Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limite...The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limited,imbalanced datasets and oversampling.In this study,the dataset was expanded to approximately 500 samples for each type,including 508 sedimentary,573 orogenic gold,548 sedimentary exhalative(SEDEX)deposits,and 364 volcanogenic massive sulfides(VMS)pyrites,utilizing random forest(RF)and support vector machine(SVM)methodologies to enhance the reliability of the classifier models.The RF classifier achieved an overall accuracy of 99.8%,and the SVM classifier attained an overall accuracy of 100%.The model was evaluated by a five-fold cross-validation approach with 93.8%accuracy for the RF and 94.9%for the SVM classifier.These results demonstrate the strong feasibility of pyrite classification,supported by a relatively large,balanced dataset and high accuracy rates.The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China,which has been inconclusive among SEDEX,VMS,or a SEDEX-VMS transition.Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite(Py1)and late recrystallized pyrite(Py2).The majority voting classified Py1 as the VMS type,with an accuracy of RF and SVM being 72.2%and 75%,respectively,and confirmed Py2 as an orogenic type with 74.3% and 77.1%accuracy,respectively.The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system,followed by late orogenic-type overprinting of metamorphism and deformation,which is consistent with the geological and geochemical observations.This study further emphasizes the advantages of Machine learning(ML)methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.展开更多
BACKGROUND Emphysematous pyelonephritis(EPN)is a life-threatening necrotizing renal parenchyma infection characterized by gas formation due to severe bacterial infection,predominantly affecting diabetic and immunocomp...BACKGROUND Emphysematous pyelonephritis(EPN)is a life-threatening necrotizing renal parenchyma infection characterized by gas formation due to severe bacterial infection,predominantly affecting diabetic and immunocompromised patients.It carries high morbidity and mortality,requiring early diagnosis and timely intervention.Various prognostic scoring systems help in triaging critically ill patients.The National Early Warning Score 2(NEWS 2)scoring system is a widely used physiological assessment tool that evaluates clinical deterioration based on vital parameters,but its standard form lacks specificity for risk stratification in EPN,necessitating modifications to improve treatment decisionmaking and prognostic accuracy in this critical condition.AIM To highlight the need to modify the NEWS 2 score to enable more intense monitoring and better treatment outcomes.METHODS This prospective study was done on all EPN patients admitted to our hospital over the past 12 years.A weighted average risk-stratification index was calculated for each of the three groups,mortality risk was calculated for each of the NEWS 2 scores,and the need for intervention for each of the three groups was calculated.The NEWS 2 score was subsequently modified with 0-6,7-14 and 15-20 scores included in groups 1,2 and 3,respectively.RESULTS A total of 171 patients with EPN were included in the study,with a predominant association with diabetes(90.6%)and a female-to-male ratio of 1.5:1.The combined prognostic scoring of the three groups was 10.7,13.0,and 21.9,respectively(P<0.01).All patients managed conservatively belonged to group 1(P<0.01).Eight patients underwent early nephrectomy,with six from group 3(P<0.01).Overall mortality was 8(4.7%),with seven from group 3(87.5%).The cutoff NEWS 2 score for mortality was identified to be 15,with a sensitivity of 87.5%,specificity of 96.9%,and an overall accuracy rate of 96.5%.The area under the curve to predict mortality based on the NEWS 2 score was 0.98,with a confidence interval of(0.97,1.0)and P<0.001.CONCLUSION Modified NEWS 2(mNEWS 2)score dramatically aids in the appropriate assessment of treatment-related outcomes.MNEWS 2 scores should become the practice standard to reduce the morbidity and mortality associated with this dreaded illness.展开更多
The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A da...The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A data set was established by collecting academic warning data of students in a certain university.The importance of the school,major,grade,and warning level for the students was analyzed using the Pearson correlation coefficient,random forest variable importance,and permutation importance.It was found that the characteristic of the major has a great impact on the academic warning level.Countermeasures such as dynamic adjustment of majors,reform of cognitive adaptation of courses,full-cycle academic support,and data-driven precise intervention were proposed to provide theoretical support and practical paths for universities to improve the efficiency of academic warning and enhance students’learning ability.展开更多
Due to batteries inconsistencies and potential faults in battery management systems,slight overcharging remains a common yet insufficiently understood safety risk,lacking effective warning methods.To illuminate the de...Due to batteries inconsistencies and potential faults in battery management systems,slight overcharging remains a common yet insufficiently understood safety risk,lacking effective warning methods.To illuminate the degradation behavior and failure mechanism of various overcharged states(100%SOC,105%SOC,110%SOC,and 115%SOC),multiple advanced in-situ characterization techniques(accelerating rate calorimeter,electrochemical impedance spectroscopy,ultrasonic scanning,and expansion instrument)were utilized.Additionally,re-overcharge-induced thermal runaway(TR)tests were conducted,with a specific emphasis on the evolution of the expansion force signal.Results indicated significant degradation at 110%SOC including conductivity loss,loss of lithium inventory,and loss of active material accompanied by internal gas generation.These failure behaviors slow down the expansion force rate during reovercharging,reducing the efficacy of active warnings that depend on rate thresholds of expansion force.Specifically,the warning time for 115%SOC battery is only 144 s,which is 740 s shorter than that for fresh battery,and the time to TR is advanced by 9 min.Moreover,the initial self-heating temperature(T1)is reduced by 62.4℃compared to that of fresh battery,reaching only 70.8℃.To address the low safety of overcharged batteries,a passive overcharge warning method utilizing relaxation expansion force was proposed,based on the continued gas generation after stopping charging,leading to a sustained increase in force.Compared to active methods that rely on thresholds of expansion force rate,the passive method can issue warnings 115 s earlier.By combining the passive and active warning methods,guaranteed effective overcharge warning can be issued 863-884 s before TR.This study introduces a novel perspective for enhancing the safety of batteries.展开更多
