Early Warning Platform Research Articles

A multifunctional evanescent wave biosensor for the universal assay of SARS-CoV-2 variants and affinity analysis of coronavirus spike protein-hACE2 interactions

The conventional detection model of passive adaptation to pathogen mutations, i.e., developing assays using corresponding antibodies or nucleic acid probes, is difficult to address frequent outbreaks of emerging infectious diseases. In particular, adaptive mutations observed in coronaviruses, which increase the affinity of the spike protein with the human cellular receptor hACE2, play pivotal roles in the transmission and immune evasion of coronaviruses. Herein, we developed a multifunctional optical fiber evanescent wave biosensor for the universal assay of coronavirus and affinity analysis of the spike protein interacting with hACE2, namely, My-SPACE. By competitively binding with Cy5.5-hACE2 between coronavirus spike proteins in mobile buffer and that modified on optical fibers from the SARS-CoV-2 wild type, My-SPACE could automatically detect SARS-CoV-2 and its variants within 10 min. My-SPACE demonstrated greater sensitivity and faster results than ELISA for SARS-CoV-2 variants, achieving 100% specificity and 94.10% sensitivity in detecting the Omicron variant in 18 clinical samples. Moreover, the interaction between hACE2 and the coronavirus spike protein was accurately characterized across SARS-CoV-2 mutants, SARS-CoV and hCoV-NL63. The accuracy of the affinity determined by My-SPACE was verified by SPR. This approach enables preliminary assessment of the transmissibility and hazards of emerging coronaviruses. The sensor fibers of My-SPACE can be reused more than 40 times, and the device is compact and easy to use; moreover, it is available as a rapid and cost-effective on-site detection tool adapted to coronavirus variability and as an effective assessment platform for early warning of coronavirus transmission risk.

Collaborative Governance Empowered by Digital Technology - Construction of Precision Intelligent Control and Early Warning Platform for Construction Solid Waste

Collaborative Governance Empowered by Digital Technology - Construction of Precision Intelligent Control and Early Warning Platform for Construction Solid Waste

Establishment of a digital remote platform for respiratory support and intelligent early warning in intensive care units

During the disease progression or treatment of critically ill patients with lung injury, the changes in respiratory mechanics are continuous and dynamic. Establishing a digital platform for respiratory support in the ICU, which enables the continuous recording, dynamic analysis, and real-time alerting of numerical and waveform data from mechanical ventilation, can help intensivists improve their understanding of "dynamic respiratory mechanics", improve respiratory therapy and patient outcomes, as well as reduce workload and increase work efficiency. The construction of a dedicated database for mechanical ventilation, based on ventilator waveforms provides essential data support for projects such as respiratory mechanics data algorithm models. This will facilitate the establishment of an auxiliary decision-making system, enable the realization of intelligent mechanical ventilation, and create a new era of dynamic respiratory mechanics.

Research on the method of digital twin operation and maintenance platform for intelligent early warning of wind turbine tower

Wind power generators have a complex structure and operate in harsh environments, where working conditions are highly variable. As a result, the operation and maintenance of wind turbines face numerous challenges. In response to the need for the development of wind power operation and maintenance informatization, it is necessary to satisfy the requirements for multi-party collaborative monitoring to ensure the long-term safe and reliable operation of wind turbines. In this paper,we proposed a method for building an intelligent early-warning digital twin platform focused on the simulation of wind turbines and tower components. The platform construction method proposed in this article is based on the Web and from the perspective of intelligent operation and maintenance of wind turbines. It establishes a warning model for tower agent simulation and vibration signal time series prediction. The tower mechanism model is established based on the operating data set of a 4MW wind turbine at Shanghai Electric. Different physical responses of the tower under different wind speeds are simulated, and an agent model using LSTM and decision tree models is established for predictive analysis. To account for uncertainty, a Bayesian-LSTM model is established to warn against predictive errors. Finally, a data-driven digital twin wind turbine platform is achieved on the Web.

