To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances...To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances.Blasting vibration monitoring was conducted in a deep-buried drill-and-blast tunnel to characterize in-situ dynamic loading conditions.Subsequently,true triaxial compression tests incorporating multi-source disturbances were performed using a self-developed wide-low-frequency true triaxial system to simulate disturbance accumulation and damage evolution in granite.The results demonstrate that combined dynamic disturbances and unloading damage significantly accelerate strength degradation and trigger shear-slip failure along preferentially oriented blast-induced fractures,with strength reductions up to 16.7%.Layered failure was observed on the free surface of pre-damaged granite under biaxial loading,indicating a disturbance-induced fracture localization mechanism.Time-stress-fracture-energy coupling fields were constructed to reveal the spatiotemporal characteristics of fracture evolution.Critical precursor frequency bands(105-150,185-225,and 300-325 kHz)were identified,which serve as diagnostic signatures of impending failure.A dynamic instability mechanism driven by multi-source disturbance superposition and pre-damage evolution was established.Furthermore,a grouting-based wave-absorption control strategy was proposed to mitigate deep dynamic disasters by attenuating disturbance amplitude and reducing excitation frequency.展开更多
The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-...The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-amine(PEA),to achieve a formaldehyde(FA)-sensitive and fluorescence-enhanced sensing film.Utilizing the specific Aza-Cope rearrangement reaction of allylamine of PEA and FA to generate a strong fluorescent product emitted at approximately 480 nm,we chose a PC whose blue band edge of stopband overlapped with the fluorescence emission wavelength.In virtue of the fluorescence enhancement property derived from slow photon effect of PC,FA was detected highly selectively and sensitively.The limit of detection(LoD)was calculated to be 1.38 nmol/L.Furthermore,the fast detection of FA(within 1 min)is realized due to the interconnected three-dimensional macroporous structure of the inverse opal PC and its high specific surface area.The prepared sensing film can be used for the detection of FA in air,aquatic products and living cells.The very close FA content in indoor air to the result from FA detector,the recovery rate of 101.5%for detecting FA in aquatic products and fast fluorescence imaging in 2 min for living cells demonstrate the reliability and accuracy of our method in practical applications.展开更多
Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.P...Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.展开更多
This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimiz...This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimization effect,etc.,aiming to better provide a certain guideline and reference for relevant researchers.展开更多
Taking the Ming Tombs Forest Farm in Beijing as the research object,this research applied multi-source data fusion and GIS heat-map overlay analysis techniques,systematically collected bird observation point data from...Taking the Ming Tombs Forest Farm in Beijing as the research object,this research applied multi-source data fusion and GIS heat-map overlay analysis techniques,systematically collected bird observation point data from the Global Biodiversity Information Facility(GBIF),population distribution data from the Oak Ridge National Laboratory(ORNL)in the United States,as well as information on the composition of tree species in suitable forest areas for birds and the forest geographical information of the Ming Tombs Forest Farm,which is based on literature research and field investigations.By using GIS technology,spatial processing was carried out on bird observation points and population distribution data to identify suitable bird-watching areas in different seasons.Then,according to the suitability value range,these areas were classified into different grades(from unsuitable to highly suitable).The research findings indicated that there was significant spatial heterogeneity in the bird-watching suitability of the Ming Tombs Forest Farm.The north side of the reservoir was generally a core area with high suitability in all seasons.The deep-aged broad-leaved mixed forests supported the overlapping co-existence of the ecological niches of various bird species,such as the Zosterops simplex and Urocissa erythrorhyncha.In contrast,the shallow forest-edge coniferous pure forests and mixed forests were more suitable for specialized species like Carduelis sinica.The southern urban area and the core area of the mausoleums had relatively low suitability due to ecological fragmentation or human interference.Based on these results,this paper proposed a three-level protection framework of“core area conservation—buffer zone management—isolation zone construction”and a spatio-temporal coordinated human-bird co-existence strategy.It was also suggested that the human-bird co-existence space could be optimized through measures such as constructing sound and light buffer interfaces,restoring ecological corridors,and integrating cultural heritage elements.This research provided an operational technical approach and decision-making support for the scientific planning of bird-watching sites and the coordination of ecological protection and tourism development.展开更多
Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantita...Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.展开更多
When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ...When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.展开更多
The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initiall...The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.展开更多
Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion.Nevertheless,when fusing highly conflicting evidence it may pro-duce counterintuitive outcomes.To address this iss...Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion.Nevertheless,when fusing highly conflicting evidence it may pro-duce counterintuitive outcomes.To address this issue,a fusion approach based on a newly defined belief exponential diver-gence and Deng entropy is proposed.First,a belief exponential divergence is proposed as the conflict measurement between evidences.Then,the credibility of each evidence is calculated.Afterwards,the Deng entropy is used to calculate information volume to determine the uncertainty of evidence.Then,the weight of evidence is calculated by integrating the credibility and uncertainty of each evidence.Ultimately,initial evidences are amended and fused using Dempster’s rule of combination.The effectiveness of this approach in addressing the fusion of three typical conflict paradoxes is demonstrated by arithmetic exam-ples.Additionally,the proposed approach is applied to aerial tar-get recognition and iris dataset-based classification to validate its efficacy.Results indicate that the proposed approach can enhance the accuracy of target recognition and effectively address the issue of fusing conflicting evidences.展开更多
Lower Limb Exoskeletons(LLEs)are receiving increasing attention for supporting activities of daily living.In such active systems,an intelligent controller may be indispensable.In this paper,we proposed a locomotion in...Lower Limb Exoskeletons(LLEs)are receiving increasing attention for supporting activities of daily living.In such active systems,an intelligent controller may be indispensable.In this paper,we proposed a locomotion intention recognition system based on time series data sets derived from human motion signals.Composed of input data and Deep Learning(DL)algorithms,this framework enables the detection and prediction of users’movement patterns.This makes it possible to predict the detection of locomotion modes,allowing the LLEs to provide smooth and seamless assistance.The pre-processed eight subjects were used as input to classify four scenes:Standing/Walking on Level Ground(S/WOLG),Up the Stairs(US),Down the Stairs(DS),and Walking on Grass(WOG).The result showed that the ResNet performed optimally compared to four algorithms(CNN,CNN-LSTM,ResNet,and ResNet-Att)with an approximate evaluation indicator of 100%.It is expected that the proposed locomotion intention system will significantly improve the safety and the effectiveness of LLE due to its high accuracy and predictive performance.展开更多
Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development...Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.展开更多
Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to pred...Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.展开更多
Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues.However,the complexity of porous media often limits the effectiveness of indivi...Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues.However,the complexity of porous media often limits the effectiveness of individual prediction methods.This study introduces a novel Particle Swarm Optimization-based Permeability Integrated Prediction model(PSO-PIP),which incorporates a particle swarm optimization algorithm enhanced with dy-namic clustering and adaptive parameter tuning(KGPSO).The model integrates multi-source data from the Lattice Boltzmann Method(LBM),Pore Network Modeling(PNM),and Finite Difference Method(FDM).By assigning optimal weight coefficients to the outputs of these methods,the model minimizes deviations from actual values and enhances permeability prediction performance.Initially,the computational performances of the LBM,PNM,and FDM are comparatively analyzed on datasets consisting of sphere packings and real rock samples.It is observed that these methods exhibit computational biases in certain permeability ranges.The PSOPIP model is proposed to combine the strengths of each computational approach and mitigate their limitations.The PSO-PIP model consistently produces predictions that are highly congruent with actual permeability values across all prediction intervals,significantly enhancing prediction accuracy.The outcomes of this study provide a new tool and perspective for the comprehensive,rapid,and accurate prediction of permeability in porous media.展开更多
In the context of the digital transformation of vocational education,a quality evaluation index system has been constructed.Based on a questionnaire survey conducted among higher vocational colleges and enterprises in...In the context of the digital transformation of vocational education,a quality evaluation index system has been constructed.Based on a questionnaire survey conducted among higher vocational colleges and enterprises in Hainan Province,it has been found that the quality of vocational education generally depends on the talent training program and professional construction at the macro level.At the meso level,the teacher level and teaching environment are critical,while at the micro level,the evaluation of talent training quality cannot be underestimated.Strategies for quality improvement in vocational education are proposed from the perspectives of talent training programs,major construction,teacher development,teaching environment,and talent training quality,all under the lens of digital transformation.展开更多