Coating microdefects and localized corrosion in coating/metal system are inevitable,accelerating the degradation of metal infrastructure.Early evaluating coating microdefects and detecting corrosion sites are urgent y...Coating microdefects and localized corrosion in coating/metal system are inevitable,accelerating the degradation of metal infrastructure.Early evaluating coating microdefects and detecting corrosion sites are urgent yet remain challenge to achieve.Herein,we propose a robust,universal and efficient fluorescence-based strategy for hierarchical warning of coating damage and metal corrosion by introducing the concepts of damage-induced fluorescence enhancement effect(DIE)and ionic-recognition induced quenching effect(RIQ).The coatings with dualresponsiveness for coating defect and steel corrosion are constructed by incorporating synthesized nanoprobes composed of metal organic frameworks(Ni–Zn-MOFs)loaded with Rhodamine B(RhB@MOFs).The initial damage to the coating causes an immediate intensification of fluorescence,while the specific ionic-recognition characteristic of RhB with Fe3t results in an evident fluorescence quenching,enabling the detection of coating damage and corrosion.Importantly,this nanoprobes are insensitive to the coating matrix and exhibit stable corrosion warning capability across various coating systems.Meanwhile,electrochemical investigations indicate that the impedance values of RM/EP maintain above 10^(8)Ωcm^(2)even after 60 days of immersion.Therefore,the incorporation of fluorescent nanoprobes greatly inhibits the intrusion of electrolytes into polymer and improves the corrosion protection performance of the coating.This powerful strategy towards dual-level damage warning provides insights for the development of long-term smart protective materials.展开更多
The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the exis...The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.展开更多
Emphysematous pyelonephritis(EPN)is a severe,a lethal necrotizing upper urinary tract infection,characterized by gas production within the renal pa-renchyma,collecting system,or perinephric tissue.EPN is emerging as a...Emphysematous pyelonephritis(EPN)is a severe,a lethal necrotizing upper urinary tract infection,characterized by gas production within the renal pa-renchyma,collecting system,or perinephric tissue.EPN is emerging as a sig-nificant concern,necessitating early diagnosis,severity assessment,and timely intervention to improve outcomes.This study proposes a modified National Early Warning Score 2(mNEWS 2)to enhance risk stratification and predictive accuracy in EPN management.The mNEWS 2 refines the original NEWS 2 system,which aggregates 6 physiological indicators(body temperature,systolic blood pressure,pulse rate,oxygen saturation,breathing rate,and degree of consciousness),by incorporating weighted risk stratification indices and specific cutoff values derived from clinical observations,statistical modeling,and predictive per-formance analysis.A pilot study identified optimal thresholds,with a score of 15 maximizing predictive performance for mortality risk and intervention needs,validated through receiver operating characteristic curve analysis.So,the mNEWS 2 score represents a significant advancement in EPN management,offering improved risk stratification and treatment outcomes.展开更多
Purpose–This study aims to design and validate an emergency response method for high-speed railway earthquake early warning(EEW)systems based on the Propagation of Local Undamped Motion(PLUM)principle in order to enh...Purpose–This study aims to design and validate an emergency response method for high-speed railway earthquake early warning(EEW)systems based on the Propagation of Local Undamped Motion(PLUM)principle in order to enhance the timeliness and accuracy of warnings under seismic threats.Design/methodology/approach–A hierarchical architecture of the railway EEW system was adopted,in which self-built stations along the railway serve as the backbone and the national seismic network provides supplementary data.Warning zones were designed along the railway using overlapping trapezoidal layouts to cover seismic stations and reduce inter-regional time delays.Offline replay experiments were conducted using 82 historical earthquake events and records from 61 seismic stations to evaluate the timeliness and accuracy of warning information.Findings–The results indicate that the PLUM-based early warning method can issue emergency response information before destructive seismic waves arrive.Multiple earthquake experiments demonstrated high reliability and stability,with effective detection across different magnitudes and epicentral distances.Furthermore,the trapezoidal overlapping zone design improved regional consistency and significantly reduced missed alerts.Originality/value–This work represents the first systematic application of the PLUM method to high-speed railway EEW in China.By integrating railway operational requirements,the proposed method provides a practical and robust emergency response strategy,offering new insights into seismic risk mitigation for China’s high-speed railways.展开更多
In order to solve the problems of high coupling and poor scalability of the traditional monomer early warning release system architecture,multi-level deployment in a complex network environment will lead to high inves...In order to solve the problems of high coupling and poor scalability of the traditional monomer early warning release system architecture,multi-level deployment in a complex network environment will lead to high investment in software and hardware and cannot achieve intensive multi-level deployment.This paper realizes the goal of system scalability by introducing micro service architecture and technology stack and realizes the goal of resource intensification by introducing the idea of a data forwarding agent.The designed architecture scheme has been practically applied in the“Jiangxi emergency early warning information release system software platform(phase I)project”(hereinafter referred to as“provincial emergency”),which meets the needs of flexible deployment of multi-level applications across meteorological wide area network(WAN),business private network of other commissions,offices,and bureaus,government extranet,Internet and other complex networks,and fully verifies the scientificity and rationality of the scheme.It has achieved the goal of intensive and scalable construction of provincial emergencies under the complex network environment,greatly improved the early warning capacity and communication capacity of emergencies and comprehensive disasters,provided a reliable guarantee for disaster prevention and reduction,and provided a reference for the construction of current and future early warning release system and capacity improvement project.展开更多
Although previous researchers have attempted to decipher ore genesis and mineralization in the Erdaokan Ag-Pb-Zn deposit,some uncertainties regarding the mineralization process and evolution of both ore-forming fluids...Although previous researchers have attempted to decipher ore genesis and mineralization in the Erdaokan Ag-Pb-Zn deposit,some uncertainties regarding the mineralization process and evolution of both ore-forming fluids and magnetite types still need to be addressed.In this study,we obtained new EPMA,LA-ICP-MS,and in situ Fe isotope data from magnetite from the Erdaokan deposit,in order to better understand the mineralization mechanism and evolution of both magnetite and the ore-forming fluids.Our results identified seven types of magnetite at Erdaokan:disseminated magnetite(Mag1),coarse-grained magnetite(Mag2a),radial magnetite(Mag2b),fragmented fine-grained magnetite(Mag2c),vermicular gel magnetite(Mag3a1 and Mag3a2),colloidal magnetite(Mag3b)and dark gray magnetite(Mag4).All of the magnetite types were hydrothermal in origin and generally low in Ti(<400 ppm)and Ni(<800 ppm),while being enriched in light Fe isotopes(δ^(56)Fe ranging from−1.54‰to−0.06‰).However,they exhibit different geochemical signatures and are thus classified into high-manganese magnetite(Mag1,MnO>5 wt%),low-silicon magnetite(Mag2a-c,SiO_(2)<1 wt%),high-silicon magnetite(Mag3a-b,SiO_(2)from 1 to 7 wt%)and high-silicon-manganese magnetite(Mag4,SiO_(2)>1 wt%,MnO>0.2 wt%),each being formed within distinct hydrothermal environments.Based on mineralogy,elemental geochemistry,Fe isotopes,temperature trends,TMg-mag and(Ti+V)vs.(Al+Mn)diagrams,we propose that the Erdaokan Ag-Pb-Zn deposit underwent multi-stage mineralization,which can be broken down into four stages and nine sub-stages.Mag1,Mag2a-c,Mag3a-b and Mag4 were formed during the first sub-stage of each of the four stages,respectively.Additionally,fluid mixing,cooling and depressurization boiling were identified as the main mechanisms for mineral precipitation.The enrichment of Ag was significantly enhanced by the superposition of multi-stage ore-forming hydrothermal fluids in the Erdaokan Ag-Pb-Zn deposit.展开更多
