Early-warning Framework Research Articles

EWDPS: A Novel Framework for Early Warning and Detection on Ethereum Phishing Scams

EWDPS: A Novel Framework for Early Warning and Detection on Ethereum Phishing Scams

Rockburst early-warning method based on time series prediction of multiple acoustic emission parameters

Rockburst early-warning is crucial for ensuring safety in deep underground engineering. Existing methods primarily focus on classifying rockburst grades, making it challenging to provide timely warnings. This paper proposes a novel rockburst early-warning framework based on time series prediction of acoustic emission (AE) parameters. Six AE parameters (rise time, count, duration, amplitude, absolute energy, and peak frequency) were identified as potential indicators for rockburst early-warning based on rockburst tests. A sliding window method was applied to process normalized AE data, calculating statistical parameters of the local duration. An LSTM-based time series prediction model was developed to forecast the future evolution of these AE parameters. This, in turn, enabled the establishment of a comprehensive multi-indicator early-warning system. The Isolation Forest (IF) algorithm, an outlier detection method, was used to determine the warning thresholds for each indicator. The CRITIC weighting method was employed to integrate the six rockburst indicators into a single early-warning coefficient (EC), with EC=100 signifying the warning trigger condition. The results demonstrate that the proposed framework effectively captures the evolution trends of AE parameters, enabling proactive early warnings. This approach addresses the limitations of existing methods, such as reliance on experience for threshold determination, lack of a clear basis for multi-indicator weights, and difficulty in quantifying early-warning trigger conditions. The framework provides a new perspective for rockburst early-warning systems.

Prediction and Early Warning of Extreme Winds for High-Speed Railway Bridge Construction Using Machine-Learning Methods

Measuring the impact of extreme winds is important in high-speed railway bridge construction to avoid the risk of engineering accidents. This study presents an early-warning framework for high-speed railway (HSR) bridge construction under extreme wind conditions, using a long-span continuous beam bridge constructed in a typhoon-prone area as a case study. Specifically, on-site wind environment measurements during a typhoon are utilized, and parameters such as fluctuating wind turbulence intensity, gust factor, turbulence integral scale, and power spectral density are employed to characterize the wind environment. Multi-step prediction of gust wind speed during the bridge construction is performed using the hybrid Back Propagation-Genetic Algorithm (BP-GA). Hierarchical warnings and control actions are proposed and implemented based on the prediction results. The analysis results of wind parameters revealed a discrepancy between the measured and specified typhoon turbulence intensities, with the windward turbulence intensity being lower than specified. The longitudinal average gust factor exceeded the transverse value. The prediction curve based on the BP-GA algorithm closely resembles the actual curve, meeting accuracy requirements. Notably, the one-step prediction provided the most accurate results. Based on the predicted wind speed trend for the upcoming half-hour, appropriate hierarchical warnings and control measures were conducted.

An integrated ecological security early-warning framework in the national nature reserve based on the gray model

Nature reserves (NRs) play a pivotal role in minimizing habitat loss and protecting wild animals and plants, which are critical for human ecological security. However, focusing only on the construction of ecological security patterns of NRs without understanding their ecological security early-warning situations and their driving factors may fail to achieve protection goals. This study constructed an ecological security early-warning framework and index system based on the Driving force-Pressure-State-Impact-Response (DPSIR) framework model. The gray model (GM) was used to predict the ecological security early-warning situation, and the Geo-detector model was applied to explore the driving factors of the ecological security early-warning system in the Baishuijiang National Nature Reserve (BNNR). The results showed that the average ecological security index (ESI) value increased from 0.2796 in 2005 to 0.3171 in 2017, with an average increase of 11.82%. The ecological security early-warning index (ESEWI) value increased from 0.3171 in 2018 to 0.3622 in 2030, which was an average increase of 12.46%. These results indicated that the ecological security situation continually improved from 2005 to 2030. By 2030, the number of towns with a “no warning” grade increased to four, the number of towns with an “extreme warning” grade was zero, and the proportion of areas with early-warnings decreased from 100% to 33%. The q values of per capita forest land areas and per capita grassland areas were both 0.9334, which indicated that environmental characteristic factors were the primary driving factors in ecological security early-warning. Our results demonstrated that the ecological security early-warning index system based on the DPSIR model and grey model can well prediction ecological security situation and provide scientific support for the ecological protection and management of NRs.