Project of Innovative Open Multi-domain Early Warning Platform Enviwise for Adverse Events in Water Bodies and Streams

This project aims to develop an innovative, open multi-domain early warning platform to monitor adverse events in water bodies and streams, such as salinity increase, acidification, temperature rise, chlorophyll content, etc. The platform involves monitoring dispersed measurement points, acquiring data from measurement stations in the form of autonomous buoys equipped with sensor sets tailored to individual needs. The platform enables remote and distributed monitoring of key locations in lakes and rivers for stakeholders, such as local governments, water managers, and businesses impacting water reservoirs. The open data aggregation system and the universal technological solution in the form of a measurement buoy will allow the acquisition of desired chemical and biological parameters, in line with the Water Framework Directive, using market-available and industry-recognized sensors. The planned buoy construction will accommodate both wired sensors directly connected to the measurement station and data acquisition from locally dispersed measurement points communicating wirelessly with the measurement station-buoy. With built-in machine learning and data analysis mechanisms, the platform will utilize a minimal number of measurement stations to achieve the desired level of data acquisition. The dispersion of sensors and the autonomy of measurement stations will ensure flexibility and scalability of measurements.

Food Security: 3D Dynamic Display and Early Warning Platform Construction and Security Strategy.

Since it affects a nation’s economy and people’s wellbeing, food security is a crucial national security requirement. In order to realize multi-angle grain data presentation and analysis and achieve the goal of deep mining, we propose a 3D dynamic visualization analysis method of multidimensional agricultural spatial–temporal data based on the self-organizing map. This method realizes the multi-angle display and analysis of grain data and achieves the purpose of deep mining. With the outbreak of COVID-19, the global food security situation is not optimistic, so it is necessary to use the food security early warning system to solve the food security issue. Machine learning has emerged widely in recent years and has been applied in various fields. Therefore, it is an excellent way to solve food security to apply the model in machine learning to construct a food security early warning system. Afterward, a food security early warning platform is developed with a support vector regression (SVR) model to ensure food security. Finally, we analyze China’s medium and long-term food security policy in line with modernization objectives. The experimental results show that the food security early warning platform based on the SVR model from 2007 to 2016 is effective compared with the actual situation every year. Through analyses, we should improve the stability, reliability, and sustainability of food supply, firmly hold the food security initiative, and construct a national food security guarantee system matching the goal of modernization.

Development and Application of an Integrated Monitoring and Early Warning Platform for Land Transportation Infrastructure under Special Engineering Conditions

To ensure the safety service performance of land transportation infrastructure under special engineering conditions, centering on the characteristics of structures such as the road high-steep slope, high embankment, special subgrade, railway track, and airport pavement, an integrated monitoring and early warning platform based on health monitoring and safety evaluation was built through the combined design of the safety indicator module, monitoring data module, information processing module, numerical simulation module, inversion prediction analysis module, and safety early warning module. The data interaction between the integrated platform and five monitoring subplatforms was realized by the B/S mode, and the related function interfaces were established. The research results show that as the monitoring data storage analysis and early warning carrier, the integrated monitoring and early warning platform not only presents key data such as comprehensive early warning information, evaluation indicators, and monitoring elements but also realizes the intelligent, automatic, and visual presentation of monitoring data. In addition, relying on the high-steep slope of the Hohhot-Bikeqi Highway (section: K528+215~K528+525), through the application of the high-steep slope monitoring and early warning subplatform system in the practical project, it is verified that the platform has a good monitoring and early warning effect, which provides a reliable platform guarantee for the health monitoring and safety operation of the project.

