Early Warning Mechanisms Research Articles

Building Effective Academic Early Warning Mechanisms: Theoretical Framework and Practical Strategies

The academic early warning mechanism plays an important role in modern educational management, by identifying and intervening in students' academic problems early on to help them complete their studies successfully. This paper explores how to build an effective academic warning mechanism from both theoretical frameworks and practical strategies, with the aim of providing reference and guidance for educational managers and teachers.

Tutoring Systems Construction of Dynamic Early Warning Mechanism of Ideological and Political Education in Universities Based on Intelligent CAD

Tutoring Systems Construction of Dynamic Early Warning Mechanism of Ideological and Political Education in Universities Based on Intelligent CAD

Double detection of mycotoxins based on aptamer induced Fe3O4@TiO2@Ag Core − Shell nanoparticles “turn on” fluorescence resonance energy transfer

Multiple and sensitive mycotoxin detection is an essential early-warning mechanism for safeguarding human health, and preserving the environment. We synthesized a turn-on Fluorescence Resonance Energy Transfer (FRET) aptamer sensor based on the unique fluorescence quenching and substrate recognition characteristics of Ag NTs (energy receptors) and aptamer modified Fe3O4@TiO2 NP (energy donor) to detect multiple toxins using a single diagnostic approach. The addition of aflatoxin B1 (AFB1) and ochratoxin A (OTA) resulted in a change in fluorescence intensity at 510 and 650 nm, which can be employed for simultaneous recognition with detection limits of 0.94 ng·mL−1 (R2 = 0.997) and 0.54 ng·mL−1 (R2 = 0.995). The aptasensors have been successfully applied for the determination of AFB1 and OTA in grain and oil samples with high recovery rates. The approach provides novel possibilities for the development of sensitive and selective aptasensors with potential applications in aptamer-recognized multifunctional biosensing.

On-site monitoring of expansive soil slope protected by soilbags based on the universal Beidou monitoring system

To explore an effective method for deformation monitoring and behavior prediction of expansive soil slope, field tests are conducted for a flexible slope protection scheme with soilbags that has been implemented in an expansive highway soil slope. A new monitoring system, i.e., the universal Beidou deformation monitoring system, is developed to overcome the limitations of traditional Global Navigation Satellite System (GNSS) software and hardware, simplify the hardware structure and realize the power sharing mode; furthermore, this system can create and upload a large amount of monitored data to a cloud platform to enable real-time calculation. Compared with traditional GNSS, the volume of equipment required is reduced by approximately 75%, and the cost is reduced by approximately 80%. Secondly, a multilevel safety early-warning evaluation system is constructed by integrating the monitoring results of the universal Beidou deformation monitoring system, bag damage states, rainfall conditions, and slope fissure development; additionally, a deformation early-warning mechanism of flexible support of soilbags was established. Finally, the deformation and collapse of flexible supports of soilbags can be successfully predicted in the field. This research on flexible support of soilbags provides new ideas and methods of deformation monitoring, safety evaluation, and early warning for expansive soil slopes.

Data Analysis and Algorithm Innovation in Power System IntelligentMonitoring and Early Warning Technology

Data analysis and algorithm innovation play a pivotal role in enhancing power system intelligent monitoring and early warning technology. With the increasing complexity of modern power grids, the integration of advanced data analytics enables real-time monitoring, fault detection, and predictive maintenance. By leveraging machine learning algorithms, anomaly detection techniques, and big data analytics, power systems can efficiently identify potential risks and failures before they escalate into serious issues. These innovations not only improve grid reliability and resilience but also optimize resource utilization. Early warning mechanisms based on intelligent algorithms provide timely alerts, allowing for preventive measures that ensure the stability and safety of the power network. This approach fosters a smarter, more adaptive power infrastructure capable of meeting growing energy demands while minimizing downtime and disruptions.This paper presents a comprehensive investigation into the development and efficacy of an intelligent early warning system for power systems. Leveraging machine learning algorithms, IoT sensors, and cloud computing frameworks, the system aims to enhance real-time monitoring capabilities and facilitate proactive intervention and maintenance. Through a series of simulations and iterations, the study demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1 score. The integration of data analytics and classification techniques enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring the reliability and efficiency of power systems. Through a series of simulations and iterations, the study demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1 score. Specifically, the system achieves an average accuracy of 95%, precision of 92%, recall of 94%, and F1 score of 92% across multiple iterations. The integration of data analytics and classification techniques enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring the reliability and efficiency of power systems.