Hybrid early-warning framework for unsafe crew acts detection and prediction

Hybrid early-warning framework for unsafe crew acts detection and prediction

Risk Early-Warning Framework for Government-Invested Construction Project Based on Fuzzy Theory, Improved BPNN, and K-Means

Government-invested construction project (GICP) has a great significance to social and economic development but suffered many risks due to its large scale, huge investment, and long construction period. The risks in GICP are complex so as to lead the project to failure; it is extremely urgent to take the risk management of GICP. This study establishes a risk early-warning framework to help the managers to understand the risk threat in advance, which supports them to make proper management strategies for the risk control. The whole framework can be concluded as three parts: information collection, data processing, and result prediction. Firstly, the 16 risk factors of GICP are identified. To express the hesitance of human decision and reduce the information loss in quantification, hesitant fuzzy linguistic term set (HFLTS) and triangular fuzzy number (TFN) are used to collect the experts’ linguistic term and transform them into numerical value. And then, these inputs are simplified into five factors based on principal component analysis (PCA), decreasing the impact of redundancy to risk early-warning. Meanwhile, the warning level is divided based on K-means, which avoids the subjectivity of experience decision. Further, the backpropagation neural network optimized by the genetic algorithm (GA-BP) is used to complete the simulation of risk value. The 75 groups of questionnaire data are used to train the network and the 10 groups are used as test set. The validation of the proposed framework has been verified with an average relative error in 7.2% and the average absolute error in 3.91. Finally, corresponding suggestions to prevent and control the different risks in GICP are put forward.

Variation of High and Low Nucleic Acid-Content Bacteria in Tibetan Ice Cores and Their Relationship to Black Carbon.

Nutrient enrichment caused by black carbon (BC) is a major ecological crisis in glacial ecosystems. The microbiological effects of BC were assessed in this study by using fluorescent fingerprinting assay based on flow cytometry (FCM) of bacterial communities with low (LNA) and high (HNA) nucleic acid-content bacteria. Here, we investigated a high-resolution temporal variation of bacterial abundance and LNA/HNA ratio in Tibetan ice cores. Our results revealed that bacterial abundance was proportional to the atmospheric BC on the glaciers. The shift of LNA functional groups to HNA functional groups in glaciers suggested BC emissions increased the proportion of highly active cells. In addition, distinct number of LNA and HNA functional groups was identified between the monsoon and non-monsoon seasons. Westerly winds with high amounts of BC accounted for high ratio of HNA functional groups during the non-monsoon season. In comparison, high moisture during the monsoon season decreased atmospheric BC loading, which increases the ratio of LNA functional groups. Correlations between BC and functional groups were very strong, showing that two functional groups may serve as early-warning indicators of microbiological effects of BC at low trophic level. Our approach provides a potential early-warning framework to study the influences of atmospheric BC on the glaciological community.

Deciphering early-warning signals of SARS-CoV-2 elimination and resurgence from limited data at multiple scales.

Inferring the transmission potential of an infectious disease during low-incidence periods following epidemic waves is crucial for preparedness. In such periods, scarce data may hinder existing inference methods, blurring early-warning signals essential for discriminating between the likelihoods of resurgence versus elimination. Advanced insight into whether elevating caseloads (requiring swift community-wide interventions) or local elimination (allowing controls to be relaxed or refocussed on case-importation) might occur can separate decisive from ineffective policy. By generalizing and fusing recent approaches, we propose a novel early-warning framework that maximizes the information extracted from low-incidence data to robustly infer the chances of sustained local transmission or elimination in real time, at any scale of investigation (assuming sufficiently good surveillance). Applying this framework, we decipher hidden disease-transmission signals in prolonged low-incidence COVID-19 data from New Zealand, Hong Kong and Victoria, Australia. We uncover how timely interventions associate with averting resurgent waves, support official elimination declarations and evidence the effectiveness of the rapid, adaptive COVID-19 responses employed in these regions.