An effective warning system for flash floods along the upper River des Peres, a small urban stream in eastern Missouri, USA, is based on three enterprise-level, automated rain gauges.Because floods in this 25 km~2 bas...An effective warning system for flash floods along the upper River des Peres, a small urban stream in eastern Missouri, USA, is based on three enterprise-level, automated rain gauges.Because floods in this 25 km~2 basin develop rapidly and are commonly caused by small but intense thunderstorm cells, these rain gauges were necessarily deployed within the watershed, and immediate telemetry and processing of rainfall delivered in 5-minute intervals is required. Available data show that damaging floods in this area occur only 30 min to 3 h following the delivery of 38 mm of rainfall or more in a single hour. Water levels along this stream can rise more than 3 m/h. Since full deployment in Nov. 2021, our system has successfully predicted 3 significant floods with one false positive.展开更多
Seismic data denoising is a critical process usually applied at various stages of the seismic processing workflow,as our ability to mitigate noise in seismic data affects the quality of our subsequent analyses.However...Seismic data denoising is a critical process usually applied at various stages of the seismic processing workflow,as our ability to mitigate noise in seismic data affects the quality of our subsequent analyses.However,finding an optimal balance between preserving seismic signals and effectively reducing seismic noise presents a substantial challenge.In this study,we introduce a multi-stage deep learning model,trained in a self-supervised manner,designed specifically to suppress seismic noise while minimizing signal leakage.This model operates as a patch-based approach,extracting overlapping patches from the noisy data and converting them into 1D vectors for input.It consists of two identical sub-networks,each configured differently.Inspired by the transformer architecture,each sub-network features an embedded block that comprises two fully connected layers,which are utilized for feature extraction from the input patches.After reshaping,a multi-head attention module enhances the model’s focus on significant features by assigning higher attention weights to them.The key difference between the two sub-networks lies in the number of neurons within their fully connected layers.The first sub-network serves as a strong denoiser with a small number of neurons,effectively attenuating seismic noise;in contrast,the second sub-network functions as a signal-add-back model,using a larger number of neurons to retrieve some of the signal that was not preserved in the output of the first sub-network.The proposed model produces two outputs,each corresponding to one of the sub-networks,and both sub-networks are optimized simultaneously using the noisy data as the label for both outputs.Evaluations conducted on both synthetic and field data demonstrate the model’s effectiveness in suppressing seismic noise with minimal signal leakage,outperforming some benchmark methods.展开更多
The earthquake early warning system is an effective means of disaster reduction to reduce losses caused by earthquakes,it can release earthquake warning information to the public before destructive seismic waves reach...The earthquake early warning system is an effective means of disaster reduction to reduce losses caused by earthquakes,it can release earthquake warning information to the public before destructive seismic waves reach the warning target area,and carry out automatic disposal of lifeline engineering facilities.Through the construction of the National Earthquake Intensity Rapid Reporting and Early Warning Project,an earthquake early warning network consisting of over 1900 monitoring stations has been established in the Beijing-Tianjin-Hebei Urban Agglomeration.The early warning system has achieved second level earthquake warning and minute level intensity rapid reporting.The implementation of these functions relies on the system's ability to timely,accurately,and reliably identify seismic waves.But with the development of social economy,the background noise of earthquake observation environment is becoming increasingly complex,which brings certain challenges to earthquake wave recognition,some interference events have the risk of triggering the earthquake warning system incorrectly.Therefore,this article focuses on seismic wave recognition in complex noise environments and proposes a seismic wave detection method based on triangulation to enhance the antiinterference ability and recognition accuracy of early warning systems.展开更多
The collapse of rock masses in fault-developed zones poses significant safety challenges during the excavation of high-stress underground caverns. This study investigates the spatiotemporal evolution of the collapse m...The collapse of rock masses in fault-developed zones poses significant safety challenges during the excavation of high-stress underground caverns. This study investigates the spatiotemporal evolution of the collapse mechanisms of the cavern in the Yebatan Hydropower Station through using microseismic (MS) monitoring and displacement measurements. We developed a multi-parameter deformation early warning model that integrates three critical indicators: deformation rate, rate increment, and tangential angle of the deformation time curve. The results of the early warning model show a significant and abrupt increase in the deformation of the rock mass during the collapse process. The safety and stability of the local cavern in the face of excavation-induced disturbances are meticulously assessed utilizing MS data. Spatiotemporal analysis of the MS monitoring indicates a high frequency of MS events during the blasting phase, with a notable clustering of these events in the vicinity of the fault. These research results provide a valuable reference for risk warnings and stability assessments in the fault development zones of analogous caverns.展开更多
