Distribution law and susceptibility of geohazards across a gradient belt of the Western Sichuan Plateau

Across a gradient belt of the Western Sichuan Plateau,geohazards have seriously limited economic and social development.According to incomplete statistics,15,673 geohazards have been recorded in the study area.In order to mitigate the threat of geohazards to human engineering activities in the region,an overall understanding of the distribution pattern of geohazards and susceptibility assessment are necessary.In this paper,a gradient belt of the Western Sichuan Plateau and its zoning criteria were defined.Subsequently,on the basis of relief amplitude,distance to faults,rainfall,and human activities,three indicators of endogenic process were introduced:Bouguer gravity anomaly gradient,vertical deformation gradient,and horizontal deformation gradient.Thereafter,the distribution patterns of geohazards were investigated through mathematical statistics and ArcGIS software.By randomly selecting 10,449 hazards,a geohazard susceptibility map was generated using the Information Value(IV)model.Finally,the IV model was validated against 5224 hazards using the Area Under Curve(AUC)method.The results show that 47.6%of the geohazards were distributed in the zone of steep slope.Geohazards showed strong responses to distance to faults,human activities,and annual rainfall.The distribution of geohazards in the gradient belt of the Western Sichuan Plateau is more sensitive to vertical internal dynamics factors(such as vertical deformation gradient and Bouguer gravity anomaly gradient)without any apparent sensitivity to horizontal internal dynamics factors.The areas of high and very-high risk account for up to 32.22%,mainly distributed in the Longmenshan and Anning River faults.According to the AUC plot,the success rate of the IV model for generating the susceptibility map is 76%.This susceptibility map and geohazard distribution pattern can provide a reference for geological disaster monitoring,preparation of post-disaster emergency measures,and town planning.

Stochastic Gradient Compression for Federated Learning over Wireless Network

As a mature distributed machine learning paradigm,federated learning enables wireless edge devices to collaboratively train a shared AI-model by stochastic gradient descent(SGD).However,devices need to upload high-dimensional stochastic gradients to edge server in training,which cause severe communication bottleneck.To address this problem,we compress the communication by sparsifying and quantizing the stochastic gradients of edge devices.We first derive a closed form of the communication compression in terms of sparsification and quantization factors.Then,the convergence rate of this communicationcompressed system is analyzed and several insights are obtained.Finally,we formulate and deal with the quantization resource allocation problem for the goal of minimizing the convergence upper bound,under the constraint of multiple-access channel capacity.Simulations show that the proposed scheme outperforms the benchmarks.

Policy Gradient Adaptive Dynamic Programming for Model-Free Multi-Objective Optimal Control

Dear Editor,In this letter,the multi-objective optimal control problem of nonlinear discrete-time systems is investigated.A data-driven policy gradient algorithm is proposed in which the action-state value function is used to evaluate the policy.In the policy improvement process,the policy gradient based method is employed.

Wettability Gradient-Induced Diode:MXene-Engineered Membrane for Passive-Evaporative Cooling

Thermoregulatory textiles,leveraging high-emissivity structural materials,have arisen as a promising candidate for personal cooling management;however,their advancement has been hindered by the underperformed water moisture transportation capacity,which impacts on their thermophysiological comfort.Herein,we designed a wettability-gradient-induced-diode(WGID)membrane achieving by MXene-engineered electrospun technology,which could facilitate heat dissipation and moisture-wicking transportation.As a result,the obtained WGID membrane could obtain a cooling temperature of 1.5℃ in the“dry”state,and 7.1℃ in the“wet”state,which was ascribed to its high emissivity of 96.40%in the MIR range,superior thermal conductivity of 0.3349 W m^(-1) K^(-1)(based on radiation-and conduction-controlled mechanisms),and unidirectional moisture transportation property.The proposed design offers an approach for meticulously engineering electrospun membranes with enhanced heat dissipation and moisture transportation,thereby paving the way for developing more efficient and comfortable thermoregulatory textiles in a high-humidity microenvironment.