Microseismic Monitoring and Disaster Warning via Mining and Filling Processes of Residual Hazardous Ore Bodies

The thick ore bodies in the Xianglushan tungsten mine have been irregularly mined, forming a super large, connected irregular goaf group and tall, isolated irregular pillars inside. At the same time, there is a production capacity task of recovering residual and dangerous ore bodies. This poses the potential for serious ground-pressure disasters, such as roof caving, pillar collapse, and large-scale goaf collapse during mining. Based on the actual needs of the site, we established a microseismic monitoring system. After analyzing the mining and filling processes and their relationships, and, combined with the distribution characteristics of microseismic multiple parameters, we constructed a ground-pressure disaster warning mode and mechanism. We analyzed the stability of the goaf, further formed a warning system, and achieved disaster warning. In response to the current situation of the difficulty of early warning of ground pressure in the Xianglushan tungsten mine, continuous on-site monitoring of existing goaves, point pillars, and strip pillars, as well as analysis of stress changes during dynamic mining and filling processes, we explored scientific and reasonable early warning mechanisms and models, understanding the relationship between the changes in microseismic parameters during dynamic mining and filling processes and ground pressure, studying and improving the reliability of underground microseismic monitoring and early warning, and achieved the internal connection between building early warning systems and the prevention of ground-pressure disasters. The results indicate that the mining and filling process of the ore body is the main factor in maintaining a stable and balanced distribution of underground ground pressure in mining engineering. Microseismic monitoring can invert the evolution of ground pressure and form a feedback system with ground-pressure warning, achieving mine safety management.

Developing a BIM based digital twin system for structural health monitoring of civil infrastructure

Abstract The utilization of building information modeling (BIM) within digital technology facilitates the creation of three-dimensional representations for monitoring data in large-scale civil infrastructure. In response to the need for intelligent structural management, this study establishes a structural health monitoring (SHM) system and foundational framework based on digital twins. This framework integrates information from various sources and facilitates collaborative efforts for structural operation and maintenance. Additionally, the SHM system integrates actual monitoring measurements and early warning mechanisms to consolidate multi-source monitoring data with BIM. Through real-time analysis, the system provides insights into the operational status of bridges, capturing geometric, physical, and performance evolution characteristics. To construct the system, engineering challenges are initially digitized, with appropriate sensors deployed on real bridge structures to monitor dynamic (acceleration) and static (strain, displacement) physical information during bridge operation. Subsequently, through wireless communication and data storage technologies, the monitored physical data serves as input for mode identification and early warning algorithms, facilitating the acquisition of structural performance information. Finally, three-dimensional display technology enables real-time calculation and rendering of BIM models, fostering the exchange and interaction of monitoring and BIM information, thus enhancing the intelligence of SHM system.

Ambient light spectrum affects larval Mexican jumping bean moth (Cydia saltitans) behavior despite light obstruction from host seed

Spectral differences in ambient light can affect animal behavior and convey crucial information about an individual’s environment. The ability to perceive and respond to differences in ambient light varies widely by taxa and is shaped by a species’ ecology. Mexican jumping bean moths, Cydia saltitans, spend their entire larval period encased in fallen host seeds and contend with potentially lethal environmental temperatures when host seeds are in direct sunlight. We investigate if and how C. saltitans larvae in host seeds respond to lighting conditions associated with these thermal risks. In a temperature-controlled experiment, we identified that larvae demonstrated distinct behavioral (“jumping”) responses corresponding to four lighting treatments (white, red, green, and purple), despite extremely minimal light penetration through host seed walls. Red light induced the greatest larval activity (measured by probability of movement and by displacement from origin), suggesting that larvae have mechanisms to perceive low levels of red light and/or to detect subtle increases in heat produced by red/near infrared-biased light spectra, possibly providing them with an early-warning mechanism against thermal stress. Our findings highlight the interplay of environmental lighting, behavior, and potential thermosensory adaptations in a species with a visually constrained environment.

