ANALYSES OF RECENT GEOMORPHIC EVOLUTION OF GARGATHY INLET, EASTERN SHORE OF VIRGINIA, U. S. A.

Uargathy Inlet is a small natural tidal inlet in the northern region ol the Virginia Darner island chain. It is 100 m wide with a throat cross-sectional area of 384m2 and an average tidal prism of 6.47 x 106 m3. The inside drainage system is 7.8 km long and 2.2km wide. The main channels comprise 5.8% of the area, shallow lagoons 19.8%, and Spartina marshes 74.4% in 1970. Over the period 1851 - 1989 the inlet narrowed and migrated northward while maintaining a weakening downdrift offset. The nearby barrier island coastline’s rapid retreat (average rate 4.78m /a, 138 years retreat 660 m) was accompanied by back barrier channel and lagoon filling and a decrease in intertidal water volume which was probably the main reason for the entrance narrowing. The northward migration of the inlet was related to the dredging of the Inside Passage (before 1949) and the breaching of southern Metompkin Island (since 1957) connected with the inlet system. This altered the interior tidal circulation and likely shifted

Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI 3DVar, EnKF, and Hybrid En3DVar for the Analysis and Short-Term Forecast of a Supercell Storm Case

Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interpolation ensemble Kalman filter(GSI-EnKF) framework were previously developed and tested with a mesoscale convective system(MCS) case. In this study, such capabilities are further developed to assimilate GOES GLM FED data within the GSI ensemble-variational(EnVar) hybrid data assimilation(DA) framework. The results of assimilating the GLM FED data using 3DVar, and pure En3DVar(PEn3DVar, using 100% ensemble covariance and no static covariance) are compared with those of EnKF/DfEnKF for a supercell storm case. The focus of this study is to validate the correctness and evaluate the performance of the new implementation rather than comparing the performance of FED DA among different DA schemes. Only the results of 3DVar and pEn3DVar are examined and compared with EnKF/DfEnKF. Assimilation of a single FED observation shows that the magnitude and horizontal extent of the analysis increments from PEn3DVar are generally larger than from EnKF, which is mainly caused by using different localization strategies in EnFK/DfEnKF and PEn3DVar as well as the integration limits of the graupel mass in the observation operator. Overall, the forecast performance of PEn3DVar is comparable to EnKF/DfEnKF, suggesting correct implementation.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

Machine learning-guided accelerated discovery of structure-property correlations in lean magnesium alloys for biomedical applications

Magnesium alloys are emerging as promising alternatives to traditional orthopedic implant materials thanks to their biodegradability,biocompatibility,and impressive mechanical characteristics.However,their rapid in-vivo degradation presents challenges,notably in upholding mechanical integrity over time.This study investigates the impact of high-temperature thermal processing on the mechanical and degradation attributes of a lean Mg-Zn-Ca-Mn alloy,ZX10.Utilizing rapid,cost-efficient characterization methods like X-ray diffraction and optical microscopy,we swiftly examine microstructural changes post-thermal treatment.Employing Pearson correlation coefficient analysis,we unveil the relationship between microstructural properties and critical targets(properties):hardness and corrosion resistance.Additionally,leveraging the least absolute shrinkage and selection operator(LASSO),we pinpoint the dominant microstructural factors among closely correlated variables.Our findings underscore the significant role of grain size refinement in strengthening and the predominance of the ternary Ca_(2)Mg_(6)Zn_(3)phase in corrosion behavior.This suggests that achieving an optimal blend of strength and corrosion resistance is attainable through fine grains and reduced concentration of ternary phases.This thorough investigation furnishes valuable insights into the intricate interplay of processing,structure,and properties in magnesium alloys,thereby advancing the development of superior biodegradable implant materials.

Study of hybrid nanofluid flow in a stationary cone-disk system with temperature-dependent fluid properties

Cone-disk systems find frequent use such as conical diffusers,medical devices,various rheometric,and viscosimetry applications.In this study,we investigate the three-dimensional flow of a water-based Ag-Mg O hybrid nanofluid in a static cone-disk system while considering temperature-dependent fluid properties.How the variable fluid properties affect the dynamics and heat transfer features is studied by Reynolds's linearized model for variable viscosity and Chiam's model for variable thermal conductivity.The single-phase nanofluid model is utilized to describe convective heat transfer in hybrid nanofluids,incorporating the experimental data.This model is developed as a coupled system of convective-diffusion equations,encompassing the conservation of momentum and the conservation of thermal energy,in conjunction with an incompressibility condition.A self-similar model is developed by the Lie-group scaling transformations,and the subsequent self-similar equations are then solved numerically.The influence of variable fluid parameters on both swirling and non-swirling flow cases is analyzed.Additionally,the Nusselt number for the disk surface is calculated.It is found that an increase in the temperature-dependent viscosity parameter enhances heat transfer characteristics in the static cone-disk system,while the thermal conductivity parameter has the opposite effect.

