Risk Warning Research Articles

A Nomogram model for the early warning of essential hypertension risks based on the principles of traditional Chinese medicine syndrome elements

A Nomogram model for the early warning of essential hypertension risks based on the principles of traditional Chinese medicine syndrome elements

Effects of flood risk warnings on preparedness behavior: Evidence from northern China

During floods caused by extreme precipitation, early warning systems and public disaster preparedness behavior are crucial in mitigating the adverse effects of floods. The torrential rain disaster in Zhengzhou, Henan, in July 2021 is a clear reminder that these warnings did not receive adequate attention, and the general public did not take sufficient preventive measures. Previous research on risk perception and disaster preparedness behavior has often overlooked social support, a key factor. In particular, for non-disaster-prone areas, little research has been conducted on the impact of flood risk warnings, social support, and risk perception on disaster preparedness behavior during sudden disasters. To bridge this divide, this study utilizes survey data collected from urban regions of northern China that are not susceptible to disasters. The primary objective is to examine the impact of flood risk warnings, social support, and four core risk perceptions on public disaster preparedness behavior. Based on the Protective Action Decision Model, we have developed a new comprehensive impact model, which provides a framework for our analysis. The findings suggest that resource attributes and stakeholder perceptions mediate the relationship between flood risk warnings and disaster preparedness intention. Furthermore, stakeholder perception exerts a stronger mediating effect than resource attribute perception. In addition, social support, along with two primary risk perceptions, has a mediating chain effect, which enhances three risk perceptions and promotes public disaster preparedness behavior. Overall, our model explains the core mechanism underlying public disaster preparedness behavior in flood-prone areas and also provides valuable insights for policymakers to develop effective risk mitigation and communication strategies during extreme precipitation events.

Machine learning and financial big data control using IoT

Machine learning algorithms have been widely used in risk prediction management systems for financial data. Early warning and control of financial risks are important areas of corporate investment decision-making, which can effectively reduce investment risks and ensure companies’ stable development. With the development of the Internet of Things, enterprises’ financial information is obtained through various intelligent devices in the enterprise financial system. Big data provides high-quality services for the economy and society in the high-tech era of information. However, the amount of financial data is large, complex and variable, so the analysis of financial data has huge difficulties, and with the in-depth application of machine learning algorithms, its shortcomings are gradually exposed. To this end, this paper collects the financial data of a listed group from 2005 to 2020, and conducts data preprocessing and Feature selection, including removing missing values, Outlier and unrelated items. Next, these data are divided into a training set and a testing set, where the training set data is used for model training and the testing set data is used to evaluate the performance of the model. Three methods are used to build and compare data control models, which are based on machine learning algorithm, based on deep learning network and the model based on artificial intelligence and Big data technology proposed in this paper. In terms of risk event prediction comparison, this paper selects two indicators to measure the performance of the model: accuracy and Mean squared error (MSE). Accuracy reflects the predictive ability of the model, which is the proportion of all correctly predicted samples to the total sample size. Mean squared error is used to evaluate the accuracy and error of the model, that is, the square of the Average absolute deviation between the predicted value and the true value. In this paper, the prediction results of the three methods are compared with the actual values, and their accuracy and Mean squared error are obtained and compared. The experimental results show that the model based on artificial intelligence and Big data technology proposed in this paper has higher accuracy and smaller Mean squared error than the other two models, and can achieve 90% accuracy in risk event prediction, which proves that it has higher ability in controlling financial data risk.