Climate Change, Biased Technological Advances and Agricultural TFP: Empirical Evidence from China

The impact of climate change on agricultural quality development under the constraint of China’s “Double Carbon” target has been widely discussed by policy practitioners and academic theorists. This paper attempts to deconstruct the logic of how climate change affects agricultural total factor productivity (TFP) in three dimensions—the structure of agricultural input factors, the change in the cropping system, and the stability of crop supply. This paper also reveals the mechanism through which biased technological progress increases agricultural TFP by weakening the magnitude of climate change and empirically tests it by using China’s provincial-level data from 2000 to 2021. This study showed that average annual temperature and annual precipitation had significant negative effects on agricultural TFP, that the number of sunshine hours had a significant positive effect on agricultural TFP, and that obvious regional differences existed in the effect of climate change on agricultural TFP. Further mechanism tests revealed that biased technological progress positively moderated the effect of climate change on agricultural TFP. Based on these findings, the appropriate countermeasures for improving climate early warning mechanisms, promoting the progress of appropriate technology, and fostering new agricultural management bodies.

Gendered Perspectives on the Recruitment and Use of Child Soldiers in Early Warning

ABSTRACT The recruitment and use of children by armed groups continue to threaten the effective protection of children. Preventive approaches to child protection, call for robust early warning mechanisms that do not only focus on the recruitment and use of children, but capture the root causes and vulnerabilities of both boys and girls for recruitment. A comprehensive early warning mechanism for prevention requires a gender-sensitive approach. This paper seeks to examine the gender dynamics of the recruitment and use of children by armed groups, and how that can inform a gender sensitive data collection and analysis to improve early warning.

Early-warning of unsafe hoisting operations: An integration of digital twin and knowledge graph

Unsafe hoisting operations have been consistently associated with numerous safety incidents involving tower cranes. Currently, the predominant measures to mitigate these operations center around comprehensive training and education, emphasizing standardized protocols prior to hoisting activities. Despite concerted efforts in this direction, a conspicuous research gap persists in early-warning mechanisms during the construction phase. This paper aims to address this gap by proposing an innovative early-warning methodology, inspired by the principles of digital twin and knowledge graph. We firstly introduce a digital twin framework designed to mirror the real-time operational status of the tower crane. This framework enables the immediate detection of deviations or infractions as they occur. Subsequently, we develop a knowledge graph capable of promptly identifying unsafe hoisting operations by leveraging real-time data obtained from the digital twin. To validate the efficacy of our proposed methodology, we construct a scaled-down replica of a tower crane and establish a tailored digital twin system. The findings of a series of experimental trials prominently underscore the system's capability to generate timely alerts in response to unsafe hoisting operations while maintaining an impressively low rate of false alarms.

Missed Signals:Analyzing the Failure of Intelligence Cooperation in ASEAN in Preventing the Jolo Cathedral Bombings 2019

This research examines the failure of ASEAN Our Eyes (AOE) to face the 2019 Jolo Cathedral bombings. ASEAN's first intelligence cooperation initiative, known as AOE, was established in 2018 in response to the growing threat of transnational terrorism (ISIS) in the region. The Jolo Cathedral bombing was one of the deadliest suicide bombings in the Philippines, demonstrating the inability of AOE as a counterterrorism mechanism to face the threat of transnational terrorism. The findings of this research shed light on the two major factors - the absence of an agreed standard of procedure and the lack of early warning systems - that contributed to the failure of the AOE. The gaps identified highlight areas for improvement in intelligence cooperation and underscore the need for standardized procedures and robust early warning mechanisms to enhance ASEAN's collective response to transnational terrorist threats.