Supposition of graphene stacks to estimate the contact resistance and conductivity of nanocomposites

In this study,the effects of stacked nanosheets and the surrounding interphase zone on the resistance of the contact region between nanosheets and the tunneling conductivity of samples are evaluated with developed equations superior to those previously reported.The contact resistance and nanocomposite conductivity are modeled by several influencing factors,including stack properties,interphase depth,tunneling size,and contact diameter.The developed model's accuracy is verified through numerous experimental measurements.To further validate the models and establish correlations between parameters,the effects of all the variables on contact resistance and nanocomposite conductivity are analyzed.Notably,the contact resistance is primarily dependent on the polymer tunnel resistivity,contact area,and tunneling size.The dimensions of the graphene nanosheets significantly influence the conductivity,which ranges from 0 S/m to90 S/m.An increased number of nanosheets in stacks and a larger gap between them enhance the nanocomposite's conductivity.Furthermore,the thicker interphase and smaller tunneling size can lead to higher sample conductivity due to their optimistic effects on the percolation threshold and network efficacy.

Panel Discussion on“Development Trends of Computer Science in the New Era”

At the panel session of the 3rd Global Forum on the Development of Computer Science,attendees had an opportunity to deliberate recent issues affecting computer science departments as a result of the recent growth in the field.6 heads of university computer science departments participated in the discussions,including the moderator,Professor Andrew Yao.The first issue was how universities are managing the growing number of applicants in addition to swelling class sizes.Several approaches were suggested,including increasing faculty hiring,implementing scalable teaching tools,and working closer with other departments through degree programs that integrate computer science with other fields.The second issue was about the position and role of computer science within broader science.Participants generally agreed that all fields are increasingly relying on computer science techniques,and that effectively disseminating these techniques to others is a key to unlocking broader scientific progress.

Peacebuilding Is Social Work:From Political Science to Social Psychology

For the longest time,peacemaking and peacekeeping were the only post-factum interventions to resolve armed conflicts usually related to a nation-state’s borders or territory.Peacebuilding has its origins in sociology(Galtung,1969;1975)and is used today as a preferred concept in matters of conflict.However,this paper explores why peacebuilding,as the Secretary General of the United Nations advocates in A New Agenda for Peace,must become a nonlinear and contextual process that promotes the prevention of conflict and invites a transformative approach to addressing the linkages between peace,security,and climate.Furthermore,this paper advocates that peacebuilding grounded in social psychology and social anthropology will bring about transformative outcomes as it will build relationships at the community level and become a preventive tool to address incipient tensions within the community.Peacebuilding as social work will benefit the community and lay the necessary foundations for a sustainable future.Social workers are equipped to assess,analyze,and solve problems.Their capacity to do ongoing social diagnosis is a critical tool to prevent skirmishes degenerating into conflicts.Social work could be the much-needed resource to further develop theory and practice that contributes to active peacebuilding.

Multimodal comparison of plasma proteins associated with blood-brain barrier impairment in Alzheimer’s disease

Background:Vascular impairment is one of the major contributors to dementia.We aimed to identify blood biomarkers suggestive of potential impairment of the blood-brain barrier(BBB)in subjects with Alzheimer’s disease(AD).Methods:We used administrative data from the VA Informatics and Computing Infrastructure Resource Center to study both inpatients and outpatients with AD.Plasma samples from healthy control and AD individuals were analyzed using enzyme-linked immunosorbent assay and proteomics approaches to identify differentially expressed proteins.Bioinformatic analysis was applied to explore significantly enriched pathways.Results:In the same cohort of patients with AD,we found twice number of subjects with cerebral amyloid angiopathy in the two-year period after the onset of AD,compared to the number of subjects with cerebral amyloid angiopathy in the two-year period prior to AD onset.Different pathways related to BBB,like cell adhesion,extracellular matrix organization and Wnt signaling,were activated and differentially expressed proteins such as ADAM22,PDGFR-α,DKK-4,Neucrin and RSOP-1 were identified.Moreover,matrix metalloproteinase-9,which is implicated in causing degradation of basal lamina and BBB disruption,was significantly increased in the plasma of AD patients.Conclusions:Alteration of proteins found in AD subjects could provide new insights into biomarkers regulating permeability and BBB integrity.