Bioinspired Multifunctional Self-Sensing Actuated Gradient Hydrogel for Soft-Hard Robot Remote Interaction

The development of bioinspired gradient hydrogels with self-sensing actuated capabilities for remote interaction with soft-hard robots remains a challenging endeavor. Here, we propose a novel multifunctional self-sensing actuated gradient hydrogel that combines ultrafast actuation and high sensitivity for remote interaction with robotic hand. The gradient network structure, achieved through a wettability difference method involving the rapid precipitation of MoO_(2) nanosheets, introduces hydrophilic disparities between two sides within hydrogel. This distinctive approach bestows the hydrogel with ultrafast thermo-responsive actuation(21° s^(-1)) and enhanced photothermal efficiency(increase by 3.7 ℃ s^(-1) under 808 nm near-infrared). Moreover, the local cross-linking of sodium alginate with Ca^(2+) endows the hydrogel with programmable deformability and information display capabilities. Additionally, the hydrogel exhibits high sensitivity(gauge factor 3.94 within a wide strain range of 600%), fast response times(140 ms) and good cycling stability. Leveraging these exceptional properties, we incorporate the hydrogel into various soft actuators, including soft gripper, artificial iris, and bioinspired jellyfish, as well as wearable electronics capable of precise human motion and physiological signal detection. Furthermore, through the synergistic combination of remarkable actuation and sensitivity, we realize a self-sensing touch bioinspired tongue. Notably, by employing quantitative analysis of actuation-sensing, we realize remote interaction between soft-hard robot via the Internet of Things. The multifunctional self-sensing actuated gradient hydrogel presented in this study provides a new insight for advanced somatosensory materials, self-feedback intelligent soft robots and human–machine interactions.

Local Adaptive Gradient Variance Attack for Deep Fake Fingerprint Detection

In recent years,deep learning has been the mainstream technology for fingerprint liveness detection(FLD)tasks because of its remarkable performance.However,recent studies have shown that these deep fake fingerprint detection(DFFD)models are not resistant to attacks by adversarial examples,which are generated by the introduction of subtle perturbations in the fingerprint image,allowing the model to make fake judgments.Most of the existing adversarial example generation methods are based on gradient optimization,which is easy to fall into local optimal,resulting in poor transferability of adversarial attacks.In addition,the perturbation added to the blank area of the fingerprint image is easily perceived by the human eye,leading to poor visual quality.In response to the above challenges,this paper proposes a novel adversarial attack method based on local adaptive gradient variance for DFFD.The ridge texture area within the fingerprint image has been identified and designated as the region for perturbation generation.Subsequently,the images are fed into the targeted white-box model,and the gradient direction is optimized to compute gradient variance.Additionally,an adaptive parameter search method is proposed using stochastic gradient ascent to explore the parameter values during adversarial example generation,aiming to maximize adversarial attack performance.Experimental results on two publicly available fingerprint datasets show that ourmethod achieves higher attack transferability and robustness than existing methods,and the perturbation is harder to perceive.

Gradient coil design with enhanced shielding constraint for a cryogen-free superconducting MRI system

The relatively fragile low-temperature stability of cryogen-free superconducting magnetic resonance imaging(MRI)magnets requires the careful management of exogenous heat sources.A strongly shielded gradient magnetic field is important for the optimal operation of cryogen-free MRI systems.In this study,we present an enhanced shielding method incorporating a regionalized stray field constraining strategy.By optimizing the constraint parameters,we could develop engineering-feasible gradient coil schemes without increasing system complexity but with the stray field intensity reduced by half.In real measurement in an integrated MRI system,the developed gradient assembly demonstrated good performance and supported to output images of excellent quality.Our findings suggested that the proposed method could potentially form a useful design paradigm for cryogen-free MRI magnets.

Fast compressed sensing spectral measurement with adaptive gradient multiscale resolution

We propose a fast,adaptive multiscale resolution spectral measurement method based on compressed sensing.The method can apply variable measurement resolution over the entire spectral range to reduce the measurement time by over 75%compared to a global high-resolution measurement.Mimicking the characteristics of the human retina system,the resolution distribution follows the principle of gradually decreasing.The system allows the spectral peaks of interest to be captured dynamically or to be specified a priori by a user.The system was tested by measuring single and dual spectral peaks,and the results of spectral peaks are consistent with those of global high-resolution measurements.

