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Once regional financial risks erupt, they not only affect the stability and security of the financial system in the region, but also trigger a comprehensive financial crisis, damage the national economy, and affect social stability. Therefore, it is necessary to regulate regional financial risks through artificial intelligence methods.

In this manuscript, we scrutinize the loan data pertaining to aggregated regional financial risks and proffer an ARIMA-SVR loan data regression model, amalgamating traditional statistical regression methods with a machine learning framework. This model initially employs the ARIMA model to accomplish historical data fitting and subsequently utilizes the resultant error as input for SVR to refine the non-linear error. Building upon this, it integrates with the original data to derive optimized prediction results.

The experimental findings reveal that the ARIMA-SVR (Autoregress Integrated Moving Average Model-Support Vector Regression) method advanced in this discourse surpasses individual methods in terms of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) indices, exhibiting superiority to the deep learning LSTM method.

An ARIMA-SVR framework for the financial risk recognition is proposed. This presentation furnishes a benchmark for future financial risk prediction and the forecasting of associated time series data.

The construction industry has an intricate and constantly evolving supply chain (SC) that includes multiple stakeholders and processes. Enhancing SC resilience (SCR) is essential for promoting industry expansion and competitiveness. The SCR has attracted considerable interest because of the increasing worldwide challenges and threats facing SCs. This study introduces a conceptual framework for recognizing and classifying resilient supply chains’ critical success factors (CSFs) in sustainable construction.

This research employs a sequential mixed-methods approach, combining qualitative and quantitative analyses. Initially, an extensive literature review and expert consultations were conducted to identify potential CSFs. The fuzzy decision-making trial and evaluation laboratory (DEMATEL) method was then applied to classify these CSFs into cause-and-effect groups. To further analyse the interrelationships among the CSFs, interpretive structural modelling (ISM) was utilised, establishing a hierarchical framework. This integrated methodology provides a comprehensive understanding of the CSFs’ relative importance and their driving and dependence power, facilitating targeted interventions for enhancing supply chain resilience in the construction sector.

This integrated methodology establishes a hierarchical ranking of CSFs across five levels by revealing their causal interrelationships. The study addresses crucial SCR issues and provides actionable insights. The 17 finalized CSFs have 12 causal and five effect factors. The “safety measures and guidelines” have the highest driving potential, whereas the “availability of standardized materials/equipment” and “skilled human resources” depend highly on causal CSFs. The findings will offer a detailed perspective to narrow the gap between unsustainability and sustainability by enhancing the resilience of CSC.

It offers a pragmatic framework for allocating resources and efforts to enhance the resilience of construction supply chains (CSCs). Policymakers can also use this study to create a more resilient SC for sustainable construction.

This study uniquely identifies and evaluates CSFs for SCR in the construction industry, providing a novel analysis of their driving power, dependence power and ranking. No previous studies have demonstrated such a comprehensive evaluation.

It is essential to level the drilling platform across which a drilling robot travels in a slant underground coal mine tunnel to ensure smooth operation of the drill rod. However, existing leveling methods do not provide dynamic performance under the drilling conditions of the underground coal mine. A four-point dynamic leveling algorithm is presented in this paper based on the platform attitude and support rod displacement (DLAAD). An experimental drilling robot demonstrates its dynamic leveling capability and ability to ensure smooth drill rod operations.

The attitude coordinate of the drilling robot is established according to its structure. A six-axis combined sensor is adopted to detect the platform attitude, thus revealing the three-axis Euler angles. The support rod displacement values are continuously detected by laser displacement sensors to obtain the displacement increment of each support rod as needed. The drilling robot is leveled according to the current support rod displacement and three-dimensional (3 D) attitude detected by the six-axis combined sensor dynamically.

Experimental results indicate that the DLAAD algorithm is correct and effectively levels the drilling platform dynamically. It can thus provide essential support in resolving drill rod sticking problems during actual underground coal mine drilling operations.

The DLAAD algorithm supports smooth drill rod operations in underground coal mines, which greatly enhances safety, reduces power consumption, and minimizes cost. The approach proposed here thus represents considerable benefits in terms of coal mine production and shows notable potential for application in similar fields.

The novel DLAAD algorithm and leveling control method are the key contributions of this work, they provide dynamical 3 D leveling and help to resolve drill rod sticking problems.

This paper aims to evaluate the construction supply chain (CSC) by examining its relationships with various key areas and its development, identifying gaps and outlining potential future research directions that affect the implementation of CSC standards during the timeframe of the United Nations’ “Decade of Action” plans in the past two decades.

This paper reports on a systematic literature review with bibliometric analysis that investigates publications from around the world on various aspects of CSC. These aspects include research methodology/data collection technique, inquiry mode, country-specific research, focused areas of study, the research aims and publication periods.

The findings of the study reveal that information technology, information sharing, collaboration, performance measurement and CSC configuration have received considerable attention and analysis. However, financial management, supply chain resilience, logistics, vendor managed inventory and rural CSC have been identified as significant areas that require further investigation since limited attention has been given to them in the existing literature.

CSC is a very dominant topic in the current study, but there are some limitations to it. Scopus and Web of Science databases were used to conduct the study. A future study can therefore consider papers related to other databases. As the focus was specifically dedicated to construction material SC only, the papers associated with SCs of labours and equipment have been eradicated.

To the best of the authors’ knowledge, this is the first structured and systematic literature review that identifies the issues related to the CSC during the timeframe of the United Nations’ “Decade of Action” plans and proposes future research directions to enhance the effectiveness and efficiency of CSC.