This study employs deformation monitoring data acquired during the construction of the Haoji railway large-scale bridge to investigate the displacement behavior of the subgrades,catenary columns,and tracks.Emphasis is...This study employs deformation monitoring data acquired during the construction of the Haoji railway large-scale bridge to investigate the displacement behavior of the subgrades,catenary columns,and tracks.Emphasis is placed on data acquisition and processing methods using total stations and automated monitoring systems.Through a comprehensive analysis of lateral,longitudinal,and vertical displacement data from 26 subgrade monitoring points,catenary columns,and track sections,this research evaluates how construction activities influence railway structures.The results show that displacement variations in the subgrades,catenary columns,and tracks remained within the established alert thresholds,exhibiting stable deformation trends and indicating that any adverse environmental impact was effectively contained.Furthermore,this paper proposes an early warning mechanism based on an automated monitoring system,which can promptly detect abnormal deformations and initiate emergency response procedures,thereby ensuring the safe operation of the railway.The integration of big data analysis and deformation prediction models offers a practical foundation for future safety management in railway construction.展开更多
This paper presents a new criterion for determining the unloading points quantitatively and consistently in a multi-stage triaxial test.The radial strain gradient(RSG)is first introduced as an arc tangent function of ...This paper presents a new criterion for determining the unloading points quantitatively and consistently in a multi-stage triaxial test.The radial strain gradient(RSG)is first introduced as an arc tangent function of the rate of change of radial strain to time.RSG is observed to correlate closely with the stress state of a compressed sample,and reaches a horizontal asymptote as approaching failure.For a given rock type,RSG value at peak stress is almost the same,irrespective of the porosity and permeability.These findings lead to the development of RSG criterion:Unloading points can be precisely determined at the time when RSG reaches a pre-determined value that is a little smaller than or equal to the RSG at peak stress.The RSG criterion is validated against other criteria and the single-stage triaxial test on various types of rocks.Failure envelopes from the RSG criterion match well with those from single-stage tests.A practical procedure is recommended to use the RSG criterion:an unconfined compression or single-stage test is first conducted to determine the RSG at peak stress for one sample,the unloading point is then selected to be a value close to the RSG at peak stress,and the multi-stage test is finally performed on another sample using the pre-selected RSG unloading criterion.Generally,the RSG criterion is applicable for any type of rocks,especially brittle rocks,where other criteria are not suitable.Further,it can be practically implemented on the most available rock mechanical testing instruments.展开更多
A new hang-off system has been proposed to improve the security of risers in hang-off modes during typhoons.However,efficient anti-typhoon evacuation strategies have not been investigated.Optimiza-tion model and metho...A new hang-off system has been proposed to improve the security of risers in hang-off modes during typhoons.However,efficient anti-typhoon evacuation strategies have not been investigated.Optimiza-tion model and method for the anti-typhoon evacuation strategies should be researched.Therefore,multi-objective functions are proposed based on operation time,evacuation speed stability,and steering stability.An evacuation path model and a dynamic model of risers with the new hang-off system are developed for design variables and constraints.A multi-objective optimization model with high-dimensional variables and complex constraints is established.Finally,a three-stage optimization method based on genetic algorithm,least square method,and the penalty function method is proposed to solve the multi-objective optimization model.Optimization results show that the operation time can be reduced through operation parameter optimization,especially evacuation heading optimization.The optimal anti-typhoon strategy is evacuation with all risers suspended along a variable path when the direction angle is large,while evacuation with all risers suspended along a straight path at another di-rection angle.Besides,the influencing factors on anti-typhoon evacuation strategies indicate that the proposed optimization model and method have strong applicability to working conditions and remarkable optimization effects.展开更多
Leveraging the achievements of the smart meteorological system nationwide,a meteorological monitoring and early warning system for alfalfa pests and diseases can be formed through the establishment of four systems,nam...Leveraging the achievements of the smart meteorological system nationwide,a meteorological monitoring and early warning system for alfalfa pests and diseases can be formed through the establishment of four systems,namely,"real-time monitoring system,forecasting and prediction system,monitoring and early warning system,and smart service system".It will enable intelligent,dynamic meteorological monitoring,early warning,and forecasting services for the occurrence and development of alfalfa pests and diseases,providing technical support for scientifically controlling their harm and improving yield and quality.展开更多
BACKGROUND Enhancing postoperative recovery is a critical goal in clinical practice and the application of innovative nursing models can significantly contribute to this objective.AIM To investigate the effects of mot...BACKGROUND Enhancing postoperative recovery is a critical goal in clinical practice and the application of innovative nursing models can significantly contribute to this objective.AIM To investigate the effects of motivational and early warning nursing interventions on wound healing and sociopsychological adaptability in patients undergoing hepatobiliary surgery.METHODS A total of 160 patients who underwent surgical treatment in the hepatobiliary department of our hospital from January 2022 to June 2024 were selected and randomly divided into a control group and an observation group,with 80 patients in each group.The control group received routine nursing care,while the observation group received a combination of motivational and early warning nursing interventions.The wound healing status(class A,B,and C wound healing and healing time),social psychological adaptability,complications,postoperative recovery,and quality of life were compared between the two groups.RESULTS The wound healing rate in the observation group was higher than that in the control group,while the wound healing time was shorter(P<0.05).The social adaptability scores in the observation group were higher than those in the control group(P<0.05).The incidence of complications was lower in the observation group than in the control group(P<0.05).Postoperative recovery and quality of life were better in the observation group than in the control group(P<0.05).CONCLUSION Motivational and early warning nursing interventions are beneficial for promoting wound healing in patients undergoing hepatobiliary surgery,reducing the incidence of complications and improving socio-psychological adaptability and postoperative quality of life.These interventions should be promoted in clinical nursing practice.展开更多
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
基金the National Key Research and Development Program of China(2021YFC2900300)the Natural Science Foundation of Guangdong Province(2024A1515030216)+2 种基金MOST Special Fund from State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences(GPMR202437)the Guangdong Province Introduced of Innovative R&D Team(2021ZT09H399)the Third Xinjiang Scientific Expedition Program(2022xjkk1301).