BACKGROUND The diagnosis of gastric carcinoma(GC)is essential for improving clinical outcomes.However,the biomarkers currently used for GC screening are not ideal.AIM To explore the diagnostic implications of the neut...BACKGROUND The diagnosis of gastric carcinoma(GC)is essential for improving clinical outcomes.However,the biomarkers currently used for GC screening are not ideal.AIM To explore the diagnostic implications of the neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR),and systemic immune-inflammatory index(SII)for GC.METHODS The baseline data of 133 patients with GC and 134 patients with precancerous gastric conditions admitted between January 2022 and December 2023 were retrospectively analyzed.The information on peripheral blood platelet,neutrophil,and lymphocyte counts in each patient was collected,and the NLR,PLR,and SII levels of both groups were calculated.Additionally,multivariate logistic regression analysis was conducted,and the diagnostic implications of NLR,PLR,and SII in differentiating patients with precancerous gastric conditions,compared with those with GC,were analyzed through receiver operating characteristic(ROC)curves.RESULTS The data indicated that NLR,PLR,and SII had abnormally increased levels in the patients with GC.Gender and body mass index were risk factors for the occurrence of GC.ROC data revealed that the areas under the curve of three patients with precancerous gastric conditions,who were differentiated from those with GC,were 0.824,0.787,and 0.842,respectively.CONCLUSION NLR,PLR,and SII are all abnormally expressed in GC and have diagnostic implications,especially when used as joint indicators,in distinguishing patients with precancerous gastric conditions from those with GC.展开更多
BACKGROUND Anastomotic leakage(AL)is a serious complication following rectal cancer surgery and is associated with increased recurrence,mortality,extended hospital stays,and delayed chemotherapy.The Onodera prognostic...BACKGROUND Anastomotic leakage(AL)is a serious complication following rectal cancer surgery and is associated with increased recurrence,mortality,extended hospital stays,and delayed chemotherapy.The Onodera prognostic nutritional index(OPNI)and inflammation-related biomarkers,such as the neutrophil-lymphocyte ratio(NLR)and platelet-to-lymphocyte ratio(PLR),have been studied in the context of cancer prognosis,but their combined efficacy in predicting AL remains unclear.AIM To investigate the relationships between AL and these markers and developed a predictive model for AL.METHODS A retrospective cohort study analyzed the outcomes of 434 patients who had undergone surgery for rectal cancer at a tertiary cancer center from 2016 to 2023.The patients were divided into two groups on the basis of the occurrence of AL:One group consisted of patients who experienced AL(n=49),and the other group did not(n=385).The investigation applied logistic regression to develop a risk prediction model utilizing clinical,pathological,and laboratory data.The efficacy of this model was then evaluated through receiver operating characteristic curve analysis.RESULTS In the present study,11.28%of the participants(49 out of 434 participants)suffered from AL.Multivariate analysis revealed that preoperative levels of the OPNI,NLR,and PLR emerged as independent risk factors for AL,with odds ratios of 0.705(95%CI:0.641-0.775,P=0.012),1.628(95%CI:1.221-2.172,P=0.024),and 0.994(95%CI:0.989-0.999,P=0.031),respectively.These findings suggest that these biomarkers could effectively predict AL risk.Furthermore,the proposed predictive model has superior discriminative ability,as demonstrated by an area under the curve of 0.910,a sensitivity of 0.898,and a specificity of 0.826,reflecting its high level of accuracy.CONCLUSION The risk of AL in rectal cancer surgery patients can be effectively predicted by assessing the preoperative levels of serum nutritional biomarkers and inflammatory indicators,emphasizing their importance in the preoperative evaluation process.展开更多
ACKGROUND The hemoglobin glycation index(HGI)represents the discrepancy between the glucose management indicator(GMI)based on mean blood glucose levels and laboratory values of glycated hemoglobin(HbA1c).The HGI is a ...ACKGROUND The hemoglobin glycation index(HGI)represents the discrepancy between the glucose management indicator(GMI)based on mean blood glucose levels and laboratory values of glycated hemoglobin(HbA1c).The HGI is a promising indicator for identifying individuals with excessive glycosylation,facilitating personalized evaluation and prediction of diabetic complications.However,the factors influencing the HGI in patients with type 1 diabetes(T1D)remain unclear.Autoimmune destruction of pancreaticβcells is central in T1D pathogenesis,yet insulin resistance can also be a feature of patients with T1D and their coexistence is called“double diabetes”(DD).However,knowledge regarding the relationship between DD features and the HGI in T1D is limited.AIM To assess the association between the HGI and DD features in adults with T1D.METHODS A total of 83 patients with T1D were recruited for this cross-sectional study.Laboratory HbA1c and GMI from continuous glucose monitoring data were collected to calculate the HGI.DD features included a family history of type 2 diabetes,overweight/obesity/central adiposity,hypertension,atherogenic dyslipidemia,an abnormal percentage of body fat(PBF)and/or visceral fat area(VFA)and decreased estimated insulin sensitivity.Skin autofluorescence of advanced glycation end products(SAF-AGEs),diabetic complications,and DD features were assessed,and their association with the HGI was analyzed.RESULTS A discrepancy was observed between HbA1c and GMI among patients with T1D and DD.A higher HGI was associated with an increased number of SAF-AGEs and a higher prevalence of diabetic microangiopathy(P=0.030),particularly retinopathy(P=0.031).Patients with three or more DD features exhibited an eight-fold increased risk of having a high HGI,compared with those without DD features(adjusted odds ratio=8.12;95%confidence interval:1.52-43.47).Specifically,an elevated PBF and/or VFA and decreased estimated insulin sensitivity were associated with high HGI.Regression analysis identified estimated insulin sensitivity and VFA as factors independently associated with HGI.CONCLUSION In patients with T1D,DD features are associated with a higher HGI,which represents a trend toward excessive glycosylation and is associated with a higher prevalence of chronic diabetic complications.展开更多
