Conditioned medium from human dental pulp stem cells treats spinal cord injury by inhibiting microglial pyroptosis

Human dental pulp stem cell transplantation has been shown to be an effective therapeutic strategy for spinal cord injury.However,whether the human dental pulp stem cell secretome can contribute to functional recovery after spinal cord injury remains unclear.In the present study,we established a rat model of spinal cord injury based on impact injury from a dropped weight and then intraperitoneally injected the rats with conditioned medium from human dental pulp stem cells.We found that the conditioned medium effectively promoted the recovery of sensory and motor functions in rats with spinal cord injury,decreased expression of the microglial pyroptosis markers NLRP3,GSDMD,caspase-1,and interleukin-1β,promoted axonal and myelin regeneration,and inhibited the formation of glial scars.In addition,in a lipopolysaccharide-induced BV2 microglia model,conditioned medium from human dental pulp stem cells protected cells from pyroptosis by inhibiting the NLRP3/caspase-1/interleukin-1βpathway.These results indicate that conditioned medium from human dental pulp stem cells can reduce microglial pyroptosis by inhibiting the NLRP3/caspase-1/interleukin-1βpathway,thereby promoting the recovery of neurological function after spinal cord injury.Therefore,conditioned medium from human dental pulp stem cells may become an alternative therapy for spinal cord injury.

Oscillation properties of eigenfunctions for Sturm-Liouville problems with interface conditions via Prufer transformation

A class of Sturm-Liouville problems with discontinuity is studied in this paper.The oscillation properties of eigenfunctions for Sturm-Liouville problems with interface conditions are obtained.The main method used in this paper is based on Prufer transformation,which is different from the classical ones.Moreover,we give two examples to verify our main results.

Role of outdoor trees on pedestrian wind and thermal conditions around a pre-education building for sustainable energy management

Finding sustainable energy resources is essential to face the increasing energy demand.Trees are an important part of ancient architecture but are becoming rare in urban areas.Trees can control and tune the pedestrian-level wind velocity and thermal condition.In this study,a numerical investigation is employed to assess the role of trees planted in the windward direction of the building complex on the thermal and pedestrian wind velocity conditions around/inside a pre-education building located in the center of the complex.Compared to the previous studies(which considered only outside buildings),this work considers the effects of trees on microclimate change both inside/outside buildings.Effects of different parameters including the leaf area density and number of trees,number of rows,far-field velocity magnitude,and thermal condition around the main building are assessed.The results show that the flow velocity in the spacing between the first-row buildings is reduced by 30%-40% when the one-row trees with 2 m height are planted 15 m farther than the buildings.Furthermore,two rows of trees are more effective in higher velocities and reduce the maximum velocity by about 50%.The investigation shows that trees also could reduce the temperature by about 1℃around the building.

Enhancing Erectile Function and Alleviating Andropause Symptoms: Clinical Efficacy of a Human Stem Cell Conditioned Medium Cream

Erectile dysfunction (ED) is increasingly prevalent in Japan, exceeding 30%, and increasing with age. Unhealthy lifestyle habits, obesity, insufficient exercise, and smoking have been implicated in its pathogenesis, along with endothelial dysfunction of the corpora cavernosa and impaired blood flow to the penis considered underlying factors. However, the current treatments are limited to Phosphodiesterase-5 (PDE5) inhibitors. ED is the primary symptom of andropathy. This study reports the clinical efficacy of human stem cell-conditioned medium cream for ED treatment. Ten men without underlying diseases suspected of andropause with ED (mean age 43.2 ± 4.4 y, Hb 15.2 ± 0.6 gm/dL, AST/ALT 30.2/37.9 ± 12.4/14.0, eGFR 82.7 ± 12.4 mL/min/1.73 m2) were targeted. The cream was applied twice daily to the genital and scrotal areas. The erectile hardness score (EHS), International Index of Erectile Function-5 (IIEF-5), and Aging Male Symptoms (AMS) scale were used to evaluate the participants before and 30 days after use, and the results were compared using paired t-tests. The post-use qualitative opinions were collected through interviews. Significant improvements were observed compared to baseline in the IIEF-5 (11.8 ± 4.6→17.2 ± 5.1, P < 0.001), and AMS (46.3 ± 6.7→37.6 ± 5.3, P < 0.001) scores post cream use. EHS did not show a statistically significant difference, but a trend towards improvement was observed. Qualitative feedback included increased morning erection, improved maintenance of erection during intercourse, and reduced post work fatigue. Human stem cell-conditioned medium contains endothelial growth factors that potentially contribute to the improvement of ED and andropause by enhancing corporal endothelial function. Future studies should include control groups to further investigate the efficacy of these treatments.