Sports injury early warning of basketball players based on RBF neural network algorithm

Basketball has always been a relatively hot sport. However, the level of basketball in China does not maintain the synchronous development trend with competitive sports, which can be seen from the achievements of various international competitions. Many basketball players have retired due to sports injuries. How to avoid and delay the occurrence of injuries to the maximum extent, and make the best competitive state to get the longest time is an urgent problem to be solved in the current basketball training and competition process. Therefore, how to reduce sports damage in basketball sports has become a crucial problem. The artificial neural network algorithm is widely used in complex system hardware fault detection, medical diagnosis, medical image processing and other complex task, to classify and forecast, and achieved good results. But in the use of the sports injury risk prevention is very limited, in sports injury risk early warning research, predecessors to sports injury factors made a lot of research and the qualitative model was established, but no quantitative evaluation research, and artificial neural network algorithm has good performance in complex system classification and prediction, so the artificial neural network algorithm is applied to sports injury risk early warning study is a very meaningful work, can carry on the accurate to the athlete sports injury risk assessment. Using RBF neural network to achieve dimensional reduction preprocessing of high-dimensional data not only has sufficient theoretical basis, but also it is more superior. Based on the optimization study of RBF neural network algorithm, we study the data-based feature selection RBF neural network, and apply it in the high-dimensional multi-objective optimization decision space and pare to quality and disadvantages prediction. Through the evaluation of the test sample, the early warning model achieves ideal results, so it is feasible to apply to the sports injury risk warning.

Association of retinopathy with risk of all-cause and specific-cause mortality in the National Health and Nutrition Examination Survey, 2005 to 2008.

This study aimed to elucidate the relationship between retinopathy status or severity and the all-cause and specific-cause mortality risk based on the updated National Health and Nutrition Examination Survey (NHANES) database and 2019 Public Access Link mortality file. In this prospective cohort study, a total of 6,797 participants aged over 40 years based on NHANES 2005-2008 were analyzed. The severity of retinopathy was classified into 4 grades-no retinopathy, mild non-proliferative retinopathy (NPR), moderate to severe NPR, and proliferative retinopathy (PR). Multiple covariate-adjusted Cox proportional hazards regression models and Fine and Gray competing risk regression models were used to assess the all-cause and cause-specific mortality risks, respectively. The propensity score matching (PSM) approach was also applied additionally to adequately balance between-group covariates to validate our findings. A final total of 4,808 participants representing 18,282,772 United States (US) non-hospitalized participants were included for analysis, 50.27% were male (n = 2,417), 55.32% were non-hispanic white (n = 2,660), and mean [SE] age, 56.10 [0.40] years. After a median follow-up of 12.24 years (interquartile range, 11.16-13.49 years), 1,164 participants died of all-cause mortality, of which 941 (80.84%) died without retinopathy and 223 (19.16%) died with retinopathy at baseline. The presence of retinopathy was associated with increased all-cause mortality, cardiovascular disease (CVD), and diabetes mellitus (DM)-specific mortality, and the results remain consistent after PSM. Severity analysis showed that only mild NPR was associated with an increased all-cause mortality risk (hazard ratio (HR) = 2.01; 95% confidence interval (CI), 1.00-4.03), while increased CVD and DM-specific mortality risk were associated with all grades of retinopathy and were exponentially greater with increasing retinopathy severity, and the trend test was also significant (P for trend 0.004 and 0.04, respectively). Our findings suggest that the diagnosis of retinopathy is an independent risk factor for all-cause mortality in people over 40 years old. Retinopathy grading is significantly associated with the survival risk of patients with CVD or DM, it can be a valuable predictor in the stratified management and risk warning of CVD or DM patients, as well as in the monitoring of systemic vasculopathy status.

Aspartame Safety as a Food Sweetener and Related Health Hazards.

Aspartame is the methyl-ester of the aspartate-phenylalanine dipeptide. Over time, it has become a very popular artificial sweetener. However, since its approval by the main food safety agencies, several concerns have been raised related to neuropsychiatric effects and neurotoxicity due to its ability to activate glutamate receptors, as well as carcinogenic risks due to the increased production of reactive oxygen species. Within this review, we critically evaluate reports concerning the safety of aspartame. Some studies evidenced subtle mood and behavioral changes upon daily high-dose intake below the admitted limit. Epidemiology studies also evidenced associations between daily aspartame intake and a higher predisposition for malignant diseases, like non-Hodgkin lymphomas and multiple myelomas, particularly in males, but an association by chance still could not be excluded. While the debate over the carcinogenic risk of aspartame is ongoing, it is clear that its use may pose some dangers in peculiar cases, such as patients with seizures or other neurological diseases; it should be totally forbidden for patients with phenylketonuria, and reduced doses or complete avoidance are advisable during pregnancy. It would be also highly desirable for every product containing aspartame to clearly indicate on the label the exact amount of the substance and some risk warnings.