文摘The application of machine learning for pyrite discrimination establishes a robust foundation for constructing the ore-forming history of multi-stage deposits;however,published models face challenges related to limited,imbalanced datasets and oversampling.In this study,the dataset was expanded to approximately 500 samples for each type,including 508 sedimentary,573 orogenic gold,548 sedimentary exhalative(SEDEX)deposits,and 364 volcanogenic massive sulfides(VMS)pyrites,utilizing random forest(RF)and support vector machine(SVM)methodologies to enhance the reliability of the classifier models.The RF classifier achieved an overall accuracy of 99.8%,and the SVM classifier attained an overall accuracy of 100%.The model was evaluated by a five-fold cross-validation approach with 93.8%accuracy for the RF and 94.9%for the SVM classifier.These results demonstrate the strong feasibility of pyrite classification,supported by a relatively large,balanced dataset and high accuracy rates.The classifier was employed to reveal the genesis of the controversial Keketale Pb-Zn deposit in NW China,which has been inconclusive among SEDEX,VMS,or a SEDEX-VMS transition.Petrographic investigations indicated that the deposit comprises early fine-grained layered pyrite(Py1)and late recrystallized pyrite(Py2).The majority voting classified Py1 as the VMS type,with an accuracy of RF and SVM being 72.2%and 75%,respectively,and confirmed Py2 as an orogenic type with 74.3% and 77.1%accuracy,respectively.The new findings indicated that the Keketale deposit originated from a submarine VMS mineralization system,followed by late orogenic-type overprinting of metamorphism and deformation,which is consistent with the geological and geochemical observations.This study further emphasizes the advantages of Machine learning(ML)methods in accurately and directly discriminating the deposit types and reconstructing the formation history of multi-stage deposits.
文摘BACKGROUND Emphysematous pyelonephritis(EPN)is a life-threatening necrotizing renal parenchyma infection characterized by gas formation due to severe bacterial infection,predominantly affecting diabetic and immunocompromised patients.It carries high morbidity and mortality,requiring early diagnosis and timely intervention.Various prognostic scoring systems help in triaging critically ill patients.The National Early Warning Score 2(NEWS 2)scoring system is a widely used physiological assessment tool that evaluates clinical deterioration based on vital parameters,but its standard form lacks specificity for risk stratification in EPN,necessitating modifications to improve treatment decisionmaking and prognostic accuracy in this critical condition.AIM To highlight the need to modify the NEWS 2 score to enable more intense monitoring and better treatment outcomes.METHODS This prospective study was done on all EPN patients admitted to our hospital over the past 12 years.A weighted average risk-stratification index was calculated for each of the three groups,mortality risk was calculated for each of the NEWS 2 scores,and the need for intervention for each of the three groups was calculated.The NEWS 2 score was subsequently modified with 0-6,7-14 and 15-20 scores included in groups 1,2 and 3,respectively.RESULTS A total of 171 patients with EPN were included in the study,with a predominant association with diabetes(90.6%)and a female-to-male ratio of 1.5:1.The combined prognostic scoring of the three groups was 10.7,13.0,and 21.9,respectively(P<0.01).All patients managed conservatively belonged to group 1(P<0.01).Eight patients underwent early nephrectomy,with six from group 3(P<0.01).Overall mortality was 8(4.7%),with seven from group 3(87.5%).The cutoff NEWS 2 score for mortality was identified to be 15,with a sensitivity of 87.5%,specificity of 96.9%,and an overall accuracy rate of 96.5%.The area under the curve to predict mortality based on the NEWS 2 score was 0.98,with a confidence interval of(0.97,1.0)and P<0.001.CONCLUSION Modified NEWS 2(mNEWS 2)score dramatically aids in the appropriate assessment of treatment-related outcomes.MNEWS 2 scores should become the practice standard to reduce the morbidity and mortality associated with this dreaded illness.
基金supported by the Basic Ability Improvement Project of Young and Middle-Aged Teachers in Colleges and Universities of Guangxi(2022KY1922,2021KY1938).
文摘The traditional academic warning methods for students in higher vocational colleges are relatively backward,single,and have many influencing factors,which have a limited effect on improving their learning ability.A data set was established by collecting academic warning data of students in a certain university.The importance of the school,major,grade,and warning level for the students was analyzed using the Pearson correlation coefficient,random forest variable importance,and permutation importance.It was found that the characteristic of the major has a great impact on the academic warning level.Countermeasures such as dynamic adjustment of majors,reform of cognitive adaptation of courses,full-cycle academic support,and data-driven precise intervention were proposed to provide theoretical support and practical paths for universities to improve the efficiency of academic warning and enhance students’learning ability.
基金supported by the National Natural Science Foundation of China(52476200,52106244)the Guangdong Basic and Applied Basic Research Foundation(2024A1515030124)+1 种基金the Science and Technology Project of China Southern Power Grid under Grant GDKJXM20230246(030100KC23020017)the Fundamental Research Funds for the Central Universities。
文摘Due to batteries inconsistencies and potential faults in battery management systems,slight overcharging remains a common yet insufficiently understood safety risk,lacking effective warning methods.To illuminate the degradation behavior and failure mechanism of various overcharged states(100%SOC,105%SOC,110%SOC,and 115%SOC),multiple advanced in-situ characterization techniques(accelerating rate calorimeter,electrochemical impedance spectroscopy,ultrasonic scanning,and expansion instrument)were utilized.Additionally,re-overcharge-induced thermal runaway(TR)tests were conducted,with a specific emphasis on the evolution of the expansion force signal.Results indicated significant degradation at 110%SOC including conductivity loss,loss of lithium inventory,and loss of active material accompanied by internal gas generation.These failure behaviors slow down the expansion force rate during reovercharging,reducing the efficacy of active warnings that depend on rate thresholds of expansion force.Specifically,the warning time for 115%SOC battery is only 144 s,which is 740 s shorter than that for fresh battery,and the time to TR is advanced by 9 min.Moreover,the initial self-heating temperature(T1)is reduced by 62.4℃compared to that of fresh battery,reaching only 70.8℃.To address the low safety of overcharged batteries,a passive overcharge warning method utilizing relaxation expansion force was proposed,based on the continued gas generation after stopping charging,leading to a sustained increase in force.Compared to active methods that rely on thresholds of expansion force rate,the passive method can issue warnings 115 s earlier.By combining the passive and active warning methods,guaranteed effective overcharge warning can be issued 863-884 s before TR.This study introduces a novel perspective for enhancing the safety of batteries.
