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At present, the daily operation and maintenance of large-scale data centers such as banks in China, due to a variety of reasons, often brings about the problem of unexpected events that are difficult to locate. In order to ensure that the systems running in the data center work efficiently, this paper proposes a method for improving the operation, maintenance, and monitoring of financial data centers based on the cloud-native model. First, we sequentially cleanse and process the financial center data to eliminate any negative impact and generate a time-trending correlation of financial attributes. We then apply association mining to data center operation and maintenance, using stock information as an example to analyze the operational results in stock trading transactions. The result of correlation mining is component B index (up)⇒ component A index (up), support = 12/100, confidence = 12/19, which indicates that in 100 trading days, the number of days that the component B index and the component A index rise together is 12 days, while the number of days that the component B rises alone is 19 days. In the case study examining the impact of association mining in stock trading, on March 15, 2022, the stock price experienced a rise from 11.456 to 11.498 within a mere 0.1s. The financial data operation and maintenance system, using association mining, identified this as “abnormal,” demonstrating the model’s successful detection of abnormal behavior.

Since the financial crisis, financial networks, such as interbank lending networks, have been highly concerned by regulators in terms of their vulnerability. To study the risk contagion power of financial networks deeply, this paper integrates short-term and long-term lending data among commercial banks and stress tests the Chinese interbank lending network by using the extended distress and default contagion model. The results show that the reconstructed short-term lending network utilizing information on bank assets exhibits a core–periphery structure with systemically important banks at its core. Stress test results suggest that an interbank lending network that does not consider long-term lending data underestimates the network’s systemic risk when market conditions are poor. In addition, we find that the Chinese interbank lending network has gradually become more risk-resistant over time, and the vulnerability of the network after the outbreak of COVID-19 is lower than that before. Finally, we suggest that regulators can make full use of ancillary data, such as long-term interbank lending data, to stress test financial networks, thereby accurately capturing the network’s structure and preparing for ex-ante interventions before a crisis strikes to mitigate network systemic risk.

The Financial Industry Regulatory Authority began collecting transaction data from brokerdealers in 2011 as a step toward enhancing its understanding of securitization markets. We use transaction data to document the importance of the interdealer network structure to market quality. Some dealers are relatively central in the network and trade with many dealers, while others are peripheral. Core dealers receive relatively lower and less dispersed spreads than peripheral dealers. We develop a model in which core and peripheral dealers trade with different customer clienteles and argue that the presence of relatively sophisticated customers in securitization markets explains these facts.

Type of the article: Research Article AbstractThe accelerating digital and green transition has intensified the role of financial centers as investment conduits, making the relationship between financial strength and smart city development both timely and policy-relevant. This study aims to examine whether and to what extent the Global Financial Centres Index (GFCI) explains variation in the Smart Centres Index (SCI) across a global sample of cities. The analysis relies on panel data covering 78 cities from 2019 to 2025, with all calculations performed in R Studio using fixed effects, random effects, and robust error-corrected estimators. The findings reveal a sharp contrast between specifications: while the fixed-effects model detects no significant relationship (β = 0.0013, p = 0.963), the random-effects model identifies a positive and statistically significant link (β = 0.0858, p < 0.001), explaining about 56% of SCI variation (R² = 0.557). Robustness checks with clustered and Driscoll–Kraay standard errors confirm the stability of this result. City-level effects highlight London (+60.24), New York (+54.39), and Singapore (+39.46) as leading overperformers, while New Delhi (–74.49) and Mumbai (–68.15) emerge as underperformers. These outcomes demonstrate that financial strength matters for smart city advancement, but local governance, infrastructure, and innovation ecosystems critically shape whether financial capacity translates into smart development.

In the context of the digital economy, the digital transformation of enterprises, as an important accelerator of new economic and social development, cannot be separated from the support of financial resources. Based on the data of China’s A-share listed companies from 2011 to 2020, this paper studies the influence of digital finance development on the digital transformation of enterprises and its mechanism of action. The empirical results demonstrate that digital finance development plays a significant role in promoting the digital transformation of enterprises, and the promotion effect is stronger for nonstate-owned enterprises, but the promotion effect of digital finance development on the digital transformation of enterprises is weaker in western regions and peripheral cities than that in eastern and central regions and central cities. Digital finance development can alleviate the financing constraint of enterprises, thus facilitating the digital transformation of enterprises. Digital finance development can drive enterprise innovation, thus promoting the digital transformation of enterprises. Therefore, this paper suggests that the government should steadily advance digital finance development. Meanwhile, financial institutions should speed up the construction of digital platforms and strengthen their support for innovative projects. In addition, enterprises should actively seize the opportunities brought by digital finance development and accelerate the construction of digital transformation.