Multi-soliton solutions of coupled Lakshmanan-Porsezian-Daniel equations with variable coefficients under nonzero boundary conditions

This paper aims to investigate the multi-soliton solutions of the coupled Lakshmanan–Porsezian–Daniel equations with variable coefficients under nonzero boundary conditions.These equations are utilized to model the phenomenon of nonlinear waves propagating simultaneously in non-uniform optical fibers.By analyzing the Lax pair and the Riemann–Hilbert problem,we aim to provide a comprehensive understanding of the dynamics and interactions of solitons of this system.Furthermore,we study the impacts of group velocity dispersion or the fourth-order dispersion on soliton behaviors.Through appropriate parameter selections,we observe various nonlinear phenomena,including the disappearance of solitons after interaction and their transformation into breather-like solitons,as well as the propagation of breathers with variable periodicity and interactions between solitons with variable periodicities.

Effects of Initial and Boundary Conditions on Heavy Rainfall Simulation over the Yellow Sea and the Korean Peninsula:Comparison of ECMWF and NCEP Analysis Data Effects and Verification with Dropsonde Observation

This study evaluated the simulation performance of mesoscale convective system(MCS)-induced precipitation,focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula.The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts(ECMWF)and National Centers for Environmental Prediction(NCEP)analysis data,as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model.Particularly,temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data.Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis,which exhibited a cold and dry bias in the lower levels below 850 hPa.The model,in terms of the precipitation simulations,particularly for high-intensity precipitation over the Yellow Sea,demonstrated higher accuracy when applying ECMWF analysis data as the initial condition.This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features(i.e.,warm and humid lower-level atmosphere)over the Yellow Sea.In conclusion,this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data.Furthermore,it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future.

Application of the Conditional Nonlinear Local Lyapunov Exponent to Second-Kind Predictability

In order to quantify the influence of external forcings on the predictability limit using observational data,the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent(CNLLE)method.The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent(NLLE)and signal-to-noise ratio methods using a coupled Lorenz model.The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings,therefore,it can quantify the predictability limit induced by the external forcings.On this basis,a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields.The spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method.This similarity supports ENSO as the major predictable signal for weather and climate prediction.In addition,a ratio of predictability limit(RPL)calculated by the CNLLE method to that calculated by the NLLE method was proposed.The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit.For instance,ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian Ocean by approximately four months,as well as the predictability limit of sea level pressure over the eastern and western Pacific Ocean.Moreover,the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere.

Research on shell-side heat and mass transfer with multi-component in LNG spiral-wound heat exchanger under sloshing conditions

The spiral-wound heat exchanger(SWHE) is the primary low-temperature heat exchanger for large-scale LNG plants due to its high-pressure resistance, compact structure, and high heat exchange efficiency. This paper studied the shell-side heat and mass transfer characteristics of vapor-liquid two-phase mixed refrigerants in an SWHE by combining a multi-component model in FLUENT software with a customized multicomponent mass transfer model. Besides, the mathematical model under the sloshing condition was obtained through mathematical derivation, and the corresponding UDF code was loaded into FLUENT as the momentum source term. The results under the sloshing conditions were compared with the relevant parameters under the steady-state condition. The shell-side heat and mass transfer characteristics of the SWHE were investigated by adjusting the component ratio and other working conditions. It was found that the sloshing conditions enhance the heat transfer performance and sometimes have insignificant effects. The sloshing condition is beneficial to reduce the flow resistance. The comprehensive performance of multi-component refrigerants has been improved and the improvement is more significant under sloshing conditions, considering both the heat transfer and pressure drop.These results will provide theoretical support for the research and design of multi-component heat and mass transfer enhancement of LNG SWHE under ocean sloshing conditions.