基金support by the National Natural Science Foundation of China(52201077)the Natural Science Foundation of Shandong Province(ZR2022QE191)+1 种基金Elite Scheme of Shandong University of Science and Technology(0104060541123)Talent introduction and Research Start-up Fund of Shandong University of Science and Technology(0104060510124).
文摘Coating microdefects and localized corrosion in coating/metal system are inevitable,accelerating the degradation of metal infrastructure.Early evaluating coating microdefects and detecting corrosion sites are urgent yet remain challenge to achieve.Herein,we propose a robust,universal and efficient fluorescence-based strategy for hierarchical warning of coating damage and metal corrosion by introducing the concepts of damage-induced fluorescence enhancement effect(DIE)and ionic-recognition induced quenching effect(RIQ).The coatings with dualresponsiveness for coating defect and steel corrosion are constructed by incorporating synthesized nanoprobes composed of metal organic frameworks(Ni–Zn-MOFs)loaded with Rhodamine B(RhB@MOFs).The initial damage to the coating causes an immediate intensification of fluorescence,while the specific ionic-recognition characteristic of RhB with Fe3t results in an evident fluorescence quenching,enabling the detection of coating damage and corrosion.Importantly,this nanoprobes are insensitive to the coating matrix and exhibit stable corrosion warning capability across various coating systems.Meanwhile,electrochemical investigations indicate that the impedance values of RM/EP maintain above 10^(8)Ωcm^(2)even after 60 days of immersion.Therefore,the incorporation of fluorescent nanoprobes greatly inhibits the intrusion of electrolytes into polymer and improves the corrosion protection performance of the coating.This powerful strategy towards dual-level damage warning provides insights for the development of long-term smart protective materials.
基金funded by the State Grid Corporation Science and Technology Project(5108-202218280A-2-391-XG).
文摘The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.
文摘Emphysematous pyelonephritis(EPN)is a severe,a lethal necrotizing upper urinary tract infection,characterized by gas production within the renal pa-renchyma,collecting system,or perinephric tissue.EPN is emerging as a sig-nificant concern,necessitating early diagnosis,severity assessment,and timely intervention to improve outcomes.This study proposes a modified National Early Warning Score 2(mNEWS 2)to enhance risk stratification and predictive accuracy in EPN management.The mNEWS 2 refines the original NEWS 2 system,which aggregates 6 physiological indicators(body temperature,systolic blood pressure,pulse rate,oxygen saturation,breathing rate,and degree of consciousness),by incorporating weighted risk stratification indices and specific cutoff values derived from clinical observations,statistical modeling,and predictive per-formance analysis.A pilot study identified optimal thresholds,with a score of 15 maximizing predictive performance for mortality risk and intervention needs,validated through receiver operating characteristic curve analysis.So,the mNEWS 2 score represents a significant advancement in EPN management,offering improved risk stratification and treatment outcomes.
文摘Purpose–This study aims to design and validate an emergency response method for high-speed railway earthquake early warning(EEW)systems based on the Propagation of Local Undamped Motion(PLUM)principle in order to enhance the timeliness and accuracy of warnings under seismic threats.Design/methodology/approach–A hierarchical architecture of the railway EEW system was adopted,in which self-built stations along the railway serve as the backbone and the national seismic network provides supplementary data.Warning zones were designed along the railway using overlapping trapezoidal layouts to cover seismic stations and reduce inter-regional time delays.Offline replay experiments were conducted using 82 historical earthquake events and records from 61 seismic stations to evaluate the timeliness and accuracy of warning information.Findings–The results indicate that the PLUM-based early warning method can issue emergency response information before destructive seismic waves arrive.Multiple earthquake experiments demonstrated high reliability and stability,with effective detection across different magnitudes and epicentral distances.Furthermore,the trapezoidal overlapping zone design improved regional consistency and significantly reduced missed alerts.Originality/value–This work represents the first systematic application of the PLUM method to high-speed railway EEW in China.By integrating railway operational requirements,the proposed method provides a practical and robust emergency response strategy,offering new insights into seismic risk mitigation for China’s high-speed railways.
文摘In order to solve the problems of high coupling and poor scalability of the traditional monomer early warning release system architecture,multi-level deployment in a complex network environment will lead to high investment in software and hardware and cannot achieve intensive multi-level deployment.This paper realizes the goal of system scalability by introducing micro service architecture and technology stack and realizes the goal of resource intensification by introducing the idea of a data forwarding agent.The designed architecture scheme has been practically applied in the“Jiangxi emergency early warning information release system software platform(phase I)project”(hereinafter referred to as“provincial emergency”),which meets the needs of flexible deployment of multi-level applications across meteorological wide area network(WAN),business private network of other commissions,offices,and bureaus,government extranet,Internet and other complex networks,and fully verifies the scientificity and rationality of the scheme.It has achieved the goal of intensive and scalable construction of provincial emergencies under the complex network environment,greatly improved the early warning capacity and communication capacity of emergencies and comprehensive disasters,provided a reliable guarantee for disaster prevention and reduction,and provided a reference for the construction of current and future early warning release system and capacity improvement project.
基金financially supported by the Heilongjiang Provincial Key R&D Program Project(No.GA21A204)Heilongjiang Provincial Natural Science Foundation of China(No.LH2022D031)the Research Project of Heilongjiang Province Bureau of Geology and Mineral Resources(No.HKY202302).