BACKGROUND Gastric cancer(GC)is the fifth most common cancer and the third leading cause of cancer-related deaths in China.Many patients with GC frequently experience symptoms related to the disease,including anorexia...BACKGROUND Gastric cancer(GC)is the fifth most common cancer and the third leading cause of cancer-related deaths in China.Many patients with GC frequently experience symptoms related to the disease,including anorexia,nausea,vomiting,and other discomforts,and often suffer from malnutrition,which in turn negatively affects perioperative safety,prognosis,and the effectiveness of adjuvant therapeutic measures.Consequently,some nutritional indicators such as nutritional risk index(NRI),prognostic nutritional index(PNI),and systemic immune-inflammatorynutritional index(SIINI)can be used as predictors of the prognosis of GC patients.AIM To examine the prognostic significance of PNI,NRI,and SIINI in postoperative patients with GC.METHODS A retrospective analysis was conducted on the clinical data of patients with GC who underwent surgical treatment at the Guangxi Medical University Cancer Hospital between January 2010 and December 2018.The area under the receiver operating characteristic(ROC)curve was assessed using ROC curve analysis,and the optimal cutoff values for NRI,PNI,and SIINI were identified using the You-Review-HTMLden index.Survival analysis was performed using the Kaplan-Meier method.In addition,univariate and multivariate analyses were conducted using the Cox proportional hazards regression model.RESULTS This study included a total of 803 patients.ROC curves were used to evaluate the prognostic ability of NRI,PNI,and SIINI.The results revealed that SIINI had superior predictive accuracy.Survival analysis indicated that patients with GC in the low SIINI group had a significantly better survival rate than those in the high SIINI group(P<0.05).Univariate analysis identified NRI[hazard ratio(HR)=0.68,95%confidence interval(CI):0.52-0.89,P=0.05],PNI(HR=0.60,95%CI:0.46-0.79,P<0.001),and SIINI(HR=2.10,95%CI:1.64-2.69,P<0.001)as prognostic risk factors for patients with GC.However,multifactorial analysis indicated that SIINI was an independent risk factor for the prognosis of patients with GC(HR=1.65,95%CI:1.26-2.16,P<0.001).CONCLUSION Analysis of clinical retrospective data revealed that SIINI is a valuable indicator for predicting the prognosis of patients with GC.Compared with NRI and PNI,SIINI may offer greater application for prognostic assessment.展开更多
The Dst index has been commonly used to measure the geomagnetic effectiveness of magnetic storm events for several decades.Based on Burton’s empirical Dst model and the global magneto-hydrodynamic(MHD)simulation of E...The Dst index has been commonly used to measure the geomagnetic effectiveness of magnetic storm events for several decades.Based on Burton’s empirical Dst model and the global magneto-hydrodynamic(MHD)simulation of Earth’s magnetosphere,here we proposed a semi-empirical model to forecast the Dst index during geomagnetic storms.In this model,the ring current contribution to the Dst index is derived from Burton’s model,while the contributions from other current systems are obtained from the global MHD simulation.In order to verify the model accuracy,a number of recent magnetic storm events are tested and the simulated Dst index is compared with the observation through the correlation coefficient(CC),prediction efficiency(PE),root mean square error(RMSE)and central root mean square error(CRMSE).The results indicate that,in the context of moderate and intense geomagnetic storm events,the semi-empirical model performs well in global MHD simulations,showing relatively higher CC and PE,and lower RMSE and CRMSE compared to those from the empirical model.Compared with the physics-based ring current models,this model inherits the advantage of fast processing from the empirical model,and easy implementation in a global MHD model of Earth’s magnetosphere.Therefore,it is suitable for the Dst estimation under a context of a global MHD simulation.展开更多
基金supported by the National Key R&D Program of China(No.2023YFB2603602)the National Natural Science Foundation of China(Nos.52222810 and 52178383).
文摘To elucidate the fracturing mechanism of deep hard rock under complex disturbance environments,this study investigates the dynamic failure behavior of pre-damaged granite subjected to multi-source dynamic disturbances.Blasting vibration monitoring was conducted in a deep-buried drill-and-blast tunnel to characterize in-situ dynamic loading conditions.Subsequently,true triaxial compression tests incorporating multi-source disturbances were performed using a self-developed wide-low-frequency true triaxial system to simulate disturbance accumulation and damage evolution in granite.The results demonstrate that combined dynamic disturbances and unloading damage significantly accelerate strength degradation and trigger shear-slip failure along preferentially oriented blast-induced fractures,with strength reductions up to 16.7%.Layered failure was observed on the free surface of pre-damaged granite under biaxial loading,indicating a disturbance-induced fracture localization mechanism.Time-stress-fracture-energy coupling fields were constructed to reveal the spatiotemporal characteristics of fracture evolution.Critical precursor frequency bands(105-150,185-225,and 300-325 kHz)were identified,which serve as diagnostic signatures of impending failure.A dynamic instability mechanism driven by multi-source disturbance superposition and pre-damage evolution was established.Furthermore,a grouting-based wave-absorption control strategy was proposed to mitigate deep dynamic disasters by attenuating disturbance amplitude and reducing excitation frequency.
