Characterizing permeability-porosity relationships of porous rocks using a stress sensitivity model in consideration of elastic-structural deformation and tortuosity sensitivity

Clarifying the relationship between stress sensitivities of permeability and porosity is of great significance in guiding underground resource mining.More and more studies focus on how to construct stress sensitivity models to describe the relationship and obtain a comprehensive stress sensitivity of porous rock.However,the limitations of elastic deformation calculation and incompleteness of considered tortuosity sensitivity lead to the fact that the existing stress sensitivity models are still unsatisfactory in terms of accuracy and generalization.Therefore,a more accurate and generic stress sensitivity model considering elastic-structural deformation of capillary cross-section and tortuosity sensitivity is proposed in this paper.The elastic deformation is derived from the fractal scaling model and Hooke's law.Considering the effects of elastic-structural deformation on tortuosity sensitivity,an empirical formula is proposed,and the conditions for its applicability are clarified.The predictive performance of the proposed model for the permeability-porosity relationships is validated in several sets of publicly available experimental data.These experimental data are from different rocks under different pressure cycles.The mean and standard deviation of relative errors of predicted stress sensitivity with respect to experimental data are 2.63%and 1.91%.Compared with other models,the proposed model has higher accuracy and better predictive generalization performance.It is also found that the porosity sensitivity exponent a,which can describe permeability-porosity relationships,is 2 when only elastic deformation is considered.a decreases from 2 when structural deformation is also considered.In addition,a may be greater than 3 due to the increase in tortuosity sensitivity when tortuosity sensitivity is considered even if the rock is not fractured.

High-Precision Flow Numerical Simulation and Productivity Evaluation of Shale Oil Considering Stress Sensitivity

Continental shale oil reservoirs,characterized by numerous bedding planes and micro-nano scale pores,feature significantly higher stress sensitivity compared to other types of reservoirs.However,research on suitable stress sensitivity characterization models is still limited.In this study,three commonly used stress sensitivity models for shale oil reservoirs were considered,and experiments on representative core samples were conducted.By fitting and comparing the data,the“exponential model”was identified as a characterization model that accurately represents stress sensitivity in continental shale oil reservoirs.To validate the accuracy of the model,a two-phase seepage mathematical model for shale oil reservoirs coupled with the exponential model was introduced.The model was discretely solved using the finite volume method,and its accuracy was verified through the commercial simulator CMG.The study evaluated the productivity of a typical horizontal well under different engineering,geological,and fracture conditions.The results indicate that considering stress sensitivity leads to a 13.57%reduction in production for the same matrix permeability.Additionally,as the fracture half-length and the number of fractures increase,and the bottomhole flowing pressure decreases,the reservoir stress sensitivity becomes higher.

近红外无创血糖浓度的Label Sensitivity算法和支持向量机回归

近红外光谱分析技术在生物医学工程领域具有广阔应用前景。无创且持续性地测量能实时监控人体血糖水平,给糖尿病患者带来极大便利性、提高生存质量、降低糖尿病并发症发生率具有很大的社会效益。无创血糖监测的想法提出较早,但仍然存在预测精度低、预测值与标签值相关性不高等难点,至今没有达到临床要求。近年来,光谱检测技术发展迅猛且机器学习技术在智能信息处理方面具有明显优势,两者结合可以有效提高人体无创血糖医学监测模型的精度和普适性。提出了一种标签敏感度算法(LS),并结合支持向量机方法建立了人体血糖含量预测模型。使用近红外光谱仪采集了4名志愿者食指处动态血液光谱数据(每名志愿者28组数据),并使用多元散射矫正(MSC)方法消除了部分光散射的影响。考虑血糖对不同波长光的吸收有差异,提出了基于血糖浓度标签差的特征波长挑选方法,并构建了标签敏感度支持向量机(LSSVR)预测模型。设计实验,对比该模型与偏最小二乘回归(PLSR)和区分度支持向量机(FSSVR)算法。结果表明,LS算法的最佳特征波长数为32,经特征波长选择后的LSSVR表现最佳,其均方误差降低至0.02 mmol·L^(-1),明显优于全谱段PLSR模型,血糖浓度的预测值与标签值的相关系数提升至99.8%,预测值全部位于可容许误差的克拉克网格A区内。LSSVR模型的优异表现为早日实现血糖的无创监测提供了新思路。