Slip distribution inversion of seismic sub-fault dip iteration using gradient based optimizer algorithm

This paper considers setting different dips for different sub-faults to fit the actual rupture situation based on the fault rupture of the 2013 Lushan M_(S)7.0 earthquake.Meanwhile,combined with the coseismic GNSS data of the Lushan earthquake,the source parameters and sliding distribution of the Lushan earthquake fault are inversed.Firstly,we use the gradient based optimizer(GBO)in nonlinear inversion to obtain the source parameters of this seismic fault.The inversion results indicate that the strike of the fault is 206.52°,the dip is 44.10°,the length is 21.92 km,and the depth is 12.79 km.To refine the sliding distribution of the seismic fault,the seismic fault is divided into 3×3 sub-faults.Then,we fix the central sub-fault dip of 44.10°;the dip of other sub-faults is obtained by iteration.After that,the model is further divided into a fault layer model composed of 23×19 sub fault slices,and using the Matlab fitting function is used to fit the dip of the 23×19 sub faults.Finally,the Lushan seismic fault plane is established as a shovel structure with steep upper and gentle lower,steep south and gentle north.The slip distribution inversion results indicate that the depth of the slip peak is 13 km,the corresponding maximum slip momentum is 0.67 m,the seismic moment is 1.10×10^(19)N·m and the corresponding moment magnitude is MW6.66.The results above are consistent with the research results of seismology.

Self-propelled Leidenfrost droplets on femtosecond-laser-induced surface with periodic hydrophobicity gradient

The controllable transfer of droplets on the surface of objects has a wide application prospect in the fields of microfluidic devices,fog collection and so on.The Leidenfrost effect can be utilized to significantly reduce motion resistance.However,the use of 3D structures limits the widespread application of self-propulsion based on Leidenfrost droplets in microelectromechanical system.To manipulate Leidenfrost droplets,it is necessary to create 2D or quasi-2D geometries.In this study,femtosecond laser is applied to fabricate a surface with periodic hydrophobicity gradient(SPHG),enabling directional self-propulsion of Leidenfrost droplets.Flow field analysis within the Leidenfrost droplets reveals that the vapor layer between the droplets and the hot surface can be modulated by the SPHG,resulting in directional propulsion of the inner gas.The viscous force between the gas and liquid then drives the droplet to move.

Seedling potential of trees species along the elevational gradient in temperate hill forest of central Nepal

Regeneration status of tree species along elevation gradient in temperate hill forest was not understood greatly.Present research examined the tree diversity and its regeneration patterns along an elevation gradient in temperate hill forest,central Nepal.Data were collected from 300 sample plots within vertical elevation bands of 10,ranging from 1365 to 2450 m asl.A random sampling method was used for data collection in three seasons,winter,pre-monsoon and post monsoon seasons.Diameter at breast height(DBH)was used to broadly categorize the plant individual into trees,saplings and seedlings.The tree species richness ranged from 12 to 25 with density of 350 to 1200 individuals per hectare.Species richness of tree and sapling showed statistically significant unimodal pattern,which peaked at mid-elevation.Elevation showed a strong and positive linear correlation with the seedling density(Deviance=0.99,p<0.001)and a significant hump-shaped relationship with sapling density(Deviance=0.95,p<0.001).Similarly,elevations showed a statistically significant negative hump-shaped relationship with all trees,saplings and seedling stages(Deviances=0.89,0.87 and 0.57).The highest values of the Shannon-Wiener index and the lowest value of the Simpson index were found at mid-elevation for all growth forms.Nearly 92%of tree species were found at regenerating stage;49%in a good renewal regeneration status,32%in fair renewal regeneration,and 11%at a poor regenerating condition.Nevertheless,4%of tree species were reported as non-regenerating stages and 4%were newly introduced species.Hence,the regeneration status of the study area was considered fairly well since sapling(78.5%)>seedling(10.6%)≤mature(10.9%).Among tested environmental variables,elevation and annual mean rainfall were the most influential factors in the regeneration of tree species.