文摘Although previous researchers have attempted to decipher ore genesis and mineralization in the Erdaokan Ag-Pb-Zn deposit,some uncertainties regarding the mineralization process and evolution of both ore-forming fluids and magnetite types still need to be addressed.In this study,we obtained new EPMA,LA-ICP-MS,and in situ Fe isotope data from magnetite from the Erdaokan deposit,in order to better understand the mineralization mechanism and evolution of both magnetite and the ore-forming fluids.Our results identified seven types of magnetite at Erdaokan:disseminated magnetite(Mag1),coarse-grained magnetite(Mag2a),radial magnetite(Mag2b),fragmented fine-grained magnetite(Mag2c),vermicular gel magnetite(Mag3a1 and Mag3a2),colloidal magnetite(Mag3b)and dark gray magnetite(Mag4).All of the magnetite types were hydrothermal in origin and generally low in Ti(<400 ppm)and Ni(<800 ppm),while being enriched in light Fe isotopes(δ^(56)Fe ranging from−1.54‰to−0.06‰).However,they exhibit different geochemical signatures and are thus classified into high-manganese magnetite(Mag1,MnO>5 wt%),low-silicon magnetite(Mag2a-c,SiO_(2)<1 wt%),high-silicon magnetite(Mag3a-b,SiO_(2)from 1 to 7 wt%)and high-silicon-manganese magnetite(Mag4,SiO_(2)>1 wt%,MnO>0.2 wt%),each being formed within distinct hydrothermal environments.Based on mineralogy,elemental geochemistry,Fe isotopes,temperature trends,TMg-mag and(Ti+V)vs.(Al+Mn)diagrams,we propose that the Erdaokan Ag-Pb-Zn deposit underwent multi-stage mineralization,which can be broken down into four stages and nine sub-stages.Mag1,Mag2a-c,Mag3a-b and Mag4 were formed during the first sub-stage of each of the four stages,respectively.Additionally,fluid mixing,cooling and depressurization boiling were identified as the main mechanisms for mineral precipitation.The enrichment of Ag was significantly enhanced by the superposition of multi-stage ore-forming hydrothermal fluids in the Erdaokan Ag-Pb-Zn deposit.
文摘An effective warning system for flash floods along the upper River des Peres, a small urban stream in eastern Missouri, USA, is based on three enterprise-level, automated rain gauges.Because floods in this 25 km~2 basin develop rapidly and are commonly caused by small but intense thunderstorm cells, these rain gauges were necessarily deployed within the watershed, and immediate telemetry and processing of rainfall delivered in 5-minute intervals is required. Available data show that damaging floods in this area occur only 30 min to 3 h following the delivery of 38 mm of rainfall or more in a single hour. Water levels along this stream can rise more than 3 m/h. Since full deployment in Nov. 2021, our system has successfully predicted 3 significant floods with one false positive.
基金supported by the King Abdullah University of Science and Technology(KAUST)。
文摘Seismic data denoising is a critical process usually applied at various stages of the seismic processing workflow,as our ability to mitigate noise in seismic data affects the quality of our subsequent analyses.However,finding an optimal balance between preserving seismic signals and effectively reducing seismic noise presents a substantial challenge.In this study,we introduce a multi-stage deep learning model,trained in a self-supervised manner,designed specifically to suppress seismic noise while minimizing signal leakage.This model operates as a patch-based approach,extracting overlapping patches from the noisy data and converting them into 1D vectors for input.It consists of two identical sub-networks,each configured differently.Inspired by the transformer architecture,each sub-network features an embedded block that comprises two fully connected layers,which are utilized for feature extraction from the input patches.After reshaping,a multi-head attention module enhances the model’s focus on significant features by assigning higher attention weights to them.The key difference between the two sub-networks lies in the number of neurons within their fully connected layers.The first sub-network serves as a strong denoiser with a small number of neurons,effectively attenuating seismic noise;in contrast,the second sub-network functions as a signal-add-back model,using a larger number of neurons to retrieve some of the signal that was not preserved in the output of the first sub-network.The proposed model produces two outputs,each corresponding to one of the sub-networks,and both sub-networks are optimized simultaneously using the noisy data as the label for both outputs.Evaluations conducted on both synthetic and field data demonstrate the model’s effectiveness in suppressing seismic noise with minimal signal leakage,outperforming some benchmark methods.
基金supported by the Spark Program of Earthquake Science and Technology(No.XH23003C)。
文摘The earthquake early warning system is an effective means of disaster reduction to reduce losses caused by earthquakes,it can release earthquake warning information to the public before destructive seismic waves reach the warning target area,and carry out automatic disposal of lifeline engineering facilities.Through the construction of the National Earthquake Intensity Rapid Reporting and Early Warning Project,an earthquake early warning network consisting of over 1900 monitoring stations has been established in the Beijing-Tianjin-Hebei Urban Agglomeration.The early warning system has achieved second level earthquake warning and minute level intensity rapid reporting.The implementation of these functions relies on the system's ability to timely,accurately,and reliably identify seismic waves.But with the development of social economy,the background noise of earthquake observation environment is becoming increasingly complex,which brings certain challenges to earthquake wave recognition,some interference events have the risk of triggering the earthquake warning system incorrectly.Therefore,this article focuses on seismic wave recognition in complex noise environments and proposes a seismic wave detection method based on triangulation to enhance the antiinterference ability and recognition accuracy of early warning systems.