A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms

Modelling air quality with a practical tool that produces real-time forecasts to mitigate risk to public health continues to face significant challenges considering the chaotic, non-linear and high dimensional nature of air quality predictor variables. The novelty of this research is to propose a hybrid early-warning artificial intelligence (AI) framework that can emulate hourly air quality variables (i.e., Particulate Matter 2.5, PM2.5; Particulate Matter 10, PM10 and lower atmospheric visibility, VIS), the atmospheric variables associated with increased respiratory induced mortality and recurrent health-care cost. Firstly, hourly air quality data series (January-2015 to December-2017) are demarcated into their respective intrinsic mode functions (IMFs) and a residual sub-series that reveal patterns and resolve data complexity characteristics, followed by partial autocorrelation function applied to each IMF and residual sub-series to unveil historical changes in air quality. To design the prescribed hybrid model, the data is partitioned into training (70%), validation (15%) and testing (15%) sub-sets. The online sequential-extreme learning machine (OS-ELM) algorithm integrated with improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is designed as a data pre-processing system to robustly extract predictive patterns and fine-tune the model generalization to a near-optimal global solution, which represents modelled air quality at hourly forecast horizons. The resulting early warning AI-based framework denoted as ICEEMDAN-OS-ELM model, is individually constructed by forecasting each IMF and residual sub-series, with hourly PM2.5, PM10, and VIS obtained by the aggregated sum of forecasted IMFs and residual sub-series. The results are benchmarked with many competing predictive approaches; e.g., hybrid ICEEMDAN-multiple-linear regression (MLR), ICEEMDAN-M5 model tree and standalone versions: OS-ELM, MLR, M5 model tree. Statistical metrics including the root-mean-square error (RMSE), mean absolute error (MAE), Willmott's Index (WI), Legates & McCabe's Index (ELM) and Nash–Sutcliffe coefficients (ENS) are used to evaluate the model's accuracy. Both visual and statistical results show that the proposed ICEEMDAN-OS-ELM model registers superior results, outperforming alternative comparison approaches. For instance, for PM2.5,ELM values ranged from 0.65–0.82 vs. 0.59–0.77 for ICEEMDAN-M5 tree, 0.59–0.74 for ICEEMDAN-MLR, 0.28–0.54 for OS-ELM, 0.27–0.54 for M5 tree and 0.25–0.53 for the MLR model. For remaining air quality variables (i.e., PM10 & VIS), the objective model (ICEEMDAN-OS-ELM) outperformed the comparative models. In particular, ICEEMDAN-OS-ELM registered relatively low RMSE/MAE, ranging from approximately 0.7–1.03 μg/m3(MAE), 1.01–1.47 μg/m3(RMSE) for PM2.5 whereas for PM10, these metrics registered a value of 1.29–3.84 μg/m3(MAE), 3.01–7.04 μg/m3(RMSE) and for Visibility, they were 0.01–3.72 μg/m3 (MAE (Mm−1)), 0.04–5.98 μg/m3 (RMSE (Mm−1)). Visual analysis of forecasted and observed air quality through a Taylor diagram illustrates the objective model's preciseness, confirming the versatility of early warning AI-model in generating air quality forecasts. The excellent performance ascertains the hybrid model's potential utility for air quality monitoring and subsequent public health risk mitigation.