Early warning of exchange rate risk based on structural shocks in international oil prices using the LSTM neural network model

The Russia–Ukraine conflict has caused an external shock to the oil market as the exchange rate risk increased with significant depreciation of major currencies relative to the U.S. dollar (USD). In this paper, structural shocks to oil prices calculated by the Ready (2018) method are introduced in addition to traditional exchange rate predictor variables such as oil prices. Utilising a long short-term memory neural network model, we verify the early warning effect of structural shocks to international oil prices on exchange rate risk in the context of the Russia–Ukraine geo-conflict. We have several empirical findings. First, the variance decomposition method indicates that oil price shocks affect the euro-dollar exchange rate risk. After the conflict between Russia and Ukraine, the euro exchange rate risk continued to rise significantly, mainly affected by supply shocks. Second, compared with traditional prediction models, the LSTM neural network model has a higher prediction accuracy, which is conducive to improving the early warning performance of exchange rate risks and maintaining the stability of the foreign exchange market. Third, the introduction of oil price shock can fully extract and use the information from oil price changes, which significantly improves the learning performance of the oil price early warning model of exchange rate risk. Fourth, the introduction of oil price risk shocks in the whole sample, the introduction of oil price demand shocks before the Russia-Ukraine conflict, and the introduction of oil price supply shocks, especially negative supply shocks, after the conflict between Russia and Ukraine, can warn exchange rate risks more effectively. This study proposes a new method for early warning of exchange rate risk that can help cross-border enterprises and government policymakers to respond to unexpected events and effectively mitigate associated risks.

Energy finance risk warning model based on GABP algorithm

Energy finance is the product of the close combination of the energy industry and the financial industry, and the two affect each other. The energy crisis may lead to a financial crisis, and the financial crisis may also lead to a energy crisis. Early risk warning for the energy financial crisis can effectively mitigate and reduce risks. This article used the GABP (Genetic Algorithm Back Propagation) algorithm model to systematically analyze and predict the risks of energy financial crises. After establishing indicators for energy finance risk warning, this article collected relevant data from 150 energy companies and 210 financial companies, and compared them with the GABP algorithm model and manual analysis model. The error value of the model is determined by the numerical expansion in the positive and negative directions based on zero scale values. The closer the zero scale value is, the smaller the error; the farther it is from the zero scale value, the greater the error. The results show that the average accuracy of the GABP model for energy finance risk warning is 85.2%, and the minimum error value is −0.23. The average accuracy of using manual analysis models for energy finance risk warning is 75.8%, with a minimum error value of 1.89. The GABP algorithm has advantages in constructing energy finance risk warning models.

High-accuracy recognition of gas–liquid two-phase flow patterns: A Flow–Hilbert–CNN hybrid model

In multiphase flow pipelines, the timely and precise identification of flow pattern characteristics is crucial to guide industrial production, assess pipeline safety, and facilitate early risk warnings. Current research highlights that the combined application of flow imagery and convolutional neural networks (CNNs) can yield high accuracy flow pattern recognition. However, in the industrial sector, conveniently measurable signals such as pressure and flow rate present as one-dimensional signals rather than two-dimensional signals such as images and matrices, which CNNs are equipped to process. Therefore, the selection of a suitable data encapsulation type to convert one-dimensional signals into matrix or image form is vital to the successful application of CNNs in this field and to the precise identification of flow patterns in industrial production. In this research, we propose a Flow–Hilbert–CNN hybrid model, which integrates the one-dimensional liquid holdup signal, the Hilbert curve technique, and CNNs. The experimental data from multiphase flow in an undulating pipeline with low liquid holdup demonstrates that this hybrid model provides excellent classification accuracy, the model effectively identified four flow patterns—slug flow, pseudo-slug flow, wavy-stratified flow, and smooth-stratified flow—with recognition accuracies of 100.0%, 90.38%, 93.07%, and 100.0%, respectively, from a 16,384-length data source. Compared to models that employ standard data encapsulation methods (Space Folding (SF) and Fast Fourier Transform (FFT)) combination with CNNs, this hybrid model exhibits remarkable recognition accuracy and superior adaptability to data source scales. Notably, in the case of small-scale datasets, the accuracy improvement was 43.24% and 35.89% in comparison with SF- and FFT-based models, respectively. Moreover, the recognition accuracy of the hybrid model shows significant improvement when compared to results from traditional machine learning models and deep neural networks combined with the liquid holdup signal. The hybrid model, when used with different CNNs—MobileNetV2, Resnet18, and VGG16—yields recognition accuracies of 95.87%, 97.76%, and 96.64%, respectively, while the accuracies of machine learning models—deep neural network, k-nearest neighbors algorithm, and support vector machine—stand at 91.35%, 74.12%, and 90.57%, respectively. These results convincingly demonstrate the universality of combining the Hilbert curve with CNNs in the hybrid model, as well as the model's reliability and superiority in industrial application. This hybrid model can facilitate online, high-precision monitoring of flow patterns in the industrial sector and aid in preventing risk accidents within the production process.

Federated Learning-based Financial Risk Early Warning Model for Baijiu Enterprises

As the development of economic globalization deepens, the early warning and management of corporate financial risks have become increasingly crucial. This paper addresses the characteristics of the baijiu (Chinese liquor) industry and proposes a federated learning-based financial risk early warning model to achieve a balance between data sharing and risk prediction among baijiu enterprises. Through federated learning, different enterprises can collaboratively train an early warning model while safeguarding data privacy, thereby enhancing prediction accuracy and comprehensiveness. This paper begins by reviewing federated learning and the field of financial risk early warning. It subsequently presents the model's design and implementation, and concludes by demonstrating the model's effectiveness and superiority in baijiu enterprise financial risk early warning through experiments.

Comprehensive early warning method of microseismic, acoustic emission, and electromagnetic radiation signals of rock burst based on deep learning

Microseismic, acoustic emission, and electromagnetic radiation monitoring methods are often used to monitor rock burst disasters in coal mines. In the process of coal mining, the time series characteristics and amplitude characteristics of microseismic, acoustic emission, and electromagnetic radiation data are mainly used to identify rockburst risk, but the results of risk identification through the three monitoring methods are quite different. Consequently, the accurate and comprehensive early warning of rock burst risk is still an urgent problem to be solved. The development of deep learning provides a new means for intelligent early warning of rock burst risk. In this paper, a comprehensive early warning method of microseismic, acoustic emission, and electromagnetic radiation (MS-AE-EMR) signals of rock bursts was proposed based on a deep learning algorithm. This method uses long short-term memory recurrent neural networks (LSTM-RNNs) to intelligently identify the MS-AE-EMR precursor signal of rock burst risk, predicts the MS-AE-EMR signal by a convolution neural network (CNN), analyses the MS-AE-EMR precursor signal of rock burst risk through the data analysis method and obtains the risk coefficient of rock burst. Moreover, by using the MS-AE-EMR original signal and risk coefficient, it trains the multi-input CNN and inputs the predicted signal into the trained multi-input CNN to obtain the predicted risk coefficient of rock burst. Analysing the risk coefficient completes the comprehensive early warning of the MS-AE-EMR signal of rock burst. After field verification, the RNN-based comprehensive early warning method of the MS-AE-EMR signal can respond positively to rock burst risk and capture the information in advance. Therefore, this method is of great significance for accurate monitoring and early warning of rock burst in coal mines.