Multifunctional biosensor constructed by Ag-coating magnetic-assisted unique urchin core porous shell structure for dual SERS enhancement, enrichment, and quantitative detection of multi-components inflammatory markers

The simultaneous, precise, and quantitative detection of multi-components inflammatory markers (IMs) in sepsis serum by surface-enhanced Raman scattering (SERS) remains a challenging problem. A novel, multifunctional biosensor with dual enrichment and enhancement was designed for the ultrasensitive and quantitative analysis of multi-components IMs. The biosensor contains SERS tags-unique urchin core/porous shell (CPS) structure modified with Raman reporters (RaRs), magnetic assist-Ag coated Fe3O4 magnetic nanoparticles (Ag MNPs) modified with internal standard (IS), and then aptamer (Apt) modification to form the sandwich structure (Ag MNPs/IMs/CPS). This multifunctional sensor used for IMS detection has the following innovations: The intensity ratio IRaRs/IIS with Lg CIMs present a good and wide linear relationship to achieve the simultaneous, precise, and quantitative detection of IMS in serum; The detection results display ultrasensitivity, and the limit of detection (LOD) for CRP, IL-6, and PCT is 100 fg/mL, 0.1 fg/mL, and 1.0 fg/mL, which is lower than other detection techniques; The calculated data of clinical blood samples of sepsis by this SERS method is consistent with the hospital results, and can provide more compositional data of IMs. Thus, this combined approach developed a sensing platform for rapid screening, accurate evaluation, early warning, and diagnosis of sepsis.

Design of Early Warning Platform for College Students’ Achievement Based on Data Mining

With the acceleration of the application of information technology in Colleges and universities in China, the efficiency of higher education is constantly improving. The focus of teaching management in Colleges and universities is to continuously improve the teaching level of colleges and universities, and the key is to strengthen the management of students’ performance. Performance warning is a form of student performance management. In recent years, data mining technology is more and more mature, and its application is also very wide. Many students have applied data mining technology to university management. In this paper, we apply data mining technology to college students’ performance early warning, and use Apriori algorithm in association rules to design and build college students’ performance early warning platform, and select two classes of students as the research object to verify. In this study, we choose the English course scores of two classes as the test data, and define the performance warning, which is based on the score below 60. The results show that six students in class a will be subject to performance warning, while seven students in class B will be subject to performance warning. In addition, the performance early warning platform designed by this method, the early warning accuracy rate is as high as 92.85%, the accuracy rate is high, has certain application advantages.

Design, Implementation and Testing of a Network-Based Earthquake Early Warning System in Greece

In this study we implemented and tested the Earthquake Early Warning system PRESTo (PRobabilistic and Evolutionary early warning System, Satriano et al., 2011) on the Greek Ionian islands of Lefkada, Zakynthos and Kefalonia. PRESTo is a free and open source platform for regional Earthquake Early Warning developed at the University of Naples Federico II, which is currently under experimentation in Southern Italy, in the area covered by the Irpinia Seismic Network. The three Ionian islands selected for this study are located on the North-Western part of the Hellenic trench. Here the seismicity rate and the seismic hazard, coupled with the vulnerability of existing critical infrastructures, make this region among the highest seismic risk areas in Europe, where the application of Earthquake Early Warning systems may become a useful strategy to mitigate the potential damage caused by earthquakes. Here we studied the feasibility of implementing an Earthquake Early Warning system on an existing seismic network, which was not specifically made for earthquake early warning purposes, and evaluated the performance of the system, using a data set of real-earthquake recordings. We first describe the technical details of the implementation of PRESTo in the area of interest, including the preliminary parameter configuration and the empirical scaling relationship calibration. Then we evaluated the performance of the system through the off-line analysis of a database of real earthquake records belonging to the most recent M > 4.0 earthquakes occurred in the area. We evaluated the performance in terms of source parameter estimation (location, magnitude), accuracy of ground shaking prediction and lead-time analysis. Finally, we show the preliminary results of the real-time application of PRESTo, performed during the period 01–31 July 2019.

Research and Development of Insect Situation Data Intelligent Cloud Platform Using Geographic Information System

With the changes in climate, pest varieties and resistance to pesticides, pests have shown frequent and recurring characteristics, making the supply of major agricultural products and food security facing severe challenges. The key to preventing and controlling pests is to correctly identify the pests and then apply the correct drugs and doses. This paper uses cloud computing, artificial intelligence and big data technology to build a cloud platform for identification and early warning of pest situation. Users use smartphones as terminals to accurately identify crop pests and form a complete crop pest distribution database, which is conducive to grasping the rules of pest occurrence. It is conducive to targeted pest control and is conducive to improving the efficiency of pesticide application control, thereby increasing crop yields.

Application of Early Warning Nursing System During COVID-19 Epidemic in Children’s Hospital

Aim: To develop a nursing early warning system in children’s hospital during the outbreak of the novel coronavirus pneumonia, and to accomplish the construction and application of this system, so as to provide decision-support of the prevention and control for COVID-19 in children’s medical institutions. Method: Children’s hospital nursing early warning system was divided into three modules: hospital nursing early warning platform includes internal and external early warning platform, nursing staff early warning program includes protection, human resources early warning plan and patient early warning program includes outpatient, emergency and ward early warning plan. The data of epidemic training, assessment, prevention and control screening from January to June 2020 were collected from the nursing early warning system to evaluate the application effect of the system. Results: A total of 18 procedures and specifications were formulated, nine hospital-level trainings and about 1000 department-level trainings were organized, two hospital-level assessments (pass rate 95.6% and 98.2%), and 78 nurses were reserved, and 10 popular science articles, five popular science videos were published during the application of the nursing early warning system. A total of 583,435 children and 139,308 caregivers were screened in outpatient, emergency and wards during pre-checks, 2385 suspected cases of novel coronavirus pneumonia were confirmed (0.41%) after the screening and 1 case (0.0002%) was finally confirmed. Conclusion: The nursing early warning system of children’s hospital can prevent and control the novel coronavirus pneumonia epidemic from each module, ensure early warning and triage of suspected infected patients, reduce the risk of cross-infection in hospital and improve the safety of the children’s hospital medical environment.

Engineering Safety Early Warning Platform Based on BIM and Internet of Things

In view of the rapid development of China’s economy and the continuous expansion of construction scale, the casualties and economic losses caused by engineering accidents are becoming more and more serious. This paper studies and puts forward a project early warning platform based on BIM Technology and Internet of things technology. Through the feasibility study of BIM Technology and Internet of things technology applied in engineering projects, and various risk assessment in engineering construction, it provides a reliable scheme. Combined with the simulation, through the modeling in revlt software and the data processing and Analysis on the platform, the early warning processing is carried out It shows the practicability and reliability of this technology in engineering safety early warning.

On the feasibility of IoT-based smart meters for earthquake early warning

The microelectromechanical systems (MEMS) accelerometer built into a smart meter (SM) has a nominal digital resolution of 16 bits. However, this resolution collapses to 7 bits of information per sample when used in an urban environment. This collapse in resolution limits the sensitivity required to effectively operate the earthquake early warning platform (EEWP). In this study, we evaluate the performance of the MEMS sensor in present SMs with respect to a reference sensor, with a special focus on its poor noise power spectral density (PSD, [Formula: see text]). We also explore the general capacity of the SM in an IoT-based EEWP and provide explicit information regarding the 16-bit digital MEMS accelerometer. Then, we investigate the functionality of the sensor in the context of event detection in the presence of background vibration. When the value of acceleration root mean square (RMS) exceeds 20 mg, the meter’s error decreases to <20%, whereas the peak ground acceleration error decreases to <20% for the peak value greater than ~70 mg. The MEMS sensor is unreliable for motions with a peak acceleration of less than 148 mg or those with an RMS value less than 46 mg. However, we note that SMs exhibit reasonable amplitude and phase coherence for frequencies above 1 Hz with respect to the reference accelerometer. To enhance the sensitivity, averaging 1000 coherent accelerometer observations enhances the digital resolution to 14 bits, which allows the efficient usage of the network bandwidth. Since the accelerometer is used as an anti-tampering mechanism, the SM is similar to a tiltmeter. Therefore, it is necessary to reconfigure SMs for early warning systems. Despite the challenges, the use of SM for an IoT-based EEWP is technically feasible.