Promoting Ethical Sensitivity in Nursing Education:A Systematic Review of Case Study-Based Health Education

Introduction: Nursing ethical sensitivity refers to a nurse’s capacity for thoughtful consideration of ethical issues when faced with dilemmas. In the nursing domain, ethical dilemmas arise when nurses face challenges in making sound ethical decisions during clinical practice. These challenges may stem from conflicts between personal values and professional responsibilities. Methodology: Articles downloaded from Pub Med, CNKI, and Google Scholar were reviewed. Results: After rigorous screening, a meticulous analysis was conducted, encompassing 10 articles and involving a substantial cohort of 2863 participants. Existing literature revealed variations in the ethical dilemmas faced by nurses across different departments. The ethical sensitivity of nurses also varies, with higher ethical sensitivity correlating with stronger empathetic abilities. Zhen et al. classified the causes of ethical dilemmas into four main categories: (1) Ethical dilemmas arising from personal reasons of nursing students;(2) Ethical dilemmas stemming from the actions of teachers;(3) Ethical dilemmas triggered by patients;(4) Ethical dilemmas resulting from miscellaneous reasons. Conclusion: According to literature findings, nursing ethical sensitivity was positively linked to the nurse’s ethical decision-making ability. A case study-oriented teaching program has proven effective in enhancing ethical sensitivity among nursing students.

Identification, pathogenicity, and fungicide sensitivity of Eutiarosporella dactylidis associated with leaf blight on maize in China

Maize(Zea mays L.) is an economically vital grain crop that is cultivated worldwide. In 2011, a maize foliar disease was detected in Lingtai and Lintao counties in Gansu Province, China. The characteristic signs and symptoms of this disease include irregular chlorotic lesions on the tips and edges of infected leaves and black punctate fruiting bodies in dead leaf tissues. Given favourable environmental conditions, this disease spread to areas surrounding Gansu. In this study, infected leaves were collected from Gansu and Ningxia Hui Autonomous Region between 2018and 2020 to identify the disease-causing pathogen. Based on morphological features, pathogenicity tests, and multilocus phylogenetic analysis involving internal transcribed spacer(ITS), 18S small subunit rDNA(SSU), 28S large subunit rDNA(LSU), translation elongation factor 1-alpha(TEF), and β-tubulin(TUB) sequences, Eutiarosporella dactylidis was identified as the causative pathogen of this newly discovered leaf blight. Furthermore, an in vitro bioassay was conducted on representative strains using six fungicides, and both fludioxonil and carbendazim were found to significantly inhibit the mycelial growth of E. dactylidis. The results of this study provide a reference for the detection and management of Eutiarosporella leaf blight.

Efficient slope reliability and sensitivity analysis using quantile-based first-order second-moment method

This paper introduces a novel approach for parameter sensitivity evaluation and efficient slope reliability analysis based on quantile-based first-order second-moment method(QFOSM).The core principles of the QFOSM are elucidated geometrically from the perspective of expanding ellipsoids.Based on this geometric interpretation,the QFOSM is further extended to estimate sensitivity indices and assess the significance of various uncertain parameters involved in the slope system.The proposed method has the advantage of computational simplicity,akin to the conventional first-order second-moment method(FOSM),while providing estimation accuracy close to that of the first-order reliability method(FORM).Its performance is demonstrated with a numerical example and three slope examples.The results show that the proposed method can efficiently estimate the slope reliability and simultaneously evaluate the sensitivity of the uncertain parameters.The proposed method does not involve complex optimization or iteration required by the FORM.It can provide a valuable complement to the existing approximate reliability analysis methods,offering rapid sensitivity evaluation and slope reliability analysis.