AdaBoost-driven multi-parameter real-time warning of rock burst risk in coal mines

The stope dynamic disaster, which occurs around the mining space and is represented by stress-type rock burst and fracture-type rock burst, seriously affects the safety production of coal mine. How to effectively forewarn rock burst risk to reduce the disaster caused by rock burst is an urgent problem to be solved in stope. In this paper, the spatiotemporal parameters that affect the occurrence of stope dynamic disaster are collected, and the big data of stope state is established. Due to the complex temporal and spatial parameters of rock bursts with different intensities occurrence and their varying degrees, it is difficult for some machine learning methods to excavate their internal relationships and make accurate warning. In this paper, (1) a big data platform is used to record and integrate the multiple parameters of stope dynamic disaster, and data preprocessing technology is used to de-noise and standardize them. (2) Based on the fused historical data, a strong classifier is iteratively found by the classification algorithm AdaBoost to identify the existence of the rock burst risk, so as to achieve the purpose of accurately and timely warning of rock burst risk. The research of this paper uses big data mining technology and machine learning method to carry out intelligent perception warning of the rock burst risk in real time. The experimental results show that the proposed method has a good effect and is of great significance to the prevention and control of rock burst disaster in stope.

A digitalization-based image edge detection algorithm in intelligent recognition of 5G smart grid

In the 5G smart grid, battery liquid leakage negatively affects the safe storage and transportation of electricity. Image edge detection help locate the liquid leakage phenomenon. This paper proposes a digitalization-based algorithm that converts the image to the codeword and judges the edge by the code weight. Its main steps are image sampling, quantization, coding and judging. Compared with traditional Sobel and Canny operators, the algorithm dramatically enhances detection ability with machine learning. Moreover, this paper applies the algorithm to detect the leakage of infrared battery images in the 5G smart grid; The results show that it can accurately extract the liquid leakage location, improving fault diagnosis and early risk warning.

Research on Big Data-driven Business Management Effectiveness Enhancement Methodology

In the digital era, business management effectiveness enhancement is an important way to promote enterprises to gain competitive advantage, and enterprises use big data to drive business model innovation and business risk warning. Based on the progress of existing data-driven business model research, the intrinsic mechanism and methods of big data-driven business model innovation are analysed based on the perspective of business model element restructuring. Meanwhile, in business risk management, especially in the financial banking sector, where risk management is the core of its business management, big data plays a crucial role.

Multi-Scenario Clustering-Based Distribution Network with Distributed Power Sources Operational Risk Warning Method

Risk assessment of the operation of distribution networks containing distributed power sources is the most important way to ensure the safe operation of distribution networks. In this paper, the main objective of the study is to assess the risk of the distribution network with distributed photovoltaic (PV) based on time-series risk analysis in multiple scenarios. Firstly, the PV output and load demand power of each time of the year are composed of multi-series data, which are clustered into several typical scenarios throughout the year, and the corresponding weights are obtained. The risk assessment and analysis are carried out in each scenario, and the risk warning is carried out according to the warning criteria. The feasibility of the proposed method is verified using an IEEE 33-node distributed system example.

Multi-parameter comprehensive early warning of coal pillar rockburst risk based on DNN

A multi-parameter comprehensive early warning method for coal pillar-type rockburst risk based on the deep neural network (DNN) is proposed in this study. By utilizing preprocessed data from the surveillance of coal pillar impact hazards in Yangcheng Coal Mine, this study incorporates training samples derived from three distinct coal pillar-type impact hazard monitoring methodologies: microseismic monitoring, borehole cutting analysis, and real-time stress monitoring. The data characteristics of the monitoring data were extracted, evaluated, classified, and verified by monitoring the data of different working faces. This method was applied to develop the depth of multi-parameter neural network comprehensive early warning software in engineering practice. The results showed that the accuracy of the depth for burst monitoring data processing is improved by 6.89%–16.87% compared to the traditional monitoring methods. This method has a better early warning effect to avoid the occurrence of coal pillar rockburst hazard.