基金supported by the National Natural Science Foundation of China(21663032 and 22061041)the Open Sharing Platform for Scientific and Technological Resources of Shaanxi Province(2021PT-004)the National Innovation and Entrepreneurship Training Program for College Students of China(S202110719044)。
文摘The SiO_(2) inverse opal photonic crystals(PC)with a three-dimensional macroporous structure were fabricated by the sacrificial template method,followed by infiltration of a pyrene derivative,1-(pyren-8-yl)but-3-en-1-amine(PEA),to achieve a formaldehyde(FA)-sensitive and fluorescence-enhanced sensing film.Utilizing the specific Aza-Cope rearrangement reaction of allylamine of PEA and FA to generate a strong fluorescent product emitted at approximately 480 nm,we chose a PC whose blue band edge of stopband overlapped with the fluorescence emission wavelength.In virtue of the fluorescence enhancement property derived from slow photon effect of PC,FA was detected highly selectively and sensitively.The limit of detection(LoD)was calculated to be 1.38 nmol/L.Furthermore,the fast detection of FA(within 1 min)is realized due to the interconnected three-dimensional macroporous structure of the inverse opal PC and its high specific surface area.The prepared sensing film can be used for the detection of FA in air,aquatic products and living cells.The very close FA content in indoor air to the result from FA detector,the recovery rate of 101.5%for detecting FA in aquatic products and fast fluorescence imaging in 2 min for living cells demonstrate the reliability and accuracy of our method in practical applications.
基金supported by Natural Science Foundation of China(Nos.62303126,62362008,author Z.Z,https://www.nsfc.gov.cn/,accessed on 20 December 2024)Major Scientific and Technological Special Project of Guizhou Province([2024]014)+2 种基金Guizhou Provincial Science and Technology Projects(No.ZK[2022]General149) ,author Z.Z,https://kjt.guizhou.gov.cn/,accessed on 20 December 2024)The Open Project of the Key Laboratory of Computing Power Network and Information Security,Ministry of Education under Grant 2023ZD037,author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024)Open Research Project of the State Key Laboratory of Industrial Control Technology,Zhejiang University,China(No.ICT2024B25),author Z.Z,https://www.gzu.edu.cn/,accessed on 20 December 2024).
文摘Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.
文摘This paper deeply discusses the causes of gear howling noise,the identification and analysis of multi-source excitation,the transmission path of dynamic noise,simulation and experimental research,case analysis,optimization effect,etc.,aiming to better provide a certain guideline and reference for relevant researchers.
基金Sponsored by Beijing Youth Innovation Talent Support Program for Urban Greening and Landscaping——The 2024 Special Project for Promoting High-Quality Development of Beijing’s Landscaping through Scientific and Technological Innovation(KJCXQT202410).
文摘Taking the Ming Tombs Forest Farm in Beijing as the research object,this research applied multi-source data fusion and GIS heat-map overlay analysis techniques,systematically collected bird observation point data from the Global Biodiversity Information Facility(GBIF),population distribution data from the Oak Ridge National Laboratory(ORNL)in the United States,as well as information on the composition of tree species in suitable forest areas for birds and the forest geographical information of the Ming Tombs Forest Farm,which is based on literature research and field investigations.By using GIS technology,spatial processing was carried out on bird observation points and population distribution data to identify suitable bird-watching areas in different seasons.Then,according to the suitability value range,these areas were classified into different grades(from unsuitable to highly suitable).The research findings indicated that there was significant spatial heterogeneity in the bird-watching suitability of the Ming Tombs Forest Farm.The north side of the reservoir was generally a core area with high suitability in all seasons.The deep-aged broad-leaved mixed forests supported the overlapping co-existence of the ecological niches of various bird species,such as the Zosterops simplex and Urocissa erythrorhyncha.In contrast,the shallow forest-edge coniferous pure forests and mixed forests were more suitable for specialized species like Carduelis sinica.The southern urban area and the core area of the mausoleums had relatively low suitability due to ecological fragmentation or human interference.Based on these results,this paper proposed a three-level protection framework of“core area conservation—buffer zone management—isolation zone construction”and a spatio-temporal coordinated human-bird co-existence strategy.It was also suggested that the human-bird co-existence space could be optimized through measures such as constructing sound and light buffer interfaces,restoring ecological corridors,and integrating cultural heritage elements.This research provided an operational technical approach and decision-making support for the scientific planning of bird-watching sites and the coordination of ecological protection and tourism development.
基金supported by the National Natural Science Foundation of China(Nos.52279107 and 52379106)the Qingdao Guoxin Jiaozhou Bay Second Submarine Tunnel Co.,Ltd.,the Academician and Expert Workstation of Yunnan Province(No.202205AF150015)the Science and Technology Innovation Project of YCIC Group Co.,Ltd.(No.YCIC-YF-2022-15)。
文摘Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.
文摘When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.
基金supported by the National Key Research and Development Program of China(grant number 2019YFE0123600)。
文摘The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.
基金supported by the National Natural Science Foundation of China(61903305,62073267)the Fundamental Research Funds for the Central Universities(HXGJXM202214).
文摘Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion.Nevertheless,when fusing highly conflicting evidence it may pro-duce counterintuitive outcomes.To address this issue,a fusion approach based on a newly defined belief exponential diver-gence and Deng entropy is proposed.First,a belief exponential divergence is proposed as the conflict measurement between evidences.Then,the credibility of each evidence is calculated.Afterwards,the Deng entropy is used to calculate information volume to determine the uncertainty of evidence.Then,the weight of evidence is calculated by integrating the credibility and uncertainty of each evidence.Ultimately,initial evidences are amended and fused using Dempster’s rule of combination.The effectiveness of this approach in addressing the fusion of three typical conflict paradoxes is demonstrated by arithmetic exam-ples.Additionally,the proposed approach is applied to aerial tar-get recognition and iris dataset-based classification to validate its efficacy.Results indicate that the proposed approach can enhance the accuracy of target recognition and effectively address the issue of fusing conflicting evidences.
基金the financial support of Shanghai Science and Technology innovation action plan(19DZ2203600).
文摘Lower Limb Exoskeletons(LLEs)are receiving increasing attention for supporting activities of daily living.In such active systems,an intelligent controller may be indispensable.In this paper,we proposed a locomotion intention recognition system based on time series data sets derived from human motion signals.Composed of input data and Deep Learning(DL)algorithms,this framework enables the detection and prediction of users’movement patterns.This makes it possible to predict the detection of locomotion modes,allowing the LLEs to provide smooth and seamless assistance.The pre-processed eight subjects were used as input to classify four scenes:Standing/Walking on Level Ground(S/WOLG),Up the Stairs(US),Down the Stairs(DS),and Walking on Grass(WOG).The result showed that the ResNet performed optimally compared to four algorithms(CNN,CNN-LSTM,ResNet,and ResNet-Att)with an approximate evaluation indicator of 100%.It is expected that the proposed locomotion intention system will significantly improve the safety and the effectiveness of LLE due to its high accuracy and predictive performance.
基金National Key Research and Development Program of China(No.2023YFB3907103).
文摘Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.
基金supported by the National Natural Science Foundation of China(41977215)。
文摘Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.
基金supported by the National Key Research and Devel-opment Program of China (Grant No.2022YFC3005503)the National Natural Science Foundation of China (Grant Nos.52322907,52179141,U23B20149,U2340232)+1 种基金the Fundamental Research Funds for the Central Universities (Grant Nos.2042024kf1031,2042024kf0031)the Key Program of Science and Technology of Yunnan Province (Grant Nos.202202AF080004,202203AA080009).
文摘Accurately and efficiently predicting the permeability of porous media is essential for addressing a wide range of hydrogeological issues.However,the complexity of porous media often limits the effectiveness of individual prediction methods.This study introduces a novel Particle Swarm Optimization-based Permeability Integrated Prediction model(PSO-PIP),which incorporates a particle swarm optimization algorithm enhanced with dy-namic clustering and adaptive parameter tuning(KGPSO).The model integrates multi-source data from the Lattice Boltzmann Method(LBM),Pore Network Modeling(PNM),and Finite Difference Method(FDM).By assigning optimal weight coefficients to the outputs of these methods,the model minimizes deviations from actual values and enhances permeability prediction performance.Initially,the computational performances of the LBM,PNM,and FDM are comparatively analyzed on datasets consisting of sphere packings and real rock samples.It is observed that these methods exhibit computational biases in certain permeability ranges.The PSOPIP model is proposed to combine the strengths of each computational approach and mitigate their limitations.The PSO-PIP model consistently produces predictions that are highly congruent with actual permeability values across all prediction intervals,significantly enhancing prediction accuracy.The outcomes of this study provide a new tool and perspective for the comprehensive,rapid,and accurate prediction of permeability in porous media.
文摘In the context of the digital transformation of vocational education,a quality evaluation index system has been constructed.Based on a questionnaire survey conducted among higher vocational colleges and enterprises in Hainan Province,it has been found that the quality of vocational education generally depends on the talent training program and professional construction at the macro level.At the meso level,the teacher level and teaching environment are critical,while at the micro level,the evaluation of talent training quality cannot be underestimated.Strategies for quality improvement in vocational education are proposed from the perspectives of talent training programs,major construction,teacher development,teaching environment,and talent training quality,all under the lens of digital transformation.
文摘BACKGROUND The diagnosis of gastric carcinoma(GC)is essential for improving clinical outcomes.However,the biomarkers currently used for GC screening are not ideal.AIM To explore the diagnostic implications of the neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR),and systemic immune-inflammatory index(SII)for GC.METHODS The baseline data of 133 patients with GC and 134 patients with precancerous gastric conditions admitted between January 2022 and December 2023 were retrospectively analyzed.The information on peripheral blood platelet,neutrophil,and lymphocyte counts in each patient was collected,and the NLR,PLR,and SII levels of both groups were calculated.Additionally,multivariate logistic regression analysis was conducted,and the diagnostic implications of NLR,PLR,and SII in differentiating patients with precancerous gastric conditions,compared with those with GC,were analyzed through receiver operating characteristic(ROC)curves.RESULTS The data indicated that NLR,PLR,and SII had abnormally increased levels in the patients with GC.Gender and body mass index were risk factors for the occurrence of GC.ROC data revealed that the areas under the curve of three patients with precancerous gastric conditions,who were differentiated from those with GC,were 0.824,0.787,and 0.842,respectively.CONCLUSION NLR,PLR,and SII are all abnormally expressed in GC and have diagnostic implications,especially when used as joint indicators,in distinguishing patients with precancerous gastric conditions from those with GC.
基金Supported by Natural Science Foundation of Xinjiang Uygur Autonomous Region,No.2022D01C297.
文摘BACKGROUND Anastomotic leakage(AL)is a serious complication following rectal cancer surgery and is associated with increased recurrence,mortality,extended hospital stays,and delayed chemotherapy.The Onodera prognostic nutritional index(OPNI)and inflammation-related biomarkers,such as the neutrophil-lymphocyte ratio(NLR)and platelet-to-lymphocyte ratio(PLR),have been studied in the context of cancer prognosis,but their combined efficacy in predicting AL remains unclear.AIM To investigate the relationships between AL and these markers and developed a predictive model for AL.METHODS A retrospective cohort study analyzed the outcomes of 434 patients who had undergone surgery for rectal cancer at a tertiary cancer center from 2016 to 2023.The patients were divided into two groups on the basis of the occurrence of AL:One group consisted of patients who experienced AL(n=49),and the other group did not(n=385).The investigation applied logistic regression to develop a risk prediction model utilizing clinical,pathological,and laboratory data.The efficacy of this model was then evaluated through receiver operating characteristic curve analysis.RESULTS In the present study,11.28%of the participants(49 out of 434 participants)suffered from AL.Multivariate analysis revealed that preoperative levels of the OPNI,NLR,and PLR emerged as independent risk factors for AL,with odds ratios of 0.705(95%CI:0.641-0.775,P=0.012),1.628(95%CI:1.221-2.172,P=0.024),and 0.994(95%CI:0.989-0.999,P=0.031),respectively.These findings suggest that these biomarkers could effectively predict AL risk.Furthermore,the proposed predictive model has superior discriminative ability,as demonstrated by an area under the curve of 0.910,a sensitivity of 0.898,and a specificity of 0.826,reflecting its high level of accuracy.CONCLUSION The risk of AL in rectal cancer surgery patients can be effectively predicted by assessing the preoperative levels of serum nutritional biomarkers and inflammatory indicators,emphasizing their importance in the preoperative evaluation process.
基金Supported by the National Key R D Program of China,No.2022YFC2010102Natural Science Foundation of Hunan Province,No.2021JC0003+1 种基金National Natural Science Foundation of China,No.82070812the Sinocare Diabetes Foundation,No.LYF2022039.
文摘ACKGROUND The hemoglobin glycation index(HGI)represents the discrepancy between the glucose management indicator(GMI)based on mean blood glucose levels and laboratory values of glycated hemoglobin(HbA1c).The HGI is a promising indicator for identifying individuals with excessive glycosylation,facilitating personalized evaluation and prediction of diabetic complications.However,the factors influencing the HGI in patients with type 1 diabetes(T1D)remain unclear.Autoimmune destruction of pancreaticβcells is central in T1D pathogenesis,yet insulin resistance can also be a feature of patients with T1D and their coexistence is called“double diabetes”(DD).However,knowledge regarding the relationship between DD features and the HGI in T1D is limited.AIM To assess the association between the HGI and DD features in adults with T1D.METHODS A total of 83 patients with T1D were recruited for this cross-sectional study.Laboratory HbA1c and GMI from continuous glucose monitoring data were collected to calculate the HGI.DD features included a family history of type 2 diabetes,overweight/obesity/central adiposity,hypertension,atherogenic dyslipidemia,an abnormal percentage of body fat(PBF)and/or visceral fat area(VFA)and decreased estimated insulin sensitivity.Skin autofluorescence of advanced glycation end products(SAF-AGEs),diabetic complications,and DD features were assessed,and their association with the HGI was analyzed.RESULTS A discrepancy was observed between HbA1c and GMI among patients with T1D and DD.A higher HGI was associated with an increased number of SAF-AGEs and a higher prevalence of diabetic microangiopathy(P=0.030),particularly retinopathy(P=0.031).Patients with three or more DD features exhibited an eight-fold increased risk of having a high HGI,compared with those without DD features(adjusted odds ratio=8.12;95%confidence interval:1.52-43.47).Specifically,an elevated PBF and/or VFA and decreased estimated insulin sensitivity were associated with high HGI.Regression analysis identified estimated insulin sensitivity and VFA as factors independently associated with HGI.CONCLUSION In patients with T1D,DD features are associated with a higher HGI,which represents a trend toward excessive glycosylation and is associated with a higher prevalence of chronic diabetic complications.
基金Supported by the Scientific Research Project of Hospital Pharmacy of Guangxi Pharmaceutical Association in 2022,No.GXYXH1-202202.
文摘BACKGROUND Gastric cancer(GC)is the fifth most common cancer and the third leading cause of cancer-related deaths in China.Many patients with GC frequently experience symptoms related to the disease,including anorexia,nausea,vomiting,and other discomforts,and often suffer from malnutrition,which in turn negatively affects perioperative safety,prognosis,and the effectiveness of adjuvant therapeutic measures.Consequently,some nutritional indicators such as nutritional risk index(NRI),prognostic nutritional index(PNI),and systemic immune-inflammatorynutritional index(SIINI)can be used as predictors of the prognosis of GC patients.AIM To examine the prognostic significance of PNI,NRI,and SIINI in postoperative patients with GC.METHODS A retrospective analysis was conducted on the clinical data of patients with GC who underwent surgical treatment at the Guangxi Medical University Cancer Hospital between January 2010 and December 2018.The area under the receiver operating characteristic(ROC)curve was assessed using ROC curve analysis,and the optimal cutoff values for NRI,PNI,and SIINI were identified using the You-Review-HTMLden index.Survival analysis was performed using the Kaplan-Meier method.In addition,univariate and multivariate analyses were conducted using the Cox proportional hazards regression model.RESULTS This study included a total of 803 patients.ROC curves were used to evaluate the prognostic ability of NRI,PNI,and SIINI.The results revealed that SIINI had superior predictive accuracy.Survival analysis indicated that patients with GC in the low SIINI group had a significantly better survival rate than those in the high SIINI group(P<0.05).Univariate analysis identified NRI[hazard ratio(HR)=0.68,95%confidence interval(CI):0.52-0.89,P=0.05],PNI(HR=0.60,95%CI:0.46-0.79,P<0.001),and SIINI(HR=2.10,95%CI:1.64-2.69,P<0.001)as prognostic risk factors for patients with GC.However,multifactorial analysis indicated that SIINI was an independent risk factor for the prognosis of patients with GC(HR=1.65,95%CI:1.26-2.16,P<0.001).CONCLUSION Analysis of clinical retrospective data revealed that SIINI is a valuable indicator for predicting the prognosis of patients with GC.Compared with NRI and PNI,SIINI may offer greater application for prognostic assessment.
基金supported by NNSFC grants 42150101,42188105,42304189National Key R&D program of China No.2021YFA-0718600the Pandeng Program of National Space Science Center,Chinese Academy of Sciences.
文摘The Dst index has been commonly used to measure the geomagnetic effectiveness of magnetic storm events for several decades.Based on Burton’s empirical Dst model and the global magneto-hydrodynamic(MHD)simulation of Earth’s magnetosphere,here we proposed a semi-empirical model to forecast the Dst index during geomagnetic storms.In this model,the ring current contribution to the Dst index is derived from Burton’s model,while the contributions from other current systems are obtained from the global MHD simulation.In order to verify the model accuracy,a number of recent magnetic storm events are tested and the simulated Dst index is compared with the observation through the correlation coefficient(CC),prediction efficiency(PE),root mean square error(RMSE)and central root mean square error(CRMSE).The results indicate that,in the context of moderate and intense geomagnetic storm events,the semi-empirical model performs well in global MHD simulations,showing relatively higher CC and PE,and lower RMSE and CRMSE compared to those from the empirical model.Compared with the physics-based ring current models,this model inherits the advantage of fast processing from the empirical model,and easy implementation in a global MHD model of Earth’s magnetosphere.Therefore,it is suitable for the Dst estimation under a context of a global MHD simulation.