Future changes in precipitation and water availability over the Tibetan Plateau projected by CMIP6 models constrained by climate sensitivity

Precipitation projections over the Tibetan Plateau(TP)show diversity among existing studies,partly due to model uncertainty.How to develop a reliable projection remains inconclusive.Here,based on the IPCC AR6–assessed likely range of equilibrium climate sensitivity(ECS)and the climatological precipitation performance,the authors constrain the CMIP6(phase 6 of the Coupled Model Intercomparison Project)model projection of summer precipitation and water availability over the TP.The best estimates of precipitation changes are 0.24,0.25,and 0.45 mm d^(−1)(5.9%,6.1%,and 11.2%)under the Shared Socioeconomic Pathway(SSP)scenarios of SSP1–2.6,SSP2–4.5,and SSP5–8.5 from 2050–2099 relative to 1965–2014,respectively.The corresponding constrained projections of water availability measured by precipitation minus evaporation(P–E)are 0.10,0.09,and 0.22 mm d^(−1)(5.7%,4.9%,and 13.2%),respectively.The increase of precipitation and P–E projected by the high-ECS models,whose ECS values are higher than the upper limit of the likely range,are about 1.7 times larger than those estimated by constrained projections.Spatially,there is a larger increase in precipitation and P–E over the eastern TP,while the western part shows a relatively weak difference in precipitation and a drier trend in P–E.The wetter TP projected by the high-ECS models resulted from both an approximately 1.2–1.4 times stronger hydrological sensitivity and additional warming of 0.6℃–1.2℃ under all three scenarios during 2050–2099.This study emphasizes that selecting climate models with climate sensitivity within the likely range is crucial to reducing the uncertainty in the projection of TP precipitation and water availability changes.

Spatial sensitivity to absorption changes for various near-infrared spectroscopy methods:A compendium review

This compendium review focuses on the spatial distribution of sensitivity to localized absorption changes in optically diffuse media,particularly for measurements relevant to near-infrared spectroscopy.The three temporal domains,continuous wave,frequency domain,and time domain,each obtain different optical data types whose changes may be related to effective homogeneous changes in the absorption coefficient.Sensitivity is the relationship between a localized perturbation and the recovered effective homogeneous absorption change.Therefore,spatial sensitivity maps representing the perturbation location can be generated for the numerous optical data types in the three temporal domains.The review first presents a history of the past 30 years of work investigating this sensitivity in optically diffuse media.These works are experimental and theoretical,presenting one-,two-,and three-dimensional sensitivity maps for different Near-Infrared Spectroscopy methods,domains,and data types.Following this history,we present a compendium of sensitivity maps organized by temporal domain and then data type.This compendium provides a valuable tool to compare the spatial sensitivity of various measurement methods and parameters in one document.Methods for one to generate these maps are provided in Appendix A,including the code.This historical review and comprehensive sensitivity map compendium provides a single source researchers may use to visualize,investigate,compare,and generate sensitivity to localized absorption change maps.

Analysis of sensitivity to hydrate blockage risk in natural gas gathering pipeline

During the operational process of natural gas gathering and transmission pipelines,the formation of hydrates is highly probable,leading to uncontrolled movement and aggregation of hydrates.The continuous migration and accumulation of hydrates further contribute to the obstruction of natural gas pipelines,resulting in production reduction,shutdowns,and pressure build-ups.Consequently,a cascade of risks is prone to occur.To address this issue,this study focuses on the operational process of natural gas gathering and transmission pipelines,where a comprehensive framework is established.This framework includes theoretical models for pipeline temperature distribution,pipeline pressure distribution,multiphase flow within the pipeline,hydrate blockage,and numerical solution methods.By analyzing the influence of inlet temperature,inlet pressure,and terminal pressure on hydrate formation within the pipeline,the sensitivity patterns of hydrate blockage risks are derived.The research indicates that reducing inlet pressure and terminal pressure could lead to a decreased maximum hydrate formation rate,potentially mitigating pipeline blockage during natural gas transportation.Furthermore,an increase in inlet temperature and terminal pressure,and a decrease in inlet pressure,results in a displacement of the most probable location for hydrate blockage towards the terminal station.However,it is crucial to note that operating under low-pressure conditions significantly elevates energy consumption within the gathering system,contradicting the operational goal of energy efficiency and reduction of energy consumption.Consequently,for high-pressure gathering pipelines,measures such as raising the inlet temperature or employing inhibitors,electrical heat tracing,and thermal insulation should be adopted to prevent hydrate formation during natural gas transportation.Moreover,considering abnormal conditions such as gas well production and pipeline network shutdowns,which could potentially trigger hydrate formation,the installation of methanol injection connectors remains necessary to ensure production safety.

Activation Distance and Capacity Analysis for Ambient Backscatter Communications with Sensitivity Constraint and Beamforming

Circuit sensitivity of sensors or tags without battery is one practical constraint for ambient backscatter communication systems.This letter considers using beamforming to reduce the sensitivity constraint and evaluates the corresponding performance in terms of the tag activation distance and the system capacity.Specifically,we derive the activation probabilities of the tag in the case of single-antenna and multi-antenna transmitters.Besides,we obtain the capacity expressions for the ambient backscatter communication system with beamforming and illustrate the power allocation that maximizes the system capacity when the tag is activated.Finally,simulation results are provided to corroborate our proposed studies.

Micromechanism and mathematical model of stress sensitivity in tight reservoirs of binary granular medium

Research on reservoir rock stress sensitivity has traditionally focused on unary granular structures,neglecting the binary nature of real reservoirs,especially tight reservoirs.Understanding the stresssensitive behavior and mathematical characterization of binary granular media remains a challenging task.In this study,we conducted online-NMR experiments to investigate the permeability and porosity evolution as well as stress-sensitive control mechanisms in tight sandy conglomerate samples.The results revealed stress sensitivity coefficients between 0.042 and 0.098 and permeability damage rates ranging from 65.6%to 90.9%,with an average pore compression coefficient of 0.0168—0.0208 MPa 1.Pore-scale compression occurred in three stages:filling,compression,and compaction,with matrix pores playing a dominant role in pore compression.The stress sensitivity of binary granular media was found to be influenced by the support structure and particle properties.High stress sensitivity was associated with small fine particle size,high fines content,high uniformity coefficient of particle size,high plastic deformation,and low Young's modulus.Matrix-supported samples exhibited a high irreversible permeability damage rate(average=74.2%)and stress sensitivity coefficients(average=0.089),with pore spaces more slit-like.In contrast,grain-supported samples showed low stress sensitivity coefficients(average=0.021)at high stress stages.Based on the experiments,we developed a mathematical model for stress sensitivity in binary granular media,considering binary granular properties and nested interactions using Hertz contact deformation and Poiseuille theory.By describing the change in activity content of fines under stress,we characterized the non-stationary state of compressive deformation in the binary granular structure and classified the reservoir into three categories.The model was applied for production prediction using actual data from the Mahu reservoir in China,showing that the energy retention rates of support-dominated,fill-dominated,and matrix-controlled reservoirs should be higher than 70.1%,88%,and 90.2%,respectively.

On an isotropic porous solid cylinder:the analytical solution and sensitivity analysis of the pressure

Within this work,we perform a sensitivity analysis to determine the influence of the material input parameters on the pressure in an isotropic porous solid cylinder.We provide a step-by-step guide to obtain the analytical solution for a porous isotropic elastic cylinder in terms of the pressure,stresses,and elastic displacement.We obtain the solution by performing a Laplace transform on the governing equations,which are those of Biot's poroelasticity in cylindrical polar coordinates.We enforce radial boundary conditions and obtain the solution in the Laplace transformed domain before reverting back to the time domain.The sensitivity analysis is then carried out,considering only the derived pressure solution.This analysis finds that the time t,Biot's modulus M,and Poisson's ratio ν have the highest influence on the pressure whereas the initial value of pressure P_(0) plays a very little role.

Sensitivity and resistance risk analysis of Didymella bryoniae populations to fluopyram

Fluopyram is an succinate dehydrogenase inhibitors(SDHI)fungicide that has been registered in China to control gummy stem blight(GSB)in watermelons for many years.However,whether the field pathogens of GSB are still sensitive to fluopyram or not is unknown.Therefore,we collected 69 Didymella bryoniae isolates from the fields that usually use fluopyram to control GSB to determine the sensitivity change.The EC_(50)(50%inhibition effect)values of fluopyram against D.bryoniae ranged from 0.0691 to 0.3503μg mL^(–1) and the variation factor was 5.07.The mean EC_(50) value was(0.1579±0.0669)μg mL^(–1) and the curve of sensitivity was unimodal.No resistant strains were found in the isolates,which means that the pathogens were still sensitive to fluopyram.The minimal inhibition concentration(MIC)of fluopyram against D.bryoniae was 3μg mL^(–1).Four low-resistant mutants and two medium-resistant mutants were obtained using fungicide taming and the resistance of mutants could be inherited stably.The growth rate of mutants decreased significantly compared with that of wild-type strains while the biomass of most mutants was similar to that of wild-type strains.The sensitivity of most resistant mutants to various stresses was increased compared with that of wild-type strains.The virulence of mutants receded except for low-resistant mutant XN51FR-1,which had the same lesion area as XN51 on the watermelon leaves.The results indicated that the fitness of resistant mutants was decreased compared with that of wild-type strains.The cross-resistance assay indicated that fluopyram-resistant mutants were positive cross-resistant to all six SDHI fungicides in this test but were still sensitive to fluazinam and tebuconazole.So the resistance risk of D.bryoniae to fluopyram was moderate.In addition,we found that the SdhB gene of low-resistant mutant XN30FR-1 had three new point mutations at positions K258N,A259P,and H277N.Medium-resistant mutant XN52FR-1 showed a mutation at position H277N and other mutants did not have any point mutation.

An efficient data-driven global sensitivity analysis method of shale gas production through convolutional neural network

The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively evaluate the relative importance of model parameters on the production forecasting performance,sensitivity analysis of parameters is required.The parameters are ranked according to the sensitivity coefficients for the subsequent optimization scheme design.A data-driven global sensitivity analysis(GSA)method using convolutional neural networks(CNN)is proposed to identify the influencing parameters in shale gas production.The CNN is trained on a large dataset,validated against numerical simulations,and utilized as a surrogate model for efficient sensitivity analysis.Our approach integrates CNN with the Sobol'global sensitivity analysis method,presenting three key scenarios for sensitivity analysis:analysis of the production stage as a whole,analysis by fixed time intervals,and analysis by declining rate.The findings underscore the predominant influence of reservoir thickness and well length on shale gas production.Furthermore,the temporal sensitivity analysis reveals the dynamic shifts in parameter importance across the distinct production stages.

Uncertainty and sensitivity analysis of in-vessel phenomena under severe accident mitigation strategy based on ISAA-SAUP program

The phenomenology involved in severe accidents in nuclear reactors is highly complex.Currently,integrated analysis programs used for severe accident analysis heavily rely on custom empirical parameters,which introduce considerable uncertainty.Therefore,in recent years,the field of severe accidents has shifted its focus toward applying uncertainty analysis methods to quantify uncertainty in safety assessment programs,known as“best estimate plus uncertainty(BEPU).”This approach aids in enhancing our comprehension of these programs and their further development and improvement.This study concentrates on a third-generation pressurized water reactor equipped with advanced active and passive mitigation strategies.Through an Integrated Severe Accident Analysis Program(ISAA),numerical modeling and uncertainty analysis were conducted on severe accidents resulting from large break loss of coolant accidents.Seventeen uncertainty parameters of the ISAA program were meticulously screened.Using Wilks'formula,the developed uncertainty program code,SAUP,was employed to carry out Latin hypercube sampling,while ISAA was employed to execute batch calculations.Statistical analysis was then conducted on two figures of merit,namely hydrogen generation and the release of fission products within the pressure vessel.Uncertainty calculations revealed that hydrogen production and the fraction of fission product released exhibited a normal distribution,ranging from 182.784 to 330.664 kg and from 15.6 to 84.3%,respectively.The ratio of hydrogen production to reactor thermal power fell within the range of 0.0578–0.105.A sensitivity analysis was performed for uncertain input parameters,revealing significant correlations between the failure temperature of the cladding oxide layer,maximum melt flow rate,size of the particulate debris,and porosity of the debris with both hydrogen generation and the release of fission products.

Explainable Neural Network for Sensitivity Analysis of Lithium-ion Battery Smart Production

Battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery performance.As battery production is complicated with strongly coupled intermediate and control parameters,an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently required.This paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven modelling.To be specific,an explainable neural network named generalized additive model with structured interaction(GAM-SI)is designed to predict two key battery properties,including electrode mass loading and porosity,while the effects of four early production terms on manufactured batteries are explained and analysed.The experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating stages.In addition,the importance ratio ranking,global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural network.Due to the merits of interpretability,the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior,further benefitting smart battery production.

A Bayesian multi-model inference methodology for imprecise momentindependent global sensitivity analysis of rock structures

Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response.This paper proposes an augmented Bayesian multi-model inference(BMMI)coupled with GSA methodology(BMMI-GSA)to address this issue by estimating the imprecision in the momentindependent sensitivity indices of rock structures arising from the small size of input data.The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties.The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo’s indices by employing a reweighting approach on candidate probabilistic models.The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India.The proposed methodology was superior to the conventional GSA(neglects all epistemic uncertainties)and Bayesian coupled GSA(B-GSA)(neglects model uncertainty)due to its capability to incorporate the uncertainties in both model type and parameters of properties.Imprecise Borgonovo’s indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates,which makes the user more informed in the data collection efforts.Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes.The accurate importance ranking of properties was only possible via samples of large sizes.Further,the impact of the prior knowledge in terms of prior ranges and distributions was significant;hence,any related assumption should be made carefully.

Assessment of electrostatic discharge sensitivity of nitrogen-rich heterocyclic energetic compounds and their salts as high energy-density dangerous compounds:A study of structural variables

Nitrogen-rich heterocyclic energetic compounds(NRHECs)and their salts have witnessed widespread synthesis in recent years.The substantial energy-density content within these compounds can lead to potentially dangerous explosive reactions when subjected to external stimuli such as electrical discharge.Therefore,developing a reliable model for predicting their electrostatic discharge sensitivity(ESD)becomes imperative.This study proposes a novel and straightforward model based on the presence of specific groups(-NH_(2) or-NH-,-N=N^(+)-O^(-)and-NNO_(2),-ONO_(2) or-NO_(2))under certain conditions to assess the ESD of NRHECs and their salts,employing interpretable structural parameters.Utilizing a comprehensive dataset comprising 54 ESD measurements of NRHECs and their salts,divided into 49/5 training/test sets,the model achieves promising results.The Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and Maximum Error for the training set are reported as 0.16 J,0.12 J,and 0.5 J,respectively.Notably,the ratios RMSE(training)/RMSE(test),MAE(training)/MAE(test),and Max Error(training)/Max Error(test)are all greater than 1.0,indicating the robust predictive capabilities of the model.The presented model demonstrates its efficacy in providing a reliable assessment of ESD for the targeted NRHECs and their salts,without the need for intricate computer codes or expert involvement.

Assessment of desertification sensitivity using an improved MEDALUS model in Northern China

Assessment of land sensitivity to desertification is an important step to support desertification monitoring and control.Based on the Mediterranean Desertification and Land Use(MEDALUS)model,we defined four quality indicators(soil,climate,vegetation and management)to evaluate the sensitivity of land in northern China to desertification.We improved MEDALUS via excluding cities from the areas at risk of desertification by means of defining a threshold value for population density.The framework,validated in northern China,further optimizes the model to link priority areas and land restoration programmed to support desertification control.We found that the four indicators influenced and restricted each other,which jointly affected the distribution of desertification sensitivity in northern China.The spatial distribution of sensitivity in northern China showed large regional differences,with clear boundaries and concentrated distributions of regions with high and low sensitivity;the overall sensitivity decreased,with some areas rated as having moderate,severe,and extremely severe sensitivity changing to slight sensitivity;and the influence weight was much higher for the management quality index than for the climate,vegetation,and soil indexes.This suggests that management was the main factor that affected desertification sensitivity in northern China,and that climate factors exacerbated sensitivity,but the factors that are driving the spatial heterogeneity of the influencing factors need further study。

Phase sensitivity with a coherent beam and twin beams via intensity difference detection

We focus on the Mach–Zehnder interferometer(MZI) with the input of a coherent beam and one of the bright entangled twin beams with an external power reference beam employed for measurement. The results show that the phase sensitivity can reach sub-Heisenberg limit and approach quantum Cramer–Rao bound by changing the squeezing parameters and the photon number of the coherent beam, under the phase-matching condition. The absence of the external power reference beam will degrade the performance of the phase sensitivity. Meanwhile, this scheme shows good robustness against the losses of the photon detectors. We present a detailed discussion about the phase sensitivities when the inputs are two coherent beams, or a coherent beam plus a single-mode squeezed vacuum beam based on the MZI. This scenario can be applied in the field of phase precision measurements and other optical sensors.