Intelligent pavement condition survey:Overview of current researches and practices

Automated pavement condition survey is of critical importance to road network management.There are three primary tasks involved in pavement condition surveys,namely data collection,data processing and condition evaluation.Artificial intelligence(AI)has achieved many breakthroughs in almost every aspect of modern technology over the past decade,and undoubtedly offers a more robust approach to automated pavement condition survey.This article aims to provide a comprehensive review on data collection systems,data processing algorithms and condition evaluation methods proposed between 2010 and 2023 for intelligent pavement condition survey.In particular,the data collection system includes AI-driven hardware devices and automated pavement data collection vehicles.The AI-driven hardware devices including right-of-way(ROW)cameras,ground penetrating radar(GPR)devices,light detection and ranging(LiDAR)devices,and advanced laser imaging systems,etc.These different hardware components can be selectively mounted on a vehicle to simultaneously collect multimedia information about the pavement.In addition,this article pays close attention to the application of artificial intelligence methods in detecting pavement distresses,measuring pavement roughness,identifying pavement rutting,analyzing skid resistance and evaluating structural strength of pavements.Based upon the analysis of a variety of the state-of-the-art artificial intelligence methodologies,remaining challenges and future needs with respect to intelligent pavement condition survey are discussed eventually.

Conditional Generative Adversarial Network Enabled Localized Stress Recovery of Periodic Composites

Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration occurs.Therefore,evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial.Repeating unit cells(RUCs)are commonly used to represent microstructural details and homogenize the effective response of composites.This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs.The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters,including volume fraction,fiber/matrix property ratio,fiber shapes,and loading direction.Subsequently,the conditional generative adversarial network(cGAN)is employed and constructed as a surrogate model to establish the statistical correlation between these parameters and the corresponding localized stresses.The stresses predicted by cGAN are validated against the remaining true data not used for training,showing good agreement.This work demonstrates that the cGAN-based micromechanics tool effectively captures the local responses of composite RUCs.It can be used for predicting potential crack initiations starting from microstructures and evaluating the effective behavior of periodic composites.

Debaryomyces hansenii supplementation in low fish meal diets promotes growth,modulates microbiota and enhances intestinal condition in juvenile marine fish

Background The development of a sustainable business model with social acceptance,makes necessary to develop new strategies to guarantee the growth,health,and well-being of farmed animals.Debaryomyces hansenii is a yeast species that can be used as a probiotic in aquaculture due to its capacity to i)promote cell proliferation and differen-tiation,ii)have immunostimulatory effects,iii)modulate gut microbiota,and/or iv)enhance the digestive function.To provide inside into the effects of D.hansenii on juveniles of gilthead seabream(Sparus aurata)condition,we inte-grated the evaluation of the main key performance indicators coupled with the integrative analysis of the intestine condition,through histological and microbiota state,and its transcriptomic profiling.Results After 70 days of a nutritional trial in which a diet with low levels of fishmeal(7%)was supplemented with 1.1%of D.hansenii(17.2×10^(5) CFU),an increase of ca.12%in somatic growth was observed together with an improve-ment in feed conversion in fish fed a yeast-supplemented diet.In terms of intestinal condition,this probiotic modu-lated gut microbiota without affecting the intestine cell organization,whereas an increase in the staining intensity of mucins rich in carboxylated and weakly sulphated glycoconjugates coupled with changes in the affinity for certain lectins were noted in goblet cells.Changes in microbiota were characterized by the reduction in abundance of several groups of Proteobacteria,especially those characterized as opportunistic groups.The microarrays-based transcrip-tomic analysis found 232 differential expressed genes in the anterior-mid intestine of S.aurata,that were mostly related to metabolic,antioxidant,immune,and symbiotic processes.Conclusions Dietary administration of D.hansenii enhanced somatic growth and improved feed efficiency param-eters,results that were coupled to an improvement of intestinal condition as histochemical and transcriptomic tools indicated.This probiotic yeast stimulated host-microbiota interactions without altering the intestinal cell organization nor generating dysbiosis,which demonstrated its safety as a feed additive.At the transcriptomic level,D.hansenii pro-moted metabolic pathways,mainly protein-related,sphingolipid,and thymidylate pathways,in addition to enhance antioxidant-related intestinal mechanisms,and to regulate sentinel immune processes,potentiating the defensive capacity meanwhile maintaining the homeostatic status of the intestine.