EARLY WARNING AND WATER QUALITY, LOW-COST IOT BASED MONITORING SYSTEM

In Egypt, the current water quality monitoring program involves thorough physio-chemical and biological analyses. However, there is still a lack of real-time water quality data that influences the urgent decision making process. The field acquisition of such data has still been costly, lengthy and laborious. Therefore, this paper aims to present a low-cost and labor-saving Early Warning Framework (EWF) for water quality monitoring of the River Nile based on the Internet of Things (IOT). A newly developed Prototype was introduced to monitor the in-situ water quality parameters; pH, turbidity and temperature at a pilot location along the River Nile within Egypt. The same parameters were also monitored using the current state-of-the-art multi-probe EXO.Then, both sets of data measurements were sent to a real-time monitoring control center for comparison and calibration. The comparison results revealed that there is no significant difference between the two measurements according to a statistical analysis done using the Minitab 16 statistical model. The Root Mean Squared Error (RMSE) values showed that the error percentages were accepted for the three monitored parameters (0.19 for pH, 0.056 for temperature, and 0.52 for turbidity). Moreover, the overall cost of the developed Prototype including sensors, raspberry Pi and all other expenses was found to be only 197 $ as compared to 11,130 $ when using EXO. Accordingly, it could be concluded that the developed Prototype can provide a low-cost early warning system for water quality monitoring. Finally, it is strongly recommended to install developed real-time water quality monitoring stations as economic wireless hotspots at a number of strategic sites along the River Nile within Egypt.

Real-time loop-mediated isothermal amplification: an early-warning tool for quarantine plant pathogen detection

An effective framework for early warning and rapid response is a crucial element to prevent or mitigate the impact of biological invasions of plant pathogens, especially at ports of entry. Molecular detection of pathogens by using PCR-based methods usually requires a well-equipped laboratory. Rapid detection tools that can be applied as point-of-care diagnostics are highly desirable, especially to intercept quarantine plant pathogens such as Xylella fastidiosa, Ceratocystis platani and Phytophthora ramorum, three of the most devastating pathogens of trees and ornamental plants in Europe and North America. To this aim, in this study we developed three different loop mediated isothermal amplification (LAMP) assays able to detect each target pathogen both in DNA extracted from axenic culture and in infected plant tissues. By using the portable instrument Genie® II, the LAMP assay was able to recognize X. fastidiosa, C. platani and P. ramorum DNA within 30 min of isothermal amplification reaction, with high levels of specificity and sensitivity (up to 0.02 pg µL−1 of DNA). These new LAMP-based tools, allowing an on-site rapid detection of pathogens, are especially suited for being used at ports of entry, but they can be also profitably used to monitor and prevent the possible spread of invasive pathogens in natural ecosystems.

The early-warning system based on hybrid optimization algorithm and fuzzy synthetic evaluation model

Portending and mastering the atmospheric quality plays a significant role in polishing up people's daily life and preventing the environment against serious pollution because air pollution has exceeded the international warning level. Since, accurate contaminants concentration forecasting and objective evaluation, which is urgent to establish an early-warning system, is a critical issue for the social community. An early-warning framework based on two modules composed of forecasting part and assessment part is proposed and successfully adopted in this paper. The forecasting part is applied to a hybrid optimization model with improved Harmony Search Algorithm with PSO strategy to forecast air contaminants concentration, while the assessment part is applied to Fuzzy Synthetic Evaluation Model with entropy weight to evaluate the air quality levels. The proposed early-warning system was investigated in three cities of Jing-Jin-Ji region of China where there exists serious air pollution for the period August 1st 2015 to September 29st 2016. The findings showed that the forecasting model is greatly superior to statistical model and other models on the urban pollutant concentration data. According to the results of air quality assessment, the evaluation model based on the entropy technique can objectively assess the atmospheric quality level.

Predicting US Banks Bankruptcy: Logit Versus Canonical Discriminant Analysis

Using a large panel of US banks over the period 2008-2013, this paper proposes an early-warning framework to identify bank leading to bankruptcy. We conduct a comparative analysis based on both Canonical Discriminant Analysis and Logit models to examine and to determine the most accurate of these models. Moreover, we analyze and improve suitability of models by comparing different optimal cut-off score (ROC curve vs theoretical value). The main conclusions are: i) Results vary with cut-off value of score, ii) the logistic regression using 0.5 as critical cut-off value outperforms DA model with an average of correct classification equal to 96.22%. However, it produces the highest error type 1 rate 42.67%, iii) ROC curve validation improves the quality of the model by minimizing the error of misclassification of bankrupt banks: only 4.42% in average and exhibiting 0% in both 2012 and 2013. Also, it emphasizes better prediction of failure of banks because it delivers in mean the highest error type II 8.43%.

Drug-drug interaction discovery and demystification using Semantic Web technologies.