Research on Port Risk Assessment Based on Various Meteorological Disasters

Within the framework of economic globalisation, ports serve as critical junctures in international trade and play a vital role. However, as infrastructure is closely linked to the natural environment, ports are highly susceptible to the impacts of meteorological disasters. Therefore, a comprehensive assessment of the risks posed by meteorological hazards to ports, establishing corresponding early warning mechanisms, and adopting reasonable response and recovery strategies, is paramount in ensuring the safe operation of ports and maintaining the stability of international trade. This study has comprehensively analysed historical data and identified the pre-established loss stratification system, improving the theoretical construct of “expected loss”. Additionally, this research has innovatively integrated the idea of preventative factors aligned with risk indicators. A quantitative algorithm was used to factor in the preventative factors within the computational procedure, deriving the weights pertinent to each risk indicator. This research aimed to reduce the subjectivity inherent in the weighting assignment process through such an approach, thereby enhancing disaster risk assessment’s scientific rigour and reliability. Moreover, it underscores the critical role of adaptive urban planning in enhancing the resilience of crucial economic nodes like ports, thereby contributing to the broader objectives of sustainable urban development.

Early warning for thermal runaway in lithium-ion batteries during various charging rates: Insights from expansion force analysis

Thermal runaway introduces a significant challenge in the widespread application of lithium-ion batteries, necessitating advanced early-warning technologies to ensure safety, particularly during charging. Only monitoring the temperature and voltage limit the performance of diagnostic algorithms. The expansion behavior of batteries, which is linked to their operating status, offers a vital indicator of safety. In this study, the evolution of multidimensional signals during overcharging experiments at different current rates is comprehensively investigated. The result shows that the abnormal expansion force can be detected at temperatures as low as 35.4 °C, which achieves an early warning signal 11 min earlier than the onset of battery thermal runaway. The effect of charging rate on battery safety is comprehensively analyzed, showing that the time interval between the warning signal of the expansion force and temperature increases steadily from 151 s to 682 s as the charging rate decreases. However, the charging rate hardly affects the stage of charge boundary of venting, which is around ±118 %. These insights are crucial for understanding early warning mechanisms in overcharged batteries, offering valuable guidance for enhancing the safety of electric vehicles and energy storage systems.

Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention.

The global incidence of infectious diseases has increased in recent years, posing a significant threat to human health. Hospitals typically serve as frontline institutions for detecting infectious diseases. However, accurately identifying warning signals of infectious diseases in a timely manner, especially emerging infectious diseases, can be challenging. Consequently, there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals. This paper examines the role of medical data in the early identification of infectious diseases, explores early warning technologies for infectious disease recognition, and assesses monitoring and early warning mechanisms for infectious diseases. We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems, in compliance with national strategies to integrate clinical treatment and disease prevention. Furthermore, hospitals should establish institution-specific, clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.

Study on Spatio-Temporal Indexing Model of Geohazard Monitoring Data Based on Data Stream Clustering Algorithm

Yunnan Province, residing in the eastern segment of the Qinghai–Tibet Plateau and the western part of the Yunnan–Guizhou Plateau, faces significant challenges due to its intricate geological structures and frequent geohazards. These pose monumental risks to community safety and infrastructure. Unfortunately, conventional spatial indexing methods struggle with the enormous influx of geohazard data, exhibiting inadequacies in efficient spatio-temporal querying and failing to meet the swift response imperatives for real-time geohazard monitoring and early warning mechanisms. In response to these challenges, this study proffers a cutting-edge spatio-temporal indexing model, the BCHR-index, undergirded by data stream clustering algorithms. The operational schema of the BCHR-index model is bifurcated into two stages: real-time and offline. The real-time phase proficiently uses micro-clusters shaped by the CluStream algorithm in unison with a B+ tree to construct indices in memory, thereby satisfying the exigent response necessities for geohazard data streams. Conversely, the offline stage employs the CluStream algorithm and the Hilbert curve to manage heterogeneously distributed spatial objects. Paired with a B+ tree, this framework promotes efficient spatio-temporal querying of geohazard data. The empirical results indicate that the indexing model implemented in this study affords millisecond-level responses when faced with query requests from real-time geohazard data streams. Moreover, in aspects of spatial query efficiency and data-insertion performance, it demonstrates superior results compared to the R-tree and Hilbert-R tree models.