Purpose This study examined the prevalence of financial toxicity (FT) and associated factors among urologic cancer patients. The association between FT and health-related quality of life (HRQoL) was also investigated. Methods A total of 429 respondents diagnosed with urologic cancers (prostate cancer, bladder and renal cancer) from Sarawak General Hospital and Subang Jaya Medical Centre in Malaysia were interviewed using a structured questionnaire. Objective and subjective FT were measured by catastrophic health expenditure (healthcare-cost-to-income ratio greater than 40%) and the Personal Financial Well-being Scale, respectively. HRQoL was measured with the Functional Assessment of Cancer Therapy – General 7 Items scale. Results Objective and subjective FT were experienced by 16.1 and 47.3% of the respondents, respectively. Respondents who sought treatment at a private hospital and had out-of-pocket health expenditures were more likely to experience objective FT, after adjustment for covariates. Respondents who were female and had a monthly household income less than MYR 5000 were more likely to experience average to high subjective FT. Greater objective FT (OR = 2.75, 95% CI 1.09–6.95) and subjective FT (OR = 4.68, 95% CI 2.63–8.30) were associated with poor HRQoL. Conclusions The significant association between both objective and subjective FT and HRQoL highlights the importance of reducing FT among urologic cancer patients. Subjective FT was found to have a greater negative impact on HRQoL.

Health care data breaches are the most rapidly increasing type of cybercrime; however, the predictors of health care data breaches are uncertain. This quantitative study aims to develop a predictive model to explain the number of hospital data breaches at the county level. This study evaluated data consolidated at the county level from 1032 short-term acute care hospitals. We considered the association between data breach occurrence (a dichotomous variable), predictors based on county demographics, and socioeconomics, average hospital workload, facility type, and average performance on several hospital financial metrics using 3 model types: logistic regression, perceptron, and support vector machine. The model coefficient performance metrics indicated convergent validity across the 3 model types for all variables except bad debt and the factor level accounting for counties with >20% and up to 40% Hispanic populations, both of which had mixed coefficient directionality. The support vector machine model performed the classification task best based on all metrics (accuracy, precision, recall, F1-score). All the 3 models performed the classification task well with directional congruence of weights. From the logistic regression model, the top 5 odds ratios (indicating a higher risk of breach) included inpatient workload, medical center status, pediatric trauma center status, accounts receivable, and the number of outpatient visits, in high to low order. The bottom 5 odds ratios (indicating the lowest odds of experiencing a data breach) occurred for counties with Black populations of >20% and <40%, >80% and <100%, and >40% but <60%, as well as counties with ≤20% Asian or between 80% and 100% Hispanic individuals. Our results are in line with those of other studies that determined that patient workload, facility type, and financial outcomes were associated with the likelihood of health care data breach occurrence. The results of this study provide a predictive model for health care data breaches that may guide health care managers to reduce the risk of data breaches by raising awareness of the risk factors.

'Catastrophe Risk Financing in Developing Countries' provides a detailed analysis of the imperfections and inefficiencies that impede the emergence of competitive catastrophe risk markets in developing countries. The book demonstrates how donors and international financial institutions can assist governments in middle- and low-income countries in promoting effective and affordable catastrophe risk financing solutions. The authors present guiding principles on how and when governments, with assistance from donors and international financial institutions, should intervene in catastrophe insurance markets. They also identify key activities to be undertaken by donors and institutions that would allow middle- and low-income countries to develop competitive and cost-effective catastrophe risk financing strategies at both the macro (government) and micro (household) levels. These principles and activities are expected to inform good practices and ensure desirable results in catastrophe insurance projects. 'Catastrophe Risk Financing in Developing Countries' offers valuable advice and guidelines to policy makers and insurance practitioners involved in the development of catastrophe insurance programs in developing countries.

Pediatric hematopoietic stem cell transplantation (HSCT) is a complex process that impacts the entire family. The traumatic nature of the pediatric HSCT period makes this a particularly vulnerable time for parents, leading to coping challenges. This study aimed to explore parents' experiences regarding the obstacles and facilitators of coping with their child's HSCT. This qualitative study used a conventional content analysis method. The study took place at largest Children's Medical Center in Iran from February to November 2023. The study utilized purposive sampling for selecting participants. Data collection began with unstructured interviews, followed by in-depth semi-structured interviews with open-ended questions. Sampling continued until data saturation was achieved after examining qualitative data from 20 participants. The qualitative analysis identified eight subcategories grouped into two main categories: \"variable support\" and \"beliefs and individual situation\". Support varied widely, with significant roles played by family, friends, healthcare providers, non-governmental organizations, and desirable beliefs and individual situation. However, inadequate family support, financial stress, and conflicts with healthcare teams were notable barriers. The findings underscore the need for comprehensive support systems and targeted interventions to address the emotional and practical challenges families face during their child's HSCT vulnerable period. Future efforts should focus on enhancing support structures and addressing barriers to improve the overall coping experience for parents.