Global energy supply risk: Evidence from the reactions of European natural gas futures to Nord Stream announcements

Motivated by recent geopolitical challenges to globalization, finance scholarship has a growing interest in the risks to economies of key supply chains. We explore the effect of Nord Stream pipeline development on daily natural gas prices in Europe. Reviewing a comprehensive list of 24 announcements related to Nord Stream pipeline developments, stoppages, and sanctions for 2013–2022, we investigate their direct impact on the volatility and returns of Dutch TTF natural gas futures. Employing bootstrap quantile regression, we find that the effects of such announcements are highly significant, especially during periods of low TTF volatility and returns distributions. Moreover, results show that both COVID-19 and the Russia-Ukraine war strenghtened the correlation between Nord Stream pipeline announcements and TTF market reactions, consistent with energy markets pricing geopolitical risks. Results, reflective of informed trading, warn of concentration risk in European gas markets, suggesting the need for policy actions to reduce supply-chain dependence. Taken together, we support the importance of diversifying both natural gas supply infrastructures and providers to mitigate geopolitical risk exposures and associated welfare costs to the real economy.

Regulating cancer risk prediction: legal considerations and stakeholder perspectives on the Canadian context

Risk prediction models hold great promise to reduce the impact of cancer in society through advanced warning of risk and improved preventative modalities. These models are evolving and becoming more complex, increasingly integrating genetic screening data and polygenic risk scores as well as calculating risk for multiple types of a disease. However, unclear regulatory compliance requirements applicable to these models raise significant legal uncertainty and new questions about the regulation of medical devices. This paper aims to address these novel regulatory questions by presenting an initial assessment of the legal status likely applicable to risk prediction models in Canada, using the CanRisk tool for breast and ovarian cancer as an exemplar. Legal analysis is supplemented with qualitative perspectives from expert stakeholders regarding the accessibility and compliance challenges of the Canadian regulatory framework. While the paper focuses on the Canadian context, it also refers to European and U.S. regulations in this domain to contrast them. Legal analysis and stakeholder perspectives highlight the need to clarify and update the Canadian regulatory framework for Software as a Medical Device as it applies to risk prediction models. Findings demonstrate how normative guidance perceived as convoluted, contradictory or overly burdensome can discourage innovation, compliance, and ultimately, implementation. This contribution aims to initiate discussion about a more optimal legal framework for risk prediction models as they continue to evolve and are increasingly integrated into landscape for public health.

Forecast Research on the Fluctuation Risk of Green Financial Market in China based on the GARCH-LSTM Mixed Model

With the rapid development of China’s green financial market, there is also the problem of uncertain risk of fluctuation in the transaction price of green financial products. Therefore, it is imperative to give play to the role of price fluctuations in early warning of risks in green financial markets. Based on the closing price data of green stock market and green bond market from April 19, 2018 to April 18, 2023, this paper combines the traditional time series model and deep learning model to fit and forecast the yield volatility of green financial market, and quantifies the risk value of green financial market. The results show that: (1) Both the green stock market and the green bond market have the characteristics of leptokurtosis and fat-tail, volatility aggregation and long-term memory to varying degrees. (2) The volatility risk in the green financial market has obvious differentiation characteristics, and the volatility in the green stock market is significantly higher than that in the green bond market. (3) The prediction accuracy of LSTM long-term and short-term memory model is significantly higher than that of GARCH family model. The GARCH-LSTM mixed model can further improve the prediction accuracy. According to the research results, the combination of modern artificial intelligence algorithm and classical econometric theory can become an important means to identify the volatility risk of green financial market, and put forward meaningful suggestions for the risk management of green financial market.

Lightning Risk Warning Method Using Atmospheric Electric Field Based on EEWT-ASG and Morpho

The current methods for lightning risk warnings that are based on atmospheric electric field (AEF) data have a tendency to rely on single features, which results in low robustness and efficiency. Additionally, there is a lack of research on canceling warning signals, contributing to the high false alarm rate (FAR) of these methods. To overcome these limitations, this study proposes a lightning risk warning method that incorporates enhanced empirical Wavelet transform-Adaptive Savitzky–Golay filter (EEWT-ASG) and one-dimensional morphology, using time-frequency domain features obtained through the Wavelet transform (WT). The proposed method achieved a probability of detection (POD) of 77.11%, miss alarm rate (MAR) of 22.89%, FAR of 40.19%, and critical success index (CSI) of 0.51, as evaluated on 83 lightning events. This method can issue a warning signal up to 22 min in advance for lightning processes.