基金Projects(52209132, 52309156) supported by the National Natural Science Foundation of ChinaProject(BK20251905) supported by the Natural Science Foundation of Jiangsu Province,China+2 种基金Project(252102320037) supported by the Henan Province Science and Technology Research,ChinaProject(CKWV20231173/KY) supported by the CRSRI Open Research Program,ChinaProject(2023KSD15) supported by the Open Research Fund of Hubei Provincial Key Laboratory of Construction and Management in Hydropower Engineering,China。
文摘The collapse of rock masses in fault-developed zones poses significant safety challenges during the excavation of high-stress underground caverns. This study investigates the spatiotemporal evolution of the collapse mechanisms of the cavern in the Yebatan Hydropower Station through using microseismic (MS) monitoring and displacement measurements. We developed a multi-parameter deformation early warning model that integrates three critical indicators: deformation rate, rate increment, and tangential angle of the deformation time curve. The results of the early warning model show a significant and abrupt increase in the deformation of the rock mass during the collapse process. The safety and stability of the local cavern in the face of excavation-induced disturbances are meticulously assessed utilizing MS data. Spatiotemporal analysis of the MS monitoring indicates a high frequency of MS events during the blasting phase, with a notable clustering of these events in the vicinity of the fault. These research results provide a valuable reference for risk warnings and stability assessments in the fault development zones of analogous caverns.
文摘This study employs deformation monitoring data acquired during the construction of the Haoji railway large-scale bridge to investigate the displacement behavior of the subgrades,catenary columns,and tracks.Emphasis is placed on data acquisition and processing methods using total stations and automated monitoring systems.Through a comprehensive analysis of lateral,longitudinal,and vertical displacement data from 26 subgrade monitoring points,catenary columns,and track sections,this research evaluates how construction activities influence railway structures.The results show that displacement variations in the subgrades,catenary columns,and tracks remained within the established alert thresholds,exhibiting stable deformation trends and indicating that any adverse environmental impact was effectively contained.Furthermore,this paper proposes an early warning mechanism based on an automated monitoring system,which can promptly detect abnormal deformations and initiate emergency response procedures,thereby ensuring the safe operation of the railway.The integration of big data analysis and deformation prediction models offers a practical foundation for future safety management in railway construction.
文摘This paper presents a new criterion for determining the unloading points quantitatively and consistently in a multi-stage triaxial test.The radial strain gradient(RSG)is first introduced as an arc tangent function of the rate of change of radial strain to time.RSG is observed to correlate closely with the stress state of a compressed sample,and reaches a horizontal asymptote as approaching failure.For a given rock type,RSG value at peak stress is almost the same,irrespective of the porosity and permeability.These findings lead to the development of RSG criterion:Unloading points can be precisely determined at the time when RSG reaches a pre-determined value that is a little smaller than or equal to the RSG at peak stress.The RSG criterion is validated against other criteria and the single-stage triaxial test on various types of rocks.Failure envelopes from the RSG criterion match well with those from single-stage tests.A practical procedure is recommended to use the RSG criterion:an unconfined compression or single-stage test is first conducted to determine the RSG at peak stress for one sample,the unloading point is then selected to be a value close to the RSG at peak stress,and the multi-stage test is finally performed on another sample using the pre-selected RSG unloading criterion.Generally,the RSG criterion is applicable for any type of rocks,especially brittle rocks,where other criteria are not suitable.Further,it can be practically implemented on the most available rock mechanical testing instruments.
基金supported by the National Natural Science Foundation of China(Grant No:52271300,52071337)National Key Research and Development Program of China(2022YFC2806501)+1 种基金High-tech Ship Research Projects Sponsored by MIIT(CBG2N21-4-25)Program for Changjiang Scholars and Innovative Research Team in University(Grant No.IRT14R58).
文摘A new hang-off system has been proposed to improve the security of risers in hang-off modes during typhoons.However,efficient anti-typhoon evacuation strategies have not been investigated.Optimiza-tion model and method for the anti-typhoon evacuation strategies should be researched.Therefore,multi-objective functions are proposed based on operation time,evacuation speed stability,and steering stability.An evacuation path model and a dynamic model of risers with the new hang-off system are developed for design variables and constraints.A multi-objective optimization model with high-dimensional variables and complex constraints is established.Finally,a three-stage optimization method based on genetic algorithm,least square method,and the penalty function method is proposed to solve the multi-objective optimization model.Optimization results show that the operation time can be reduced through operation parameter optimization,especially evacuation heading optimization.The optimal anti-typhoon strategy is evacuation with all risers suspended along a variable path when the direction angle is large,while evacuation with all risers suspended along a straight path at another di-rection angle.Besides,the influencing factors on anti-typhoon evacuation strategies indicate that the proposed optimization model and method have strong applicability to working conditions and remarkable optimization effects.
文摘Leveraging the achievements of the smart meteorological system nationwide,a meteorological monitoring and early warning system for alfalfa pests and diseases can be formed through the establishment of four systems,namely,"real-time monitoring system,forecasting and prediction system,monitoring and early warning system,and smart service system".It will enable intelligent,dynamic meteorological monitoring,early warning,and forecasting services for the occurrence and development of alfalfa pests and diseases,providing technical support for scientifically controlling their harm and improving yield and quality.
文摘BACKGROUND Enhancing postoperative recovery is a critical goal in clinical practice and the application of innovative nursing models can significantly contribute to this objective.AIM To investigate the effects of motivational and early warning nursing interventions on wound healing and sociopsychological adaptability in patients undergoing hepatobiliary surgery.METHODS A total of 160 patients who underwent surgical treatment in the hepatobiliary department of our hospital from January 2022 to June 2024 were selected and randomly divided into a control group and an observation group,with 80 patients in each group.The control group received routine nursing care,while the observation group received a combination of motivational and early warning nursing interventions.The wound healing status(class A,B,and C wound healing and healing time),social psychological adaptability,complications,postoperative recovery,and quality of life were compared between the two groups.RESULTS The wound healing rate in the observation group was higher than that in the control group,while the wound healing time was shorter(P<0.05).The social adaptability scores in the observation group were higher than those in the control group(P<0.05).The incidence of complications was lower in the observation group than in the control group(P<0.05).Postoperative recovery and quality of life were better in the observation group than in the control group(P<0.05).CONCLUSION Motivational and early warning nursing interventions are beneficial for promoting wound healing in patients undergoing hepatobiliary surgery,reducing the incidence of complications and improving socio-psychological adaptability and postoperative quality of life.These interventions should be promoted in clinical nursing practice.