Rational design of smart adsorbent equipped with a sensitive indicator via ligand exchange: A hierarchical porous mixed-ligand MOF for simultaneous removal and detection of Hg2+

The increasing accumulation of toxic mercury species in water environment has posed a serious health threat worldwide, making it inevitable to develop the versatile materials to achieve efficient prevention and remediation of mercury pollution. Guided by the solvent-assisted ligand exchange (SALE) approach, this work rationally constructed a mixed-ligand NH2-UiO-66-SH (NSU66) with hierarchical-pore structure by incorporating the thiol-rich ligands (H2DMBD) into the water-stable NH2-UiO-66 (NU66) precursor to act as a smart adsorbent equipped with sensitive detector for simultaneous sensing and removal of Hg2+. Unlike the traditional adsorbents, the as-prepared NSU66 not only exhibits a remarkable removal ability with fast capture rate (within 60.0 min), large uptake capacity (265.29 mg/g), and qualified selectivity, but also possesses satisfactory sensing capability, accompanied by low detection limit (3.50 × 10−2 µM), wide linear range (1.00–99.7 µM), high specificity, and strong anti-interference capability. The detection function plays a vital role in indicating the removal behavior and the pre-enrichment effect of adsorption process correspondingly improves the sensitivity of indicator. Notably, the sensing and trapping capabilities of NSU66 are significantly improved compared to the NU66, which stems from the delicate design of the mixed-ligand and hierarchical-pore structure. Furthermore, proven excellent stability and recyclability emphasize the feasibility of NSU66 in practical applications. These results suggest that the smart NSU66 adsorbent can serve as a favorable platform for early warning and guided removal of toxic Hg2+ in water.

Establishment of multi-point trigger and multi-channel surveillance mechanism for intelligent early warning of infectious diseases in China

This paper reviews the limitations of current infectious disease surveillance and early warning system in China, analyzes the concepts and countermeasures of the establishment of an intelligent early warning platform of infectious diseases based on multi-point trigger mechanism and multi-channel surveillance mechanism and proposes the realization routes for the purpose of facilitating capacity building and improvement of surveillance and early warning of infectious diseases in China.

Continuous Bayesian Network in Intelligent Analysis and Early Warning Platform of Silk Making Process

Continuous Bayesian Network in Intelligent Analysis and Early Warning Platform of Silk Making Process

Financial Big Data Analysis and Early Warning Platform: A Case Study

In order to keep the bottom line of systemic financial risks and prevent the mitigation of major risks, this work focuses on the investigation of multi-source heterogeneous data fusion algorithms and cleaning technologies to establish a suitable style for data analysis and big data computation frame. In this paper, according to the above method, we provide the basis for early analysis of economic security. Utilizing the big data analysis, an emerging information technology method, we can be able to explore new risk early-warning methods, build a risk monitoring and early-warning platform and achieve scientific economic decision-making, so that the sources of economic risk in national economic security can be traced.

Automatic Monitoring System in Underground Engineering Construction: Review and Prospect

Automatic monitoring system is one of the main means to ensure the safety of underground engineering construction. This paper summarizes the current international research and application status of the underground engineering monitoring system from three aspects of data acquisition, data transmission, and data processing and emphatically introduces the mainstream new technology of the monitoring system. Furthermore, this paper puts forward specific and implementable technical routes based on the current intelligent technology and the challenges faced by future monitoring, which can provide direction and reference for future research, including high‐precision real‐time acquisition and safe and reliable transmission of monitoring data, multisource data fusion, and the visual intelligent early warning platform.

Design of a Weixin Service Platform for Bird Damage Early Warning in Power Grid

With the improvement of ecological environment and the expansion of the scale of power grid construction, “bird damage” has brought great hidden dangers to the safe and stable operation of power grid. On the one hand, human awareness of natural protection has been enhanced, the speed of bird reproduction has been accelerated, and the threat of birds to the line has gradually increased. On the other hand, bird damage prevention measures were not considered in the early design of power grid. Bird damage was prevalent in operation. After bird damage, the response measures were not timely, the treatment efficiency was low, and the number of line trips and non-outages increased. Especially in some backward areas, once the power grid loses contact with the provincial network, it will cause large-scale blackouts in the county (township) often occur, which directly affects the safe, stable and reliable operation of the power grid. It is urgent to prevent bird damage on the line.