To develop a novel pharmacovigilance inferential framework to infer mechanistic explanations for asserted drug-drug interactions (DDIs) and deduce potential DDIs. A mechanism-based DDI knowledge base was constructed by integrating knowledge from several existing sources at the pharmacokinetic, pharmacodynamic, pharmacogenetic, and multipathway interaction levels. A query-based framework was then created to utilize this integrated knowledge base in conjunction with 9 inference rules to infer mechanistic explanations for asserted DDIs and deduce potential DDIs. The drug-drug interactions discovery and demystification (D3) system achieved an overall 85% recall rate in terms of inferring mechanistic explanations for the DDIs integrated into its knowledge base, while demonstrating a 61% precision rate in terms of the inference or lack of inference of mechanistic explanations for a balanced, randomly selected collection of interacting and noninteracting drug pairs. The successful demonstration of the D3 system's ability to confirm interactions involving well-studied drugs enhances confidence in its ability to deduce interactions involving less-studied drugs. In its demonstration, the D3 system infers putative explanations for most of its integrated DDIs. Further enhancements to this work in the future might include ranking interaction mechanisms based on likelihood of applicability, determining the likelihood of deduced DDIs, and making the framework publicly available. The D3 system provides an early-warning framework for augmenting knowledge of known DDIs and deducing unknown DDIs. It shows promise in suggesting interaction pathways of research and evaluation interest and aiding clinicians in evaluating and adjusting courses of drug therapy.

Design of an Early Warning System for Patients with Cardiovascular Diseases Under Mobile Environment

We proposed a framework for early warning of Cardiovascular diseases (CVDs) under mobile environment, which is a real-time and personalized problem involving various complex knowledge. Specially, the principles and methods for early warning are proposed, as well as the selection and collection of key indicators. The system integrates patients and families, physicians, hospitals and community clinics, which consists of two tiers. The mobile device tier could detect the abnormal situations of the patients, and the service center will discover and refine the knowledge of personal situation which could increase the effect and efficiency of CVD's early warning.