Assessing the supply risks of critical metals in China's low-carbon energy transition

The unprecedented low-carbon energy transition has heightened concerns about the security of critical metals (CMs) supplies that are essential for clean energy technologies. As the world's largest consumer and importer, China’s CMs supply may face significant challenges due to geopolitical uncertainties, price volatility, and other dynamics. Here, we introduce a risk-modeling framework to holistically gauge the multifaceted supply risks for 30 CMs embedded in clean energy technology spanning from 2008 to 2020. Our analysis indicates that approximately one-third of CMs supplies grapple with elevated risk, and half of these CMs are associated with electric vehicle manufacturing. These risks stem mainly from significant disruption potential (e.g., lithium and palladium) and substantial import reliance (e.g., nickel and niobium). Although China's overall CMs supply risk has remained relatively stable, the nation has grown increasingly susceptible to disruptions, especially with the surge in clean energy initiatives and associated price hikes. Our detailed analysis of the risk comparison reveals that China's supply risk for nine metals (e.g., copper and chromium) exceeds that of other countries that consume large amounts of CMs. Therefore, by adopting focused strategies related to metals, both governments and industries could benefit from global partnerships, strategic stockpiling, early warning mechanisms and sustainable supply chain management, paving the way for a smooth low-carbon energy transition for China.

Research on the Mechanism of Corn Price Formation in China Based on the PLS-SEM Model.

After the cancellation of the temporary corn storage system in 2016, the price of corn in China returned to market regulation, resulting in increased price volatility. This study focuses on monthly data from April 2016 to March 2023 in China. The partial least squares structural equation modeling (PLS-SEM) is employed to analyze the impact of corn supply and demand factors, substitute prices, monetary supply, international corn prices, and international energy prices on the volatility of corn prices in China. Results indicate that supply and demand factors are the most direct influencers of corn prices, with demand factors having the most significant impact. Monetary supply and substitute prices affect corn prices through the demand side. External factors mainly consist of international energy and corn prices. The impact of international energy on Chinese corn prices is achieved through international corn prices, which directly influence the prices in China. It is recommended to stabilize corn market prices by regulating corn supply and demand, to improve the monitoring and early warning mechanisms for international energy and corn prices, and to implement measures for prudent regulation of monetary supply.

Summary of key viral respiratory infectious diseases surveillance in some countries and regions in the world

Respiratory virus infections spread quickly, and some diseases are even more likely to cause a global epidemic. In order to explore how better to carry out surveillance of viral respiratory infectious diseases, the author searched the surveillance situation of key viral respiratory infectious diseases in America, Europe, and China, et al., then put forward several suggestions such as strengthening application of information technology, broaden monitoring channels, building early monitoring and early warning mechanisms.

Spatio-temporal analysis and risk modeling of foot-and-mouth disease outbreaks in China

FMD is an acute contagious disease that poses a significant threat to the health and safety of cloven-hoofed animals in Asia, Europe, and Africa. The impact of FMD exhibits geographical disparities within different regions of China. The present investigation undertook an exhaustive analysis of documented occurrences of bovine FMD in China, spanning the temporal range from 2011 to 2020. The overarching objective was to elucidate the temporal and spatial dynamics underpinning these outbreaks. Acknowledging the pivotal role of global factors in FMD outbreaks, advanced machine learning techniques were harnessed to formulate an optimal prediction model by integrating comprehensive meteorological data pertinent to global FMD. Random Forest algorithm was employed with top three contributing factors including Isothermality(bio3), Annual average temperature(bio1) and Minimum temperature in the coldest month(bio6), all relevant to temperature. By encompassing both local and global factors, our study provides a comprehensive framework for understanding and predicting FMD outbreaks. Furthermore, we conducted a phylogenetic analysis to trace the origin of Foot-and-mouth disease virus (FMDV), pinpointing India as the country posing the greatest potential hazard by leveraging the spatio-temporal attributes of the collected data. Based on this finding, a quantitative risk model was developed for the legal importation of live cattle from India to China. The model estimated an average probability of 0.002254% for FMDV-infected cattle imported from India to China. TA sensitivity analysis identified two critical nodes within the model: he possibility of false negative clinical examination in infected cattle at destination (P5) and he possibility of false negative clinical examination in infected cattle at source(P3). This comprehensive approach offers a thorough evaluation of FMD landscape within China, considering both domestic and global perspectives, thereby augmenting the efficacy of early warning mechanisms.