Impact of wetting-drying cycles and acidic conditions on the soil aggregate stability of yellow‒brown soil

Soil aggregate is the basic structural unit of soil,which is the foundation for supporting ecosystem functions,while its composition and stability is significantly affected by the external environment.This study was conducted to explore the effect of external environment(wetting-drying cycles and acidic conditions)on the soil aggregate distribution and stability and identify the key soil physicochemical factors that affect the soil aggregate stability.The yellow‒brown soil from the Three Gorges Reservoir area(TGRA)was used,and 8 wetting-drying conditions(0,1,2,3,4,5,10 and 15 cycles)were simulated under 4 acidic conditions(pH=3,4,5 and 7).The particle size distribution and soil aggregate stability were determined by wet sieving method,the contribution of environmental factors(acid condition,wetting-drying cycle and their combined action)to the soil aggregate stability was clarified and the key soil physicochemical factors that affect the soil aggregate stability under wetting-drying cycles and acidic conditions were determined by using the Pearson’s correlation analysis,Partial least squares path modeling(PLS‒PM)and multiple linear regression analysis.The results indicate that wetting-drying cycles and acidic conditions have significant effects on the stability of soil aggregates,the soil aggregate stability gradually decreases with increasing number of wetting-drying cycles and it obviously decreases with the increase of acidity.Moreover,the combination of wetting-drying cycles and acidic conditions aggravate the reduction in the soil aggregate stability.The wetting-drying cycles,acidic conditions and their combined effect imposes significant impact on the soil aggregate stability,and the wetting-drying cycles exert the greatest influence.The soil aggregate stability is significantly correlated with the pH,Ca^(2+),Mg^(2+),maximum disintegration index(MDI)and soil bulk density(SBD).The PLS‒PM and multiple linear regression analysis further reveal that the soil aggregate stability is primarily influenced by SBD,Ca^(2+),and MDI.These results offer a scientific basis for understanding the soil aggregate breakdown mechanism and are helpful for clarifying the coupled effect of wetting-drying cycles and acid rain on terrestrial ecosystems in the TGRA.

Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

Tailoring Classical Conditioning Behavior in TiO_(2) Nanowires:ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware

Neuromorphic hardware equipped with associative learn-ing capabilities presents fascinating applications in the next generation of artificial intelligence.However,research into synaptic devices exhibiting complex associative learning behaviors is still nascent.Here,an optoelec-tronic memristor based on Ag/TiO_(2) Nanowires:ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors.Effective implementation of synaptic behaviors,including long and short-term plasticity,and learning-forgetting-relearning behaviors,were achieved in the device through the application of light and electrical stimuli.Leveraging the optoelectronic co-modulated characteristics,a simulation of neuromorphic computing was conducted,resulting in a handwriting digit recognition accuracy of 88.9%.Furthermore,a 3×7 memristor array was constructed,confirming its application in artificial visual memory.Most importantly,complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli,respectively.After training through associative pairs,reflexes could be triggered solely using light stimuli.Comprehen-sively,under specific optoelectronic signal applications,the four features of classical conditioning,namely acquisition,extinction,recovery,and generalization,were elegantly emulated.This work provides an optoelectronic memristor with associative behavior capabilities,offering a pathway for advancing brain-machine interfaces,autonomous robots,and machine self-learning in the future.

Classification research of TCM pulse conditions based on multi-label voice analysis

Objective To explore the feasibility of remotely obtaining complex information on traditional Chinese medicine(TCM)pulse conditions through voice signals.Methods We used multi-label pulse conditions as the entry point and modeled and analyzed TCM pulse diagnosis by combining voice analysis and machine learning.Audio features were extracted from voice recordings in the TCM pulse condition dataset.The obtained features were combined with information from tongue and facial diagnoses.A multi-label pulse condition voice classification DNN model was built using 10-fold cross-validation,and the modeling methods were validated using publicly available datasets.Results The analysis showed that the proposed method achieved an accuracy of 92.59%on the public dataset.The accuracies of the three single-label pulse manifestation models in the test set were 94.27%,96.35%,and 95.39%.The absolute accuracy of the multi-label model was 92.74%.Conclusion Voice data analysis may serve as a remote adjunct to the TCM diagnostic method for pulse condition assessment.

Improving autoencoder-based unsupervised damage detection in uncontrolled structural health monitoring under noisy conditions

Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.