能源预测预警理论与方法研究进展

PDF HTML阅读 XML下载 导出引用 引用提醒 能源预测预警理论与方法研究进展 DOI: 10.5846/stxb201306091520 作者: 作者单位: 北京师范大学环境学院,北京师范大学环境学院,北京师范大学环境学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家科技支撑计划课题(2012BAK30B03); 国家自然科学基金(41271543, 91325302); 国家基金委创新研究群体科学基金(51121003); 高等学校博士学科点专项科研基金(20130003110027) Advances in theories and methods of energy forecasting and early warning Author: Affiliation: State Key Joint Laboratory of Environmental Simulation and Pollution Control,School of Environment,Beijing Normal University,State Key Joint Laboratory of Environmental Simulation and Pollution Control,School of Environment,Beijing Normal University,State Key Joint Laboratory of Environmental Simulation and Pollution Control,School of Environment,Beijing Normal University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:能源作为一种稀缺性的战略资源是国民经济增长和社会进步的物质基础,但是随着化石能源耗竭及能源使用造成的环境问题日趋严重,能源安全问题逐渐成为关注的焦点,而能源预测预警也成为能源系统科学领域的新兴学科,其内容包含能源安全理论、基于模型的能源供需预测和基于安全评价指标体系的能源预警等方面内容。通过系统回顾能源安全的理论及其演变的历程,重点综述了自上而下、自下而上和混合建模3种建模思路的能源预测模型,探讨了三类模型的优点和局限性,并根据能源安全预警评价指标浓缩信息的程度,将现有预警评价体系划分单个型指标评价体系和聚合型指标评价体系两大类。通过对以上研究内容的总结分析,明确了当前能源预测预警研究各领域的研究进展,及其在理论和应用方面的优势与不足。在未来研究中,建议从供应链的角度出发,考虑能源系统内部各因素及与外部因素的相互作用,构建基于链式的预警体系,以有效弥补现有研究中的不足。 Abstract:Energy as a scarce strategy resource is the physical base for national economic development and social progress. Along with the fossil fuel depletion and environmental problems induced by energy utilization, the early-warning of energy security has gradually attracted more attention and thereby emerged as a rising discipline of energy sciences, covering the energy security theory, energy demand forecasting models and energy security early warning index systems. After reviewing the evolution of energy security theory, we summarized the merits and limitations of three kinds of commonly used forecasting models including top-down models, bottom-up models and hybrid energy models. Top-down models such as CGE could give a detailed description about the interaction among different economic sectors. However, these models fail to provide a concrete description for energy technologies. For bottom-up model, although a detailed consideration of technologies related to energy production and consumption has been incorporated into modeling, few economies can be described adequately based on such an engineering perspective. In order to integrate the advantage of both modelling approaches, more and more hybrid models have been constructed to overcome the weakness in recent years. Although more dimensions such as environmental, social, and economic aspects have been integrated to the hybrid model, the simulation for water environment and biodiversity is still insufficient. In terms of the modelling technique, the issues like boundary difference, database difference for hybrid model need to be concerned. Additionally, the current energy demand forecasting models can only predict the future energy demand without consideration of energy security. It is therefore necessary to develop the energy security evaluation indicator system. The existing energy security evaluation indicator systems can be divided into two categories: individual indicator-based system and aggregate indicator-based system. Individual indicator system attempts to employ various individual indicators such as reserve to production ratio, strategic fuel stocks, and net energy import dependency to quantitatively assess the energy security level, while aggregated indicator system as a combination of individual indicators usually develops a composite index to evaluate the energy security. Despite many efforts have been devoted to energy demand forecasting models and energy security evaluation indicator system, a universal early-warning framework of energy security, however, has not been constructed. The limitation for current early-warning system of energy security can be summarized as follows: 1) Interaction mechanism among the dimension of energy security, environmental protection, and economy for energy security evaluation indicator is still unclear. 2) Theoretical research and practical application for existing early-warning system of energy security remain seperate. 3) Weights of the indicators are still determined by subjective judgment rather than objective methods. According to the summary of advances in various energy forecasting and early warning studies and their advantages and limitations as well, it is suggested that in the future research, a chain-based early warning system should be established in perspective of supply chain considering the interactions between internal and external factors of the energy system so as to promote the current energy early-warning system. 参考文献 相似文献 引证文献

Currency Crises During the Great Recession: Is this Time Different?

During the 2007-2009 financial crisis the foreign exchange market was characterized by large volatility and wide currency swings. In this paper we evaluate whether during the period of the Great Recession there has been a structural break in the relationship between fundamentals and exchange rates within an early-warning framework. This is done by extending the original data set by Kaminsky and Reinhart (1999) and including not only the most recent period, but also 17 new countries. Our analysis considers two variations of the original early-warning system. First, we propose two new methods to obtain the probability distribution of the early-warning indicator (conditional on the occurrence of a crisis) -- one fully parametric and one based on a novel distribution-free semi-parametric approach. Second, we compare the original early-warning indicator with a core indicator that includes only ``pseudo-financial variables'' (domestic credit/GDP, the real exchange rate, international reserves and the real interest-rate differential) and we evaluate their performance not only for currency crises during the Great Recession, but also for the Asian Crisis. All tests make us conclude that ``this time is different'', i.e. early-warning systems based on traditional macroeconomic variables have not only failed to forecast currency crises during the Great Recession, but have also significantly worsened with respect to the period of the Asian crisis.

A framework for early warning and proactive control systems in food supply chain networks

It is inherent to food supply chain networks that performance deviations occur occasionally due to variations in product quality and quantity. To reduce losses, one wants to be informed about such deviations as soon as possible, preferably even before they occur. Then it is possible to take actions to prevent or reduce negative consequences. In practice, extensive operational data is recorded, forming a valuable source for early warning and proactive control systems, i.e. decision support systems for prediction and prevention of such performance problems. Data mining methods are ideal for analyzing such data sources and extracting useable information from them. In this paper, we present a novel framework for early warning and proactive control systems that combine expert knowledge and data mining methods to exploit recorded data. We discuss the implementation of a prototype system and the experiences from a case study regarding the applicability of the framework.