risk management innovation be comprehensively promoted, resulting in effective control of complex financial risks. Based on this, this paper analyzes the changes and characteristics of financial risk management in the era of big data, discusses the important role and specific applications of big data technology in the field of financial risk management and proposes relevant strategies for enhancing financial risk management in the big data environment, aiming to advance the progress of financial risk management in the era of big data.