Multi-scenario Simulation for 2060 and Driving Factors of the Eco-spatial Carbon Sink in the Beibu Gulf Urban Agglomeration, China

Since China announced its goal of becoming carbon-neutral by 2060, carbon neutrality has become a major target in the development of China's urban agglomerations. This study applied the Future Land Use Simulation(FLUS) model to predict the land use pattern of the ecological space of the Beibu Gulf urban agglomeration, in 2060 under ecological priority, agricultural priority and urbanized priority scenarios. The Integrated Valuation of Ecosystem Services and Trade-offs(In VEST) model was employed to analyse the spatial changes in ecological space carbon storage in each scenario from 2020 to 2060. Then, this study used a Geographically Weighted Regression(GWR) model to determine the main driving factors that influence the changes in land carbon sinking capacity. The results of the study can be summarised as follows: firstly, the agricultural and ecological priority scenarios will achieve balanced urban expansion and environmental protection of resources in an ecological space. The urbanized priority scenario will reduce the carbon sinking capacity. Among the simulation scenarios for 2060, carbon storage in the urbanized priority scenario will decrease by 112.26 × 10^(6) t compared with that for 2020 and the average carbon density will decrease by 0.96 kg/m^(2) compared with that for 2020. Carbon storage in the agricultural priority scenario will increase by 84.11 × 10^(6) t, and the average carbon density will decrease by 0.72 kg/m^(2). Carbon storage in the ecological priority scenario will increase by 3.03 × 10^(6) t, and the average carbon density will increase by 0.03 kg/m^(2). Under the premise that the population of the town will increases continuously, the ecological priority development approach may be a wise choice.Secondly, slope, distance to river and elevation are the most important factors that influence the carbon sink pattern of the ecological space in the Beibu Gulf urban agglomeration, followed by GDP, population density, slope direction and distance to traffic infrastructure.At the same time, urban space expansion is the main cause of the changes of this natural factors. Thirdly, the decreasing trend of ecological space is difficult to reverse, so reasonable land use policy to curb the spatial expansion of cities need to be made.

A quantitative microplasticity-based approach to rationalize the poor strengthening response of polycrystalline Mg alloys

This work aims to understand the inefficiency of nanoprecipitates to strengthen a weakly textured,polycrystalline Mg-Gd-Y-Zr alloy.An experimental micromechanical approach consisting on micropillar compression combined with analytical electron microscopy is put in place to analyze the effect of nanoprecipitation on soft and hard basal slip and twinning in individual grains with different orientations.This study shows that,in grains that are favorably oriented for basal slip(“soft”basal slip),aging leads to extreme localization due to the ability of basal dislocations to shear the nanoparticles,resulting overall in the softening of basal systems.Additionally,in grains in which the c-axis is almost perpendicular to the compression axis,prismatic slip dominates deformation in the solid solution state and nanoprecipitation favors twinning due to the concomitant lattice solute depletion.Finally,in grains oriented with their c-axis making an angle of about 5-7°with respect to the compression axis,which deform mainly by“hard”basal slip,precipitation leads to the strengthening of basal systems in the absence of obvious localization.This work reveals that the poor hardening response of the polycrystalline alloy is related to the capability of basal dislocations to shear the nanoparticles,in the absence of Orowan looping events,and to the associated basal slip localization.