Impact of Initial Soil Conditions on Soil Hydrothermal and Surface Energy Fluxes in the Permafrost Region of the Tibetan Plateau

Accurate initial soil conditions play a crucial role in simulating soil hydrothermal and surface energy fluxes in land surface process modeling.This study emphasized the influence of the initial soil temperature(ST)and soil moisture(SM)conditions on a land surface energy and water simulation in the permafrost region in the Tibetan Plateau(TP)using the Community Land Model version 5.0(CLM5.0).The results indicate that the default initial schemes for ST and SM in CLM5.0 were simplistic,and inaccurately represented the soil characteristics of permafrost in the TP which led to underestimating ST during the freezing period while overestimating ST and underestimating SLW during the thawing period at the XDT site.Applying the long-term spin-up method to obtain initial soil conditions has only led to limited improvement in simulating soil hydrothermal and surface energy fluxes.The modified initial soil schemes proposed in this study comprehensively incorporate the characteristics of permafrost,which coexists with soil liquid water(SLW),and soil ice(SI)when the ST is below freezing temperature,effectively enhancing the accuracy of the simulated soil hydrothermal and surface energy fluxes.Consequently,the modified initial soil schemes greatly improved upon the results achieved through the long-term spin-up method.Three modified initial soil schemes experiments resulted in a 64%,88%,and 77%reduction in the average mean bias error(MBE)of ST,and a 13%,21%,and 19%reduction in the average root-mean-square error(RMSE)of SLW compared to the default simulation results.Also,the average MBE of net radiation was reduced by 7%,22%,and 21%.

Effect of boundary conditions on shakedown analysis of heterogeneous materials

The determination of the ultimate load-bearing capacity of structures made of elastoplastic heterogeneous materials under varying loads is of great importance for engineering analysis and design. Therefore, it is necessary to accurately predict the shakedown domains of these materials. The static shakedown theorem, also known as Melan's theorem, is a fundamental method used to predict the shakedown domains of structures and materials. Within this method, a key aspect lies in the construction and application of an appropriate self-equilibrium stress field(SSF). In the structural shakedown analysis, the SSF is typically constructed by governing equations that satisfy no external force(NEF) boundary conditions. However, we discover that directly applying these governing equations is not suitable for the shakedown analysis of heterogeneous materials. Researchers must consider the requirements imposed by the Hill-Mandel condition for boundary conditions and the physical significance of representative volume elements(RVEs). This paper addresses this issue and demonstrates that the sizes of SSFs vary under different boundary conditions, such as uniform displacement boundary conditions(DBCs), uniform traction boundary conditions(TBCs), and periodic boundary conditions(PBCs). As a result, significant discrepancies arise in the predicted shakedown domain sizes of heterogeneous materials. Built on the demonstrated relationship between SSFs under different boundary conditions, this study explores the conservative relationships among different shakedown domains, and provides proof of the relationship between the elastic limit(EL) factors and the shakedown loading factors under the loading domain of two load vertices. By utilizing numerical examples, we highlight the conservatism present in certain results reported in the existing literature. Among the investigated boundary conditions, the obtained shakedown domain is the most conservative under TBCs.Conversely, utilizing PBCs to construct an SSF for the shakedown analysis leads to less conservative lower bounds, indicating that PBCs should be employed as the preferred boundary conditions for the shakedown analysis of heterogeneous materials.

Consideration of the influence of supports in modeling the electromagnetic fields of 25 kV traction networks under emergency conditions

Single-phase 25 kV traction networks of electrified alternating current(AC)railways create electromagnetic fields(EMFs)with significant levels of intensity.The most intense magnetic fields occur when short circuits exist between the contact wire and rails or ground.Despite the short duration of exposure,they can adversely affect electronic devices and induce significant voltages in adjacent power lines,which is dangerous for operating personnel.Although numerous investigations have focused on modeling the EMF of traction networks and power lines,the challenge of determining the three-dimensional electromagnetic fields near metal supports during the flow of a short-circuit current through them is yet to be resolved.In this case,the field has a complex spatial structure that significantly complicates the calculations of intensities.This study proposes a methodology,algorithms,software,and digital models for determining the EMF in the described emergency scenarios.During the modeling process,the objects being studied were represented by segments of thin wires to analyze the distribution of the electric charge and calculate the intensities of the electric and magnetic fields.This approach was implemented in the Fazonord software,and the modeling results show a substantial increase in EMF levels close to the support,with a noticeable decrease in the levels as the distance from it increases.The procedure implemented in the commercial software Fazonord is universal and can be used to determine electromagnetic fields at any electrical power facility that includes live parts of limited length.Based on the proposed procedure,the EMF near the supports of overhead power lines and traction networks of various designs could be determined,the EMF levels at substations can be calculated,and the influence of metal structures located near traction networks,such as pedestrian crossings at railway stations,can be considered.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.