Climate-growth relationships of Pinus tabuliformis along an altitudinal gradient on Baiyunshan Mountain,Central China

A set of standard chronologies for tree-ring width(TRW),earlywood width(EWW)and latewood width(LWW)in Pinus tabuliformis Carr.along an altitudi-nal gradient(1450,1400,and 1350 m a.s.l.)on Baiyunshan Mountain,Central China to analyze the effect of varying temperature and precipitation on growth along the gradi-ent.Correlation analyses showed that at all three altitudes and the TRW and EWW chronologies generally had signifi-cant negative correlations with mean and maximum tem-peratures in the current April and May and with minimum temperatures in the prior July and August,but significant positive correlations with precipitation in the current May.Correlations were generally significantly negative between LWW chronologies and all temperatures in the prior July and August,indicating that the prior summer temperature had a strong lag effect on the growth of P.tabuliformis that increased with altitude.The correlation with the standard-ized precipitation evapotranspiration index(SPEI)confirmed that wet conditions in the current May promoted growth of TR and EW at all altitudes.Significant altitudinal differences were also found;at 1400 m,there were significant positive correlations between EWW chronologies and SPEI in the current April and significant negative correlations between LWW chronologies and SPEI in the current September,but these correlations were not significant at 1450 m.At 1350 m,there were also significant negative correlations between the TRW and the EWW chronologies and SPEI in the prior October and the current July and between LWW chronology and SPEI in the current August,but these cor-relations were not significant at 1400 m.Moving correlation results showed a stable response of EWW in relation to the SPEI in the current May at all three altitudes and of LWW to maximum temperature in the prior July-August at 1400 m from 2002 to 2018.The EWW chronology at 1400 m and the LWW chronology at 1450 m were identified as more suitable for climate reconstruction.These results provide a strong scientific basis for forest management decisions and climate reconstructions in Central China.

Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.

Gyro-Landau-fluid simulations of impurity effects on ion temperature gradient driven turbulence transport

The effects of impurities on ion temperature gradient(ITG)driven turbulence transport in tokamak core plasmas are investigated numerically via global simulations of microturbulence with carbon impurities and adiabatic electrons.The simulations use an extended fluid code(ExFC)based on a four-field gyro-Landau-fluid(GLF)model.The multispecies form of the normalized GLF equations is presented,which guarantees the self-consistent evolution of both bulk ions and impurities.With parametric profiles of the cyclone base case,well-benchmarked ExFC is employed to perform simulations focusing on different impurity density profiles.For a fixed temperature profile,it is found that the turbulent heat diffusivity of bulk ions in a quasi-steady state is usually lower than that without impurities,which is contrary to the linear and quasilinear predictions.The evolutions of the temperature gradient and heat diffusivity exhibit a fast relaxation process,indicating that the destabilization of the outwardly peaked impurity profile is a transient state response.Furthermore,the impurity effects from different profiles can obviously influence the nonlinear critical temperature gradient,which is likely to be dominated by linear effects.These results suggest that the improvement in plasma confinement could be attributed to the impurities,most likely through adjusting both heat diffusivity and the critical temperature gradient.

Mechanical property of cylindrical sandwich shell with gradient core of entangled wire mesh

To explore the wide-frequency damping and vibration-attenuation performances in the application of aerospace components,the cylindrical sandwich shell structure with a gradient core of entangled wire mesh was proposed in this paper.Firstly,the gradient cores of entangled wire mesh in the axial and radial directions were prepared by using an in-house Numerical Control weaving machine,and the metallurgical connection between skin sheets and the gradient core was performed using vacuum brazing.Secondly,to investigate the mechanical properties of cylindrical sandwich shells with axial or radial gradient cores,quasi-static and dynamic mechanical experiments were carried out.The primary evaluations of mechanical properties include secant stiffness,natural frequency,Specific Energy Absorption(SEA),vibration acceleration level,and so on.The results suggest that the vibration-attenuation performance of the sandwich shell is remarkable when the high-density core layer is at the end of the shell or abuts the inner skin.The axial gradient material has almost no influence on the vibration frequencies of the shell,whereas the vibration frequencies increase dramatically when the high-density core layer approaches the skin.Moreover,compared to the conventional sandwich shells,the proposed functional grading cylindrical sandwich shell exhibits more potential in mass reduction,stiffness designing,and energy dissipation.

Perception Enhanced Deep Deterministic Policy Gradient for Autonomous Driving in Complex Scenarios

Autonomous driving has witnessed rapid advancement;however,ensuring safe and efficient driving in intricate scenarios remains a critical challenge.In particular,traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles,susceptibility to traffic flow bottlenecks,and imperfect data in perceiving environmental information,rendering them a vital issue in the practical application of autonomous driving.To address the traffic challenges,this work focused on complex roundabouts with multi-lane and proposed a Perception EnhancedDeepDeterministic Policy Gradient(PE-DDPG)for AutonomousDriving in the Roundabouts.Specifically,themodel incorporates an enhanced variational autoencoder featuring an integrated spatial attention mechanism alongside the Deep Deterministic Policy Gradient framework,enhancing the vehicle’s capability to comprehend complex roundabout environments and make decisions.Furthermore,the PE-DDPG model combines a dynamic path optimization strategy for roundabout scenarios,effectively mitigating traffic bottlenecks and augmenting throughput efficiency.Extensive experiments were conducted with the collaborative simulation platform of CARLA and SUMO,and the experimental results show that the proposed PE-DDPG outperforms the baseline methods in terms of the convergence capacity of the training process,the smoothness of driving and the traffic efficiency with diverse traffic flow patterns and penetration rates of autonomous vehicles(AVs).Generally,the proposed PE-DDPGmodel could be employed for autonomous driving in complex scenarios with imperfect data.

Spatio-temporal evolution and topographic gradient effect of land use and ecosystem service value in the Lhasa River Basin

The Lhasa River Basin forms an essential human settlement area in the southern part of the Qinghai-Tibet Plateau.This study employed ecosystem service value(ESV)evaluation model,terrain gradient grading,and Geodetector to analyze land use and ESV in the Lhasa River Basin from 1985 to 2020.The findings reveal that:(1)From 1985 to 2020,grassland was the dominant land use.There was a trend of grassland reduction and the expansion of other land types.(2)ESV has increased over the research period(with a total increase of 0.84%),with higher values in the southeast and lower values in the northwest.Grassland contributed the most to ESV,and climate regulation and hydrological regulation were the ecosystem services that contribute the most to ESV.(3)Natural factors like NDVI and altitude,as well as economic factors like population density and distance from roads,influenced the spatial differentiation of ESV,the explanatory power of NDVI reached up to 0.47.The interaction between factors had a greater impact than individual factors.These research results can provide theoretical support for national spatial planning and ecological environment protection in the Lhasa River Basin and other similar areas.

Impacts of proppant distribution on development of tight oil reservoirs with threshold pressure gradient

Field evidence indicates that proppant distribution and threshold pressure gradient have great impacts on well productivity.Aiming at the development of unconventional oil reservoirs in Triassic Chang-7 Unit,Ordos Basin of China,we presented an integrated workflow to investigate how(1)proppant placement in induced fracture and(2)non-linear flow in reservoir matrix would affect well productivity and fluid flow in the reservoir.Compared with our research before(Yue et al.,2020),here we extended this study into the development of multi-stage fractured horizontal wells(MFHWs)with large-scale complicated fracture geometry.The integrated workflow is based on the finite element method and consists of simulation models for proppant-laden fluid flow,fracture flow,and non-linear seepage flow,respectively.Simulation results indicate that the distribution of proppant inside the induced cracks significantly affects the productivity of the MFHW.When we assign an idealized proppant distribution instead of the real distribution,there will be an overestimation of 44.98%in daily oil rate and 30.63%in cumulative oil production after continuous development of 1000 days.Besides,threshold pressure gradient(TPG)also significantly affects the well performance in tight oil reservoirs.If we simply apply linear Darcy’s law to the reservoir matrix,the overall cumulative oil production can be overrated by 77%after 1000 days of development.In general,this research provides new insights into the development of tight oil reservoirs with TPG and meanwhile reveals the significance of proppant distribution and non-linear fluid flow in the production scenario design.

Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

L_(1)-Smooth SVM with Distributed Adaptive Proximal Stochastic Gradient Descent with Momentum for Fast Brain Tumor Detection

Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes.Machine learning models have become key players in automating brain tumor detection.Gradient descent methods are the mainstream algorithms for solving machine learning models.In this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor detection.Firstly,the smooth hinge loss is introduced to be used as the loss function of SVM.It avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent optimization.Secondly,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the model.Finally,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth SVM.Distributed computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of data.The DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core resources.Due to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence speed.From the perspective of loss reduction,DPGD converges faster than PGD.The experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor detection.Frompre-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.