Agricultural Water Footprint of Southern Highbush Blueberry Produced Commercially with Drip Irrigation and Sprinkler Frost Protection

A study was conducted from 2010 to 2017 to determine the water footprint for producing blueberries in the Entre Ríos province of Argentina. Three cultivars of southern highbush blueberry (hybrid cross of Vaccinium sp.) were evaluated in the study, including “Star”, “Emerald”, and “Snowchaser”. In each case, the plants were irrigated by drip and protected from frost using overhead sprinklers. Water requirements for irrigation and frost protection varied among the cultivars due to differences in the timing of flowering and fruit development. The annual water footprint for fruit production in each cultivar is expressed in units of cubic meters of water used to produce one ton of fresh fruit and ranged from 212 - 578 m3∙t−1 for “Star”, 296 - 985 m3∙t−1 for “Emerald”, and 536 - 4066 m3∙t−1 for “Snowchaser”. “Snowchaser” flowered earlier than the other cultivars and, therefore, needed more water for frost protection. “Star”, on the other hand, ripened the latest among the cultivars and required little to no water for frost protection. Frost protection required a minimum of 30 m3∙h−1 of water per hectare and in addition to drip irrigation was a major component of the water footprint.

Zn-doped nickel iron(oxy)hydroxide nanocubes passivated by polyanions with high catalytic activity and corrosion resistance for seawater oxidation

Electrochemical water splitting to produce hydrogen fuel is a promising renewable energy-conversion technique.Large-scale electrolysis of freshwater may deplete water resources and cause water scarcity worldwide.Thus,seawater electrolysis is a potential solution to the future energy and water crisis.In seawater electrolysis,it is critical to develop cost-effective electrocatalysts to split seawater without chloride corrosion.Herein,we present zinc-doped nickel iron(oxy)hydroxide nanocubes passivated by negatively charged polyanions(NFZ-PBA-S)that exhibits outstanding catalytic activity,stability,and selectivity for seawater oxidation.Zn dopants and polyanion-rich passivated surface layers in NFZ-PBA-S could effectively repel chlorine ions and enhance corrosion resistance,enabling its excellent catalytic activity and stability for seawater oxidation.

Peri-Net-Pro: the neural processes with quantified uncertainty for crack patterns

This paper develops a deep learning tool based on neural processes(NPs)called the Peri-Net-Pro,to predict the crack patterns in a moving disk and classifies them according to the classification modes with quantified uncertainties.In particular,image classification and regression studies are conducted by means of convolutional neural networks(CNNs)and NPs.First,the amount and quality of the data are enhanced by using peridynamics to theoretically compensate for the problems of the finite element method(FEM)in generating crack pattern images.Second,case studies are conducted with the prototype microelastic brittle(PMB),linear peridynamic solid(LPS),and viscoelastic solid(VES)models obtained by using the peridynamic theory.The case studies are performed to classify the images by using CNNs and determine the suitability of the PMB,LBS,and VES models.Finally,a regression analysis is performed on the crack pattern images with NPs to predict the crack patterns.The regression analysis results confirm that the variance decreases when the number of epochs increases by using the NPs.The training results gradually improve,and the variance ranges decrease to less than 0.035.The main finding of this study is that the NPs enable accurate predictions,even with missing or insufficient training data.The results demonstrate that if the context points are set to the 10th,100th,300th,and 784th,the training information is deliberately omitted for the context points of the 10th,100th,and 300th,and the predictions are different when the context points are significantly lower.However,the comparison of the results of the 100th and 784th context points shows that the predicted results are similar because of the Gaussian processes in the NPs.Therefore,if the NPs are employed for training,the missing information of the training data can be supplemented to predict the results.

Gaussian process hydrodynamics

We present a Gaussian process(GP)approach,called Gaussian process hydrodynamics(GPH)for approximating the solution to the Euler and Navier-Stokes(NS)equations.Similar to smoothed particle hydrodynamics(SPH),GPH is a Lagrangian particle-based approach that involves the tracking of a finite number of particles transported by a flow.However,these particles do not represent mollified particles of matter but carry discrete/partial information about the continuous flow.Closure is achieved by placing a divergence-free GP priorξon the velocity field and conditioning it on the vorticity at the particle locations.Known physics(e.g.,the Richardson cascade and velocityincrement power laws)is incorporated into the GP prior by using physics-informed additive kernels.This is equivalent to expressingξas a sum of independent GPsξl,which we call modes,acting at different scales(each modeξlself-activates to represent the formation of eddies at the corresponding scales).This approach enables a quantitative analysis of the Richardson cascade through the analysis of the activation of these modes,and enables us to analyze coarse-grain turbulence statistically rather than deterministically.Because GPH is formulated by using the vorticity equations,it does not require solving a pressure equation.By enforcing incompressibility and fluid-structure boundary conditions through the selection of a kernel,GPH requires significantly fewer particles than SPH.Because GPH has a natural probabilistic interpretation,the numerical results come with uncertainty estimates,enabling their incorporation into an uncertainty quantification(UQ)pipeline and adding/removing particles(quanta of information)in an adapted manner.The proposed approach is suitable for analysis because it inherits the complexity of state-of-the-art solvers for dense kernel matrices and results in a natural definition of turbulence as information loss.Numerical experiments support the importance of selecting physics-informed kernels and illustrate the major impact of such kernels on the accuracy and stability.Because the proposed approach uses a Bayesian interpretation,it naturally enables data assimilation and predictions and estimations by mixing simulation data and experimental data.

Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures

Molecular dynamics(MD)has served as a powerful tool for designing materials with reduced reliance on laboratory testing.However,the use of MD directly to treat the deformation and failure of materials at the mesoscale is still largely beyond reach.In this work,we propose a learning framework to extract a peridynamics model as a mesoscale continuum surrogate from MD simulated material fracture data sets.Firstly,we develop a novel coarse-graining method,to automatically handle the material fracture and its corresponding discontinuities in the MD displacement data sets.Inspired by the weighted essentially non-oscillatory(WENO)scheme,the key idea lies at an adaptive procedure to automatically choose the locally smoothest stencil,then reconstruct the coarse-grained material displacement field as the piecewise smooth solutions containing discontinuities.Then,based on the coarse-grained MD data,a two-phase optimizationbased learning approach is proposed to infer the optimal peridynamics model with damage criterion.In the first phase,we identify the optimal nonlocal kernel function from the data sets without material damage to capture the material stiffness properties.Then,in the second phase,the material damage criterion is learnt as a smoothed step function from the data with fractures.As a result,a peridynamics surrogate is obtained.As a continuum model,our peridynamics surrogate model can be employed in further prediction tasks with different grid resolutions from training,and hence allows for substantial reductions in computational cost compared with MD.We illustrate the efficacy of the proposed approach with several numerical tests for the dynamic crack propagation problem in a single-layer graphene.Our tests show that the proposed data-driven model is robust and generalizable,in the sense that it is capable of modeling the initialization and growth of fractures under discretization and loading settings that are different from the ones used during training.

Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics

Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical functions.However,material identification is a challenging task,especially when the characteristic of the material is highly nonlinear in nature,as is common in biological tissue.In this work,we identify unknown material properties in continuum solid mechanics via physics-informed neural networks(PINNs).To improve the accuracy and efficiency of PINNs,we develop efficient strategies to nonuniformly sample observational data.We also investigate different approaches to enforce Dirichlet-type boundary conditions(BCs)as soft or hard constraints.Finally,we apply the proposed methods to a diverse set of time-dependent and time-independent solid mechanic examples that span linear elastic and hyperelastic material space.The estimated material parameters achieve relative errors of less than 1%.As such,this work is relevant to diverse applications,including optimizing structural integrity and developing novel materials.

Addressing nonresponse bias in forest inventory change estimation using response homogeneity classifications

Estimating amounts of change in forest resources over time is a key function of most national forest inventories(NFI). As this information is used broadly for many management and policy purposes, it is imperative that accurate estimations are made from the survey sample. Robust sampling designs are often used to help ensure representation of the population, but often the full sample is unrealized due to hazardous conditions or possibly lack of land access permission. Potentially, bias may be imparted to the sample if the nonresponse is nonrandom with respect to forest characteristics, which becomes more difficult to assess for change estimation methods that require measurements of the same sample plots at two points in time, i.e., remeasurement. To examine potential nonresponse bias in change estimates, two synthetic populations were constructed: 1) a typical NFI population consisting of both forest and nonforest plots, and 2) a population that mimics a large catastrophic disturbance event within a forested population. Comparisons of estimates under various nonresponse scenarios were made using a standard implementation of post-stratified estimation as well as an alternative approach that groups plots having similar response probabilities(response homogeneity). When using the post-stratified estimators, the amount of change was overestimated for the NFI population and was underestimated for the disturbance population, whereas the response homogeneity approach produced nearly unbiased estimates under the assumption of equal response probability within groups. These outcomes suggest that formal strategies may be needed to obtain accurate change estimates in the presence of nonrandom nonresponse.

Deep convolutional Ritz method: parametