The measurement of the pairing gap is crucial for investigating the physical properties of superconductors or superfluids.We propose a strategy to measure the pairing gap through the dynamical excitations.With the ran...The measurement of the pairing gap is crucial for investigating the physical properties of superconductors or superfluids.We propose a strategy to measure the pairing gap through the dynamical excitations.With the random phase approximation(RPA),we study the dynamical excitations of a two-dimensional attractive Fermi-Hubbard model by calculating its dynamical structure factor.Two distinct collective modes emerge:a Goldstone phonon mode at transferred momentum q=[0,0]and a roton mode at q=[p,p].The roton mode exhibits a sharp molecular peak in the low-energy regime.Notably,the area under the roton molecular peak scales with the square of the pairing gap,which holds even in three-dimensional and spin-orbit coupled(SOC)optical lattices.This finding suggests an experimental approach to measure the pairing gap in lattice systems by analyzing the dynamical structure factor at q=[p,p].展开更多
This editorial critically evaluated the recent study by Wang et al,which systematically investigated the efficacy of perioperative disinfection and isolation measures(including preoperative povidone-iodine disinfectio...This editorial critically evaluated the recent study by Wang et al,which systematically investigated the efficacy of perioperative disinfection and isolation measures(including preoperative povidone-iodine disinfection,intraoperative sterile barrier techniques,and postoperative intensive care)in reducing infection rates.The study further incorporated the surgical site infection risk prediction model(constructed via the least absolute shrinkage and selection operator al-gorithm,integrating patients'baseline characteristics,surgical indicators,and regional antibiotic-resistant bacterial data),and proposed a dynamic prevention and control system termed“disinfection protocols-predictive models–real-time monitoring”.The article highlighted that preoperative risk stratification,intraoperative personalized antibiotic selection,and postoperative multidimensional monitoring(encompassing inflammatory biomarkers,imaging,and microbiological testing)enabled the precise identification of high-risk patients and optimized intervention thresholds.Future research is deemed necessary to validate the synergistic effects of disinfection protocols and predictive models through large-scale multicenter studies,combined with advanced intraoperative rapid microbial detection technologies.This approach aims to establish standardized infection control protocols tailored for precision medicine and regional adaptability.Future research should prioritize validating the synergistic effects of disinfection protocols and predictive models via multi-center studies,while incorporating advanced rapid intraoperative microbial detection technologies to develop standardized infection prevention and control procedures.Such efforts will enhance the implementation of precise and regionally adaptive infection control strategies.展开更多
To accurately reconstruct the tomographic gamma scanning(TGS)transmission measurement image,this study optimized the transmission reconstruction equation based on the actual situation of TGS transmission measurement.U...To accurately reconstruct the tomographic gamma scanning(TGS)transmission measurement image,this study optimized the transmission reconstruction equation based on the actual situation of TGS transmission measurement.Using the transmission reconstruction equation and the Monte Carlo program Geant4,an innovative virtual trajectory length model was constructed.This model integrated the solving process for the trajectory length and detection efficiency within the same model.To mitigate the influence of the angular distribution ofγ-rays emitted by the transmitted source at the detector,the transport processes of numerous particles traversing a virtual nuclear waste barrel with a density of zero were simulated.Consequently,a certain amount of information was captured at each step of particle transport.Simultaneously,the model addressed the nonuniform detection efficiency of the detector end face by considering whether the energy deposition of particles in the detector equaled their initial energy.Two models were established to validate the accuracy and reliability of the virtual trajectory length model.Model 1 was a simplified nuclear waste barrel,whereas Model 2 closely resembled the actual structure of a nuclear waste barrel.The results indicated that the proposed virtual trajectory length model significantly enhanced the precision of the trajectory length determination,substantially increasing the quality of the reconstructed images.For example,the reconstructed images of Model 2 using the“point-to-point”and average trajectory models revealed a signalto-noise ratio increase of 375.0%and 112.7%,respectively.Thus,the virtual trajectory length model proposed in this study holds paramount significance for the precise reconstruction of transmission images.Moreover,it can provide support for the accurate detection of radioactive activity in nuclear waste barrels.展开更多
As important infrastructure for airborne communication platforms,unmanned aerial vehicles(UAVs)are expected to become a key part of 6G wireless networks.Thus,modeling low-and medium-altitude propagation channels has a...As important infrastructure for airborne communication platforms,unmanned aerial vehicles(UAVs)are expected to become a key part of 6G wireless networks.Thus,modeling low-and medium-altitude propagation channels has attracted much attention.Air-to-ground(A2G)propagation channel models vary in different scenarios,requiring accurate models for designing and evaluating UAV communication links.Unlike terrestrial models,A2G channel models lack detailed investigation.Therefore,this paper provides an overview of existing A2G channel measurement campaigns,different types of A2G channel models for various environments,and future research directions for UAV airland channel modeling.This study focuses on the potential of millimeter-wave technology for UAV A2G channel modeling and highlights nonsuburban scenarios requiring consideration in future modeling efforts.展开更多
In modern industrial design trends featuring with integration,miniaturization,and versatility,there is a growing demand on the utilization of microstructural array devices.The measurement of such microstructural array...In modern industrial design trends featuring with integration,miniaturization,and versatility,there is a growing demand on the utilization of microstructural array devices.The measurement of such microstructural array components often encounters challenges due to the reduced scale and complex structures,either by contact or noncontact optical approaches.Among these microstructural arrays,there are still no optical measurement methods for micro corner-cube reflector arrays.To solve this problem,this study introduces a method for effectively eliminating coherent noise and achieving surface profile reconstruction in interference measurements of microstructural arrays.The proposed denoising method allows the calibration and inverse solving of system errors in the frequency domain by employing standard components with known surface types.This enables the effective compensation of the complex amplitude of non-sample coherent light within the interferometer optical path.The proposed surface reconstruction method enables the profile calculation within the situation that there is complex multi-reflection during the propagation of rays in microstructural arrays.Based on the measurement results,two novel metrics are defined to estimate diffraction errors at array junctions and comprehensive errors across multiple array elements,offering insights into other types of microstructure devices.This research not only addresses challenges of the coherent noise and multi-reflection,but also makes a breakthrough for quantitively optical interference measurement of microstructural array devices.展开更多
Chikungunya is a mosquito-borne viral infection caused by the chikungunya virus(CHIKV).It is characterized by acute onset of high fever,severe polyarthralgia,myalgia,headache,and maculopapular rash.The virus is rapidl...Chikungunya is a mosquito-borne viral infection caused by the chikungunya virus(CHIKV).It is characterized by acute onset of high fever,severe polyarthralgia,myalgia,headache,and maculopapular rash.The virus is rapidly spreading and may establish in new regions where competent mosquito vectors are present.This research analyzes the regulatory dynamics of a stochastic differential equation(SDE)model describing the transmission of the CHIKV,incorporating seasonal variations,immunization efforts,and environmentalffuctuations modeled through Poisson random measure noise under demographic heterogeneity.The model guarantees the existence of a global positive solution and demonstrates periodic dynamics driven by environmental factors.A key contribution of this study is the formulation of a stochastic threshold parameter,R0L,which characterizes the conditions for disease persistence or extinction under random environmental inffuences.Although our analysis highlights age-speciffc heterogeneities to illustrate differential transmission risks,the framework is general and can incorporate other vulnerable demographic groups,ensuring broader applicability of the results.Using the Monte Carlo Markov Chain(MCMC)method,we estimate R0L=1.4978(95%C-I:1.4968–1.5823)based on CHIKV data from Florida,USA,spanning 2005 to 2017,suggesting that the outbreak remains active and requires targeted control strategies.The effectiveness of immunization,screening,and treatment strategies varies depending on the prioritized demographic groups,due to substantial differences in CHIKV incidence across age categories in the USA.Numerical simulations were conducted using the truncated Euler–Maruyama method to robustly capture the stochastic dynamics of CHIKV transmission with Poissondriven jumps.Employing an iterative approach and assuming mild convexity conditions,we formulated and solved a parameterized near-optimality problem using the Ekeland variational principle.Ourffndings indicate that vaccination campaigns are signiffcantly more effective when focused on vulnerable adults over the age of 66,as well as individuals aged 21 to 25.Furthermore,enhancements in vaccine effcacy,diagnostic screening,and treatment protocols all contribute substantially to minimizing infection rates compared to current standard approaches.These insights support the development of targeted,age-speciffc public health interventions that can signiffcantly improve the management and control of future CHIKV outbreaks.展开更多
The equivalent source(ES)method in the spherical coordinate system has been widely applied to processing,reduction,field modeling,and geophysical and geological interpretation of satellite magnetic anomaly data.Howeve...The equivalent source(ES)method in the spherical coordinate system has been widely applied to processing,reduction,field modeling,and geophysical and geological interpretation of satellite magnetic anomaly data.However,the inversion for the ES model suffers from nonuniqueness and instability,which remain unresolved.To mitigate these issues,we introduce both the minimum and flattest models into the model objective function as an alternative regularization approach in the spherical ES method.We first present the methods,then analyze the accuracy of forward calculation and test the proposed ES method in this study by using synthetic data.The experimental results from simulation data indicate that our proposed regularization effectively suppresses the Backus effect and mitigates inversion instability in the low-latitude region.Finally,we apply the proposed method to magnetic anomaly data from China Seismo-Electromagnetic Satellite-1(CSES-1)and Macao Science Satellite-1(MSS-1)magnetic measurements over Africa by constructing an ES model of the large-scale lithospheric magnetic field.Compared with existing global lithospheric magnetic field models,our ES model demonstrates good consistency at high altitudes and predicts more stable fields at low altitudes.Furthermore,we derive the reduction to the pole(RTP)magnetic anomaly fields and the apparent susceptibility contrast distribution based on the ES model.The latter correlates well with the regional tectonic framework in Africa and surroundings.展开更多
Ensuring highway safety relies heavily on pavement friction resistance.To enable network-level pavement skid resistance monitoring and management,this study proposes a non-contact three-dimensional laser surface testi...Ensuring highway safety relies heavily on pavement friction resistance.To enable network-level pavement skid resistance monitoring and management,this study proposes a non-contact three-dimensional laser surface testing method to obtain detailed aggregate surface data.The existing contact-based skid resistance measurement methods suffer from poor reproducibility and repeatability,hindering their application for network-level management.In this research,traditional multiple linear regression and four machine learning methods,support vector machine(SVM),random forest(RF),gradient boosting decision tree(GBDT),and convolutional neural network(CNN),are utilized to evaluate and predict pavement frictional performance.To assess the proposed methods,data from 45 pavement sites in Oklahoma,including 6 major preventive maintenance(PM)treatments and 7 typical types of aggregates,are collected.Parallel data acquisition is conducted at highway speeds using a grip tester and a high-speed texture profiler to measure pavement skid resistance and surface macro-texture,respectively.Aggregate properties are captured in 3D using a portable ultra-high-resolution 3D laser imaging scanner,leading to the calculation of four types of 3D aggregate parameters characterizing the micro-texture of aggregate surfaces.The relationship between pavement surface friction and texture is explored using machine learning models.The results reveal that the random forest and gradient boosting decision tree models exhibit the highest accuracy,SVM and CNN perform moderately,while the traditional linear regression method fares the worst.By assessing the importance of the 38 parameter variables,the most critical 21 variables were selected for model development.Test results demonstrate that the GBDT model exhibits the best predictive performance,with an explanatory capability of 87.4%for road friction performance.The findings demonstrate the feasibility of replacing contact-based pavement friction evaluation with non-contact texture measurements,offering promising prospects for a network-level pavement skid resistance monitoring and management system.展开更多
Wind turbines are continuously exposed to harsh environmental and operational conditions throughout their lifetime,leading to the gradual degradation of their components.If left unaddressed,these degraded components c...Wind turbines are continuously exposed to harsh environmental and operational conditions throughout their lifetime,leading to the gradual degradation of their components.If left unaddressed,these degraded components can adversely affect turbine performance and significantly increase the likelihood of failure.As degradation progresses,the risk of failure escalates,making it essential to implement appropriate risk control measures.One effective risk control method involves performing inspection and monitoring activities that provide valuable insights into the condition of the structure,enabling the formulation of appropriate maintenance strategies based on accurate assessments.Supervisory Control and Data Acquisition(SCADA)systems offer low-resolution condition monitoring data that can be used for fault detection,diagnosis,quantification,prognosis,and maintenance planning.One commonly used method involves predicting power generation using SCADA data and comparing it against measured power generation.Significant discrepancies between predicted and measured values can indicate suboptimal operation,natural aging,or unnatural faults.Various predictive models,including parametric and non-parametric(statistical)approaches,have been proposed for estimating power generation.However,the imperfect nature of these models introduces uncertainties in the predicted power output.Additionally,SCADA monitoring data is prone to uncertainties arising from various sources.The presence of uncertainties from these two sources-imperfect predictive models and imperfect SCADA data-introduces uncertainty in the predicted power generation.This uncertainty complicates the process of determining whether discrepancies between measured and predicted values are significant enough to warrant maintenance actions.Depending on the nature of uncertainty-aleatory,arising from inherent randomness,or epistemic,stemming from incomplete knowledge or limited data-different analytical approaches,like Probabilistic and Possibilistic,can be applied for effective management.Both,Probabilistic and Possibilistic,Approaches offer distinct advantages and limitations.The Possibilistic Approach,rooted in fuzzy set theory,is particularly well suited for addressing epistemic uncertainties,especially those caused by imprecision or sparse statistical information.This makes it especially relevant for applications such as wind turbines,where it is often challenging to construct accurate probability distribution functions for environmental parameters due to limited sensor data from hard-to-access locations.This research focuses on developing a methodology for identifying suboptimal operation in wind turbines by comparing Grid Produced Power(Measured Produced Power)with Predicted Produced Power.To achieve this,the paper introduces a Possibilistic Approach for power prediction that accounts for uncertainties stemming from both model imperfections and measurement errors in SCADA data.The methodology combines machine learning models,used to establish predictive relationships between environmental inputs and power output,with a Possibilistic Framework that represents uncertainty through possibility distribution functions based on fuzzy logic and interval analysis.A real-world case study using operational SCADA data demonstrates the approach,with XGBoost selected as the final predictive model due to its strong accuracy and computational efficiency.展开更多
Standardization is necessary for the early industrialization of the new materials and technology.It is achieved by having agreed practices for the measurement of properties and other characteristics.The promising use ...Standardization is necessary for the early industrialization of the new materials and technology.It is achieved by having agreed practices for the measurement of properties and other characteristics.The promising use of graphene-based materials in fields like electronics,energy,and composites has resulted in standards for their nomenclature,the measurement of key characteristics,and their specification,etc.Among these,standards for measuring the key characteristics are crucial.The critical parameters are the number of layers,the type and concentration of defects and functional groups,elemental composition,sheet resistance,and carrier mobility.Standards for characterizing these have been analyzed by the International Organization for Standardization Technical Committee in ISO/TC229 and the International Electrotechnical Commission Technical Committee in IEC/TC113.These give details of applicable or preferred samples,the fundamental principles of the techniques,specific precautions,and points for attention in the relevant standards.The pivotal role of the ISO/TC229 and IEC/TC113 standards is considered and challenges and future trends are outlined.展开更多
We analyzed accident factors in a 2020 ship collision case that occurred off Kii Oshima Island using the SHELL model analysis and examined corresponding collision prevention measures.The SHELL model analysis is a fram...We analyzed accident factors in a 2020 ship collision case that occurred off Kii Oshima Island using the SHELL model analysis and examined corresponding collision prevention measures.The SHELL model analysis is a framework for identifying accident factors related to human abilities and characteristics,hardware,software,and the environment.Beyond assessing the accident factors in each element,we also examined the interrelationship between humans and each element.This study highlights the importance of(1)training to enhance situational awareness,(2)improving decision-making skills,and(3)establishing structured decision-making procedures to prevent maritime collision accidents.Additionally,we considered safety measures through(4)hardware enhancements and(5)environmental measures.Furthermore,to prevent accidents,implementing measures grounded in(6)predictions is deemed effective.This study identified accident factors through prediction alongside the SHELL model analysis and proposed countermeasures based on the findings.By applying these predictions,more countermeasures can be derived,which,when combined strategically,can significantly aid in preventing maritime collision accidents.展开更多
The phase equilibria relationship of the system RbCl-PEG6000-H2O were investigated at temperatures of 288.2,298.2,and 308.2 K,the compositions of solid-liquid equilibria(SLE)and liquid-liquid equilibria(LLE)were deter...The phase equilibria relationship of the system RbCl-PEG6000-H2O were investigated at temperatures of 288.2,298.2,and 308.2 K,the compositions of solid-liquid equilibria(SLE)and liquid-liquid equilibria(LLE)were determined.The complete phase diagrams,binodal curve diagrams,and tie-line diagrams were all plotted.Results show that both solid-liquid equilibria and liquid-liquid equilibria relationships at each studied temperature.The complete phase diagrams at 288.2 K,298.2 K and 308.2 K consist of six phase regions:unsaturated liquid region(L),two saturated solutions with one solid phase of RbCl(L_S),one saturated liquid phase with two solid phases of PEG6000 and RbCl(2S+L),an aqueous two-phase region(2L),and a region with two liquids and one solid phase of RbCl(2L_S).With the increase in temperature,the layering ability of the aqueous two-phase system increases,and both regions(2L)and(2L_S)increase.The binodal curves were fitted using the nonlinear equations proposed by Mistry,Hu,and Jayapal.Additionally,the tie-line data were correlated with the Othmer-Tobias,Bancroft,Hand,and Bachman equations.The liquid-liquid equilibria at 288.2 K,298.2 K and 308.2 K were calculated using the NRTL model.The findings confirm that the experimental and calculated values are in close agreement,demonstrating the model’s effectiveness in representing the system’s behavior.展开更多
Despite broad consensus on the importance of enterprise digital transformation,significant discrepancies persist regarding its actual effects.This divergence stems primarily from two key measurement challenges:(1)a la...Despite broad consensus on the importance of enterprise digital transformation,significant discrepancies persist regarding its actual effects.This divergence stems primarily from two key measurement challenges:(1)a lack of clear and consistent definitions of enterprise digital transformation,and(2)a lack of rigorous and accurate measurement methodologies.These shortcomings lead to research findings that are incomparable,difficult to replicate,and often conflicting.To effectively address the aforementioned challenges,this paper employs machine learning and large language models(LLMs)to construct a novel set of indicators for enterprise digital transformation.The work begins by manually annotating sentences from annual reports of listed companies in China from 2006 to 2020.These labeled sentences are then used to train and fine-tune several machine learning models,including LLMs.The ERNIE model,demonstrating the best classification performance among the models tested,is selected as the sentence classifier to predict sentence labels across the full text of the annual reports,ultimately constructing the enterprise digital transformation metrics.Both theoretical analysis and multiple data cross-validations demonstrate that the metrics developed in this paper are more accurate than existing approaches.Based on these metrics,the paper empirically examines the impact of enterprise digital transformation on financial performance.Our findings reveal three key points:(1)enterprise digital transformation significantly enhances financial performance,with big data,AI,mobile internet,cloud computing,and the Internet of Things(IoT)all playing a significant role;however,blockchain technology does not show a significant effect;(2)the significant positive effect of digital transformation on financial performance is primarily observed in firms with weaker initial financial performance;and(3)enterprise digital transformation improves financial performance mainly through enhancing efficiency and reducing costs.This research has practical implications for promoting enterprise digital transformation and fostering high-quality economic development.展开更多
Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asy...In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.展开更多
AIM:To evaluate the differences and consistency of vault measurements obtained by Scheimpflug tomography(Pentacam),anterior segment optical coherence tomography(AS-OCT,CASIA II),and ultrasound biomicroscopy(UBM)follow...AIM:To evaluate the differences and consistency of vault measurements obtained by Scheimpflug tomography(Pentacam),anterior segment optical coherence tomography(AS-OCT,CASIA II),and ultrasound biomicroscopy(UBM)following implantable collamer lens(ICL)V4c implantation.METHODS:Vault measurements were acquired using three modalities:Pentacam,CASIA II AS-OCT,and UBM.Repeated-measures analysis of variance was used to compare the vault values obtained by the three devices.The correlation and consistency of measurements among the three instruments were assessed using the Pearson correlation coefficient,intraclass correlation coefficient(ICC),and Bland-Altman plots.RESULTS:This retrospective study enrolled 210 myopic eyes of 210 patients(158 women and 52 men)who underwent ICL implantation:108 eyes had a myopic ICL V4c implanted,and 102 eyes had a toric ICL V4c implanted.The mean vault values measured by Pentacam,CASIA II,and UBM were 452.64±204.20μm,538.57±203.54μm,and 560.95±227.54μm,respectively,with statistically significant differences among the three groups(P<0.05).Pearson correlation analysis showed strong positive correlations between vault values measured by different instruments(all P<0.001).ICC results indicated good consistency among the three measurement modalities(all P<0.001).Stratified analysis revealed that when the vault value was≤250μm,the correlation and consistency of measurements across the three instruments were lower than those in the medium and high vault subgroups.CONCLUSION:Vault values measured by Pentacam are lower than those obtained by CASIA II and UBM,with UBM yielding the highest mean vault values.Measurements from the three instruments are not interchangeable but can serve as mutual references due to their significant correlation and good overall consistency.Pentacam and CASIA II demonstrate the highest consistency in vault measurement.Notably,when the vault value is≤250μm,the consistency between Pentacam and the other two instruments decreases significantly.展开更多
In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation ...In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade.展开更多
In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and cha...In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.展开更多
Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important a...Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.展开更多
The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpec...The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpected non-physical phase points in practice.We conduct an analysis on the phase-space characteristics and the fixed-point theory underlying the k-ε turbulence model,and employ the classical Kolmogorov flow as a framework,leveraging its direct numerical simulation(DNS)data to construct a one-dimensional(1D)system under periodic/fixed boundary conditions.The RANS results demonstrate that under periodic boundary conditions,the k-ε model exhibits only a unique trivial fixed point,with asymptotes capturing the phase portraits.The stability of this trivial fixed point is determined by a mathematically derived stability phase diagram,indicating the fact that the k-ε model will never converge to correct values under periodic conditions.In contrast,under fixed boundary conditions,the model can yield a stable non-trivial fixed point.The evolutionary mechanisms and their relationship with boundary condition settings systematically explain the inherent limitations of the k-ε model,i.e.,its deficiency in computing the flow field under periodic boundary conditions and sensitivity to boundary-value specifications under fixed boundary conditions.These conclusions are finally validated with the open-source code OpenFOAM.展开更多
基金supported by the National Natural Science Foundation of China[Grant Nos.U23A2073(P.Z.)and 11547034(H.Z.)].
文摘The measurement of the pairing gap is crucial for investigating the physical properties of superconductors or superfluids.We propose a strategy to measure the pairing gap through the dynamical excitations.With the random phase approximation(RPA),we study the dynamical excitations of a two-dimensional attractive Fermi-Hubbard model by calculating its dynamical structure factor.Two distinct collective modes emerge:a Goldstone phonon mode at transferred momentum q=[0,0]and a roton mode at q=[p,p].The roton mode exhibits a sharp molecular peak in the low-energy regime.Notably,the area under the roton molecular peak scales with the square of the pairing gap,which holds even in three-dimensional and spin-orbit coupled(SOC)optical lattices.This finding suggests an experimental approach to measure the pairing gap in lattice systems by analyzing the dynamical structure factor at q=[p,p].
文摘This editorial critically evaluated the recent study by Wang et al,which systematically investigated the efficacy of perioperative disinfection and isolation measures(including preoperative povidone-iodine disinfection,intraoperative sterile barrier techniques,and postoperative intensive care)in reducing infection rates.The study further incorporated the surgical site infection risk prediction model(constructed via the least absolute shrinkage and selection operator al-gorithm,integrating patients'baseline characteristics,surgical indicators,and regional antibiotic-resistant bacterial data),and proposed a dynamic prevention and control system termed“disinfection protocols-predictive models–real-time monitoring”.The article highlighted that preoperative risk stratification,intraoperative personalized antibiotic selection,and postoperative multidimensional monitoring(encompassing inflammatory biomarkers,imaging,and microbiological testing)enabled the precise identification of high-risk patients and optimized intervention thresholds.Future research is deemed necessary to validate the synergistic effects of disinfection protocols and predictive models through large-scale multicenter studies,combined with advanced intraoperative rapid microbial detection technologies.This approach aims to establish standardized infection control protocols tailored for precision medicine and regional adaptability.Future research should prioritize validating the synergistic effects of disinfection protocols and predictive models via multi-center studies,while incorporating advanced rapid intraoperative microbial detection technologies to develop standardized infection prevention and control procedures.Such efforts will enhance the implementation of precise and regionally adaptive infection control strategies.
基金supported by The Youth Science Foundation of Sichuan Province(Nos.2022NSFSC1230,2022NSFSC1231,and 23NSFSC5321)the Science and Technology Innovation Seedling Project of Sichuan Province(No.MZGC20230080)+2 种基金the General project of national Natural Science Foundation of China(No.12075039)the Youth Science Foundation of China(No.12105030)the Key project of the National Natural Science Foundation of China(No.U19A2086)。
文摘To accurately reconstruct the tomographic gamma scanning(TGS)transmission measurement image,this study optimized the transmission reconstruction equation based on the actual situation of TGS transmission measurement.Using the transmission reconstruction equation and the Monte Carlo program Geant4,an innovative virtual trajectory length model was constructed.This model integrated the solving process for the trajectory length and detection efficiency within the same model.To mitigate the influence of the angular distribution ofγ-rays emitted by the transmitted source at the detector,the transport processes of numerous particles traversing a virtual nuclear waste barrel with a density of zero were simulated.Consequently,a certain amount of information was captured at each step of particle transport.Simultaneously,the model addressed the nonuniform detection efficiency of the detector end face by considering whether the energy deposition of particles in the detector equaled their initial energy.Two models were established to validate the accuracy and reliability of the virtual trajectory length model.Model 1 was a simplified nuclear waste barrel,whereas Model 2 closely resembled the actual structure of a nuclear waste barrel.The results indicated that the proposed virtual trajectory length model significantly enhanced the precision of the trajectory length determination,substantially increasing the quality of the reconstructed images.For example,the reconstructed images of Model 2 using the“point-to-point”and average trajectory models revealed a signalto-noise ratio increase of 375.0%and 112.7%,respectively.Thus,the virtual trajectory length model proposed in this study holds paramount significance for the precise reconstruction of transmission images.Moreover,it can provide support for the accurate detection of radioactive activity in nuclear waste barrels.
基金supported by the National Natural Science Foundation of China under Grant No.42176190Fundamental Research Funds for the Central Universities,CHD under Grant Nos.300102243401 and 300102244203Research Funds for the Interdisciplinary Projects,CHU under Grant Nos.300104240912 and 300104240922。
文摘As important infrastructure for airborne communication platforms,unmanned aerial vehicles(UAVs)are expected to become a key part of 6G wireless networks.Thus,modeling low-and medium-altitude propagation channels has attracted much attention.Air-to-ground(A2G)propagation channel models vary in different scenarios,requiring accurate models for designing and evaluating UAV communication links.Unlike terrestrial models,A2G channel models lack detailed investigation.Therefore,this paper provides an overview of existing A2G channel measurement campaigns,different types of A2G channel models for various environments,and future research directions for UAV airland channel modeling.This study focuses on the potential of millimeter-wave technology for UAV A2G channel modeling and highlights nonsuburban scenarios requiring consideration in future modeling efforts.
基金Supported by National Natural Science Foundation of China(Grant Nos.52375414,52075100)Shanghai Science and Technology Committee Innovation Grant of China(Grant No.23ZR1404200).
文摘In modern industrial design trends featuring with integration,miniaturization,and versatility,there is a growing demand on the utilization of microstructural array devices.The measurement of such microstructural array components often encounters challenges due to the reduced scale and complex structures,either by contact or noncontact optical approaches.Among these microstructural arrays,there are still no optical measurement methods for micro corner-cube reflector arrays.To solve this problem,this study introduces a method for effectively eliminating coherent noise and achieving surface profile reconstruction in interference measurements of microstructural arrays.The proposed denoising method allows the calibration and inverse solving of system errors in the frequency domain by employing standard components with known surface types.This enables the effective compensation of the complex amplitude of non-sample coherent light within the interferometer optical path.The proposed surface reconstruction method enables the profile calculation within the situation that there is complex multi-reflection during the propagation of rays in microstructural arrays.Based on the measurement results,two novel metrics are defined to estimate diffraction errors at array junctions and comprehensive errors across multiple array elements,offering insights into other types of microstructure devices.This research not only addresses challenges of the coherent noise and multi-reflection,but also makes a breakthrough for quantitively optical interference measurement of microstructural array devices.
基金Ongoing Research Funding program(ORF-2025-1404),King Saud University,Riyadh,Saudi Arabia。
文摘Chikungunya is a mosquito-borne viral infection caused by the chikungunya virus(CHIKV).It is characterized by acute onset of high fever,severe polyarthralgia,myalgia,headache,and maculopapular rash.The virus is rapidly spreading and may establish in new regions where competent mosquito vectors are present.This research analyzes the regulatory dynamics of a stochastic differential equation(SDE)model describing the transmission of the CHIKV,incorporating seasonal variations,immunization efforts,and environmentalffuctuations modeled through Poisson random measure noise under demographic heterogeneity.The model guarantees the existence of a global positive solution and demonstrates periodic dynamics driven by environmental factors.A key contribution of this study is the formulation of a stochastic threshold parameter,R0L,which characterizes the conditions for disease persistence or extinction under random environmental inffuences.Although our analysis highlights age-speciffc heterogeneities to illustrate differential transmission risks,the framework is general and can incorporate other vulnerable demographic groups,ensuring broader applicability of the results.Using the Monte Carlo Markov Chain(MCMC)method,we estimate R0L=1.4978(95%C-I:1.4968–1.5823)based on CHIKV data from Florida,USA,spanning 2005 to 2017,suggesting that the outbreak remains active and requires targeted control strategies.The effectiveness of immunization,screening,and treatment strategies varies depending on the prioritized demographic groups,due to substantial differences in CHIKV incidence across age categories in the USA.Numerical simulations were conducted using the truncated Euler–Maruyama method to robustly capture the stochastic dynamics of CHIKV transmission with Poissondriven jumps.Employing an iterative approach and assuming mild convexity conditions,we formulated and solved a parameterized near-optimality problem using the Ekeland variational principle.Ourffndings indicate that vaccination campaigns are signiffcantly more effective when focused on vulnerable adults over the age of 66,as well as individuals aged 21 to 25.Furthermore,enhancements in vaccine effcacy,diagnostic screening,and treatment protocols all contribute substantially to minimizing infection rates compared to current standard approaches.These insights support the development of targeted,age-speciffc public health interventions that can signiffcantly improve the management and control of future CHIKV outbreaks.
基金supported by the National Natural Science Foundation of China(Grant Nos.42250103 and 42174090)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB2023ZR02)the MOST Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources(Grant No.MSFGPMR2022-4).
文摘The equivalent source(ES)method in the spherical coordinate system has been widely applied to processing,reduction,field modeling,and geophysical and geological interpretation of satellite magnetic anomaly data.However,the inversion for the ES model suffers from nonuniqueness and instability,which remain unresolved.To mitigate these issues,we introduce both the minimum and flattest models into the model objective function as an alternative regularization approach in the spherical ES method.We first present the methods,then analyze the accuracy of forward calculation and test the proposed ES method in this study by using synthetic data.The experimental results from simulation data indicate that our proposed regularization effectively suppresses the Backus effect and mitigates inversion instability in the low-latitude region.Finally,we apply the proposed method to magnetic anomaly data from China Seismo-Electromagnetic Satellite-1(CSES-1)and Macao Science Satellite-1(MSS-1)magnetic measurements over Africa by constructing an ES model of the large-scale lithospheric magnetic field.Compared with existing global lithospheric magnetic field models,our ES model demonstrates good consistency at high altitudes and predicts more stable fields at low altitudes.Furthermore,we derive the reduction to the pole(RTP)magnetic anomaly fields and the apparent susceptibility contrast distribution based on the ES model.The latter correlates well with the regional tectonic framework in Africa and surroundings.
基金study is under the research project“development of aggregate characteristics-based preventive maintenance treatments using 3D laser imaging and aggregate imaging technology for optimized skid resistance of pavements”sponsored by the Oklahoma Department of Transportation(ODOT SPR 2275).
文摘Ensuring highway safety relies heavily on pavement friction resistance.To enable network-level pavement skid resistance monitoring and management,this study proposes a non-contact three-dimensional laser surface testing method to obtain detailed aggregate surface data.The existing contact-based skid resistance measurement methods suffer from poor reproducibility and repeatability,hindering their application for network-level management.In this research,traditional multiple linear regression and four machine learning methods,support vector machine(SVM),random forest(RF),gradient boosting decision tree(GBDT),and convolutional neural network(CNN),are utilized to evaluate and predict pavement frictional performance.To assess the proposed methods,data from 45 pavement sites in Oklahoma,including 6 major preventive maintenance(PM)treatments and 7 typical types of aggregates,are collected.Parallel data acquisition is conducted at highway speeds using a grip tester and a high-speed texture profiler to measure pavement skid resistance and surface macro-texture,respectively.Aggregate properties are captured in 3D using a portable ultra-high-resolution 3D laser imaging scanner,leading to the calculation of four types of 3D aggregate parameters characterizing the micro-texture of aggregate surfaces.The relationship between pavement surface friction and texture is explored using machine learning models.The results reveal that the random forest and gradient boosting decision tree models exhibit the highest accuracy,SVM and CNN perform moderately,while the traditional linear regression method fares the worst.By assessing the importance of the 38 parameter variables,the most critical 21 variables were selected for model development.Test results demonstrate that the GBDT model exhibits the best predictive performance,with an explanatory capability of 87.4%for road friction performance.The findings demonstrate the feasibility of replacing contact-based pavement friction evaluation with non-contact texture measurements,offering promising prospects for a network-level pavement skid resistance monitoring and management system.
文摘Wind turbines are continuously exposed to harsh environmental and operational conditions throughout their lifetime,leading to the gradual degradation of their components.If left unaddressed,these degraded components can adversely affect turbine performance and significantly increase the likelihood of failure.As degradation progresses,the risk of failure escalates,making it essential to implement appropriate risk control measures.One effective risk control method involves performing inspection and monitoring activities that provide valuable insights into the condition of the structure,enabling the formulation of appropriate maintenance strategies based on accurate assessments.Supervisory Control and Data Acquisition(SCADA)systems offer low-resolution condition monitoring data that can be used for fault detection,diagnosis,quantification,prognosis,and maintenance planning.One commonly used method involves predicting power generation using SCADA data and comparing it against measured power generation.Significant discrepancies between predicted and measured values can indicate suboptimal operation,natural aging,or unnatural faults.Various predictive models,including parametric and non-parametric(statistical)approaches,have been proposed for estimating power generation.However,the imperfect nature of these models introduces uncertainties in the predicted power output.Additionally,SCADA monitoring data is prone to uncertainties arising from various sources.The presence of uncertainties from these two sources-imperfect predictive models and imperfect SCADA data-introduces uncertainty in the predicted power generation.This uncertainty complicates the process of determining whether discrepancies between measured and predicted values are significant enough to warrant maintenance actions.Depending on the nature of uncertainty-aleatory,arising from inherent randomness,or epistemic,stemming from incomplete knowledge or limited data-different analytical approaches,like Probabilistic and Possibilistic,can be applied for effective management.Both,Probabilistic and Possibilistic,Approaches offer distinct advantages and limitations.The Possibilistic Approach,rooted in fuzzy set theory,is particularly well suited for addressing epistemic uncertainties,especially those caused by imprecision or sparse statistical information.This makes it especially relevant for applications such as wind turbines,where it is often challenging to construct accurate probability distribution functions for environmental parameters due to limited sensor data from hard-to-access locations.This research focuses on developing a methodology for identifying suboptimal operation in wind turbines by comparing Grid Produced Power(Measured Produced Power)with Predicted Produced Power.To achieve this,the paper introduces a Possibilistic Approach for power prediction that accounts for uncertainties stemming from both model imperfections and measurement errors in SCADA data.The methodology combines machine learning models,used to establish predictive relationships between environmental inputs and power output,with a Possibilistic Framework that represents uncertainty through possibility distribution functions based on fuzzy logic and interval analysis.A real-world case study using operational SCADA data demonstrates the approach,with XGBoost selected as the final predictive model due to its strong accuracy and computational efficiency.
文摘Standardization is necessary for the early industrialization of the new materials and technology.It is achieved by having agreed practices for the measurement of properties and other characteristics.The promising use of graphene-based materials in fields like electronics,energy,and composites has resulted in standards for their nomenclature,the measurement of key characteristics,and their specification,etc.Among these,standards for measuring the key characteristics are crucial.The critical parameters are the number of layers,the type and concentration of defects and functional groups,elemental composition,sheet resistance,and carrier mobility.Standards for characterizing these have been analyzed by the International Organization for Standardization Technical Committee in ISO/TC229 and the International Electrotechnical Commission Technical Committee in IEC/TC113.These give details of applicable or preferred samples,the fundamental principles of the techniques,specific precautions,and points for attention in the relevant standards.The pivotal role of the ISO/TC229 and IEC/TC113 standards is considered and challenges and future trends are outlined.
文摘We analyzed accident factors in a 2020 ship collision case that occurred off Kii Oshima Island using the SHELL model analysis and examined corresponding collision prevention measures.The SHELL model analysis is a framework for identifying accident factors related to human abilities and characteristics,hardware,software,and the environment.Beyond assessing the accident factors in each element,we also examined the interrelationship between humans and each element.This study highlights the importance of(1)training to enhance situational awareness,(2)improving decision-making skills,and(3)establishing structured decision-making procedures to prevent maritime collision accidents.Additionally,we considered safety measures through(4)hardware enhancements and(5)environmental measures.Furthermore,to prevent accidents,implementing measures grounded in(6)predictions is deemed effective.This study identified accident factors through prediction alongside the SHELL model analysis and proposed countermeasures based on the findings.By applying these predictions,more countermeasures can be derived,which,when combined strategically,can significantly aid in preventing maritime collision accidents.
基金supported by the National Natural Science Foundation of China(U1507111).
文摘The phase equilibria relationship of the system RbCl-PEG6000-H2O were investigated at temperatures of 288.2,298.2,and 308.2 K,the compositions of solid-liquid equilibria(SLE)and liquid-liquid equilibria(LLE)were determined.The complete phase diagrams,binodal curve diagrams,and tie-line diagrams were all plotted.Results show that both solid-liquid equilibria and liquid-liquid equilibria relationships at each studied temperature.The complete phase diagrams at 288.2 K,298.2 K and 308.2 K consist of six phase regions:unsaturated liquid region(L),two saturated solutions with one solid phase of RbCl(L_S),one saturated liquid phase with two solid phases of PEG6000 and RbCl(2S+L),an aqueous two-phase region(2L),and a region with two liquids and one solid phase of RbCl(2L_S).With the increase in temperature,the layering ability of the aqueous two-phase system increases,and both regions(2L)and(2L_S)increase.The binodal curves were fitted using the nonlinear equations proposed by Mistry,Hu,and Jayapal.Additionally,the tie-line data were correlated with the Othmer-Tobias,Bancroft,Hand,and Bachman equations.The liquid-liquid equilibria at 288.2 K,298.2 K and 308.2 K were calculated using the NRTL model.The findings confirm that the experimental and calculated values are in close agreement,demonstrating the model’s effectiveness in representing the system’s behavior.
基金supported by the Fundamental Research Funds for the Central Universitiesfollowing projects:the Major Project of the National Social Science Fund of China(NSSFC)“Research on the Synergistic Mechanisms of Innovation and Governance for High-Quality Development of the Digital Economy”(Grant No.22&ZD070)+1 种基金the Youth Project of the National Natural Science Foundation of China(NSFC)“Research on Risk-Taking of Zombie Enterprises from a Government-Enterprise Interaction Perspective:Tendency,Behavioral Patterns,and Economic Consequences”(Grant No.72002213)the General Program of the National Natural Science Foundation of China(NSFC)“Reshaping Enterprise Nature,Boundaries,and Internal Organization in the Digital Economy”(Grant No.72273144).
文摘Despite broad consensus on the importance of enterprise digital transformation,significant discrepancies persist regarding its actual effects.This divergence stems primarily from two key measurement challenges:(1)a lack of clear and consistent definitions of enterprise digital transformation,and(2)a lack of rigorous and accurate measurement methodologies.These shortcomings lead to research findings that are incomparable,difficult to replicate,and often conflicting.To effectively address the aforementioned challenges,this paper employs machine learning and large language models(LLMs)to construct a novel set of indicators for enterprise digital transformation.The work begins by manually annotating sentences from annual reports of listed companies in China from 2006 to 2020.These labeled sentences are then used to train and fine-tune several machine learning models,including LLMs.The ERNIE model,demonstrating the best classification performance among the models tested,is selected as the sentence classifier to predict sentence labels across the full text of the annual reports,ultimately constructing the enterprise digital transformation metrics.Both theoretical analysis and multiple data cross-validations demonstrate that the metrics developed in this paper are more accurate than existing approaches.Based on these metrics,the paper empirically examines the impact of enterprise digital transformation on financial performance.Our findings reveal three key points:(1)enterprise digital transformation significantly enhances financial performance,with big data,AI,mobile internet,cloud computing,and the Internet of Things(IoT)all playing a significant role;however,blockchain technology does not show a significant effect;(2)the significant positive effect of digital transformation on financial performance is primarily observed in firms with weaker initial financial performance;and(3)enterprise digital transformation improves financial performance mainly through enhancing efficiency and reducing costs.This research has practical implications for promoting enterprise digital transformation and fostering high-quality economic development.
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
基金Supported by the National Natural Science Foundation of China(12261018)Universities Key Laboratory of Mathematical Modeling and Data Mining in Guizhou Province(2023013)。
文摘In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.
基金Supported by the National Natural Science Foundation of China(No.82171095)the Project of Shanghai Science and Technology(No.23XD1400500)the Research Fund of Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital(No.24QNPY049).
文摘AIM:To evaluate the differences and consistency of vault measurements obtained by Scheimpflug tomography(Pentacam),anterior segment optical coherence tomography(AS-OCT,CASIA II),and ultrasound biomicroscopy(UBM)following implantable collamer lens(ICL)V4c implantation.METHODS:Vault measurements were acquired using three modalities:Pentacam,CASIA II AS-OCT,and UBM.Repeated-measures analysis of variance was used to compare the vault values obtained by the three devices.The correlation and consistency of measurements among the three instruments were assessed using the Pearson correlation coefficient,intraclass correlation coefficient(ICC),and Bland-Altman plots.RESULTS:This retrospective study enrolled 210 myopic eyes of 210 patients(158 women and 52 men)who underwent ICL implantation:108 eyes had a myopic ICL V4c implanted,and 102 eyes had a toric ICL V4c implanted.The mean vault values measured by Pentacam,CASIA II,and UBM were 452.64±204.20μm,538.57±203.54μm,and 560.95±227.54μm,respectively,with statistically significant differences among the three groups(P<0.05).Pearson correlation analysis showed strong positive correlations between vault values measured by different instruments(all P<0.001).ICC results indicated good consistency among the three measurement modalities(all P<0.001).Stratified analysis revealed that when the vault value was≤250μm,the correlation and consistency of measurements across the three instruments were lower than those in the medium and high vault subgroups.CONCLUSION:Vault values measured by Pentacam are lower than those obtained by CASIA II and UBM,with UBM yielding the highest mean vault values.Measurements from the three instruments are not interchangeable but can serve as mutual references due to their significant correlation and good overall consistency.Pentacam and CASIA II demonstrate the highest consistency in vault measurement.Notably,when the vault value is≤250μm,the consistency between Pentacam and the other two instruments decreases significantly.
文摘In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade.
基金the World Climate Research Programme(WCRP),Climate Variability and Predictability(CLIVAR),and Global Energy and Water Exchanges(GEWEX)for facilitating the coordination of African monsoon researchsupport from the Center for Earth System Modeling,Analysis,and Data at the Pennsylvania State Universitythe support of the Office of Science of the U.S.Department of Energy Biological and Environmental Research as part of the Regional&Global Model Analysis(RGMA)program area。
文摘In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.
基金Project supported by the National Natural Science Foundation of China(Nos.12372214 and U2341231)。
文摘The Reynolds-averaged Navier-Stokes(RANS)technique enables critical engineering predictions and is widely adopted.However,since this iterative computation relies on the fixed-point iteration,it may converge to unexpected non-physical phase points in practice.We conduct an analysis on the phase-space characteristics and the fixed-point theory underlying the k-ε turbulence model,and employ the classical Kolmogorov flow as a framework,leveraging its direct numerical simulation(DNS)data to construct a one-dimensional(1D)system under periodic/fixed boundary conditions.The RANS results demonstrate that under periodic boundary conditions,the k-ε model exhibits only a unique trivial fixed point,with asymptotes capturing the phase portraits.The stability of this trivial fixed point is determined by a mathematically derived stability phase diagram,indicating the fact that the k-ε model will never converge to correct values under periodic conditions.In contrast,under fixed boundary conditions,the model can yield a stable non-trivial fixed point.The evolutionary mechanisms and their relationship with boundary condition settings systematically explain the inherent limitations of the k-ε model,i.e.,its deficiency in computing the flow field under periodic boundary conditions and sensitivity to boundary-value specifications under fixed boundary conditions.These conclusions are finally validated with the open-source code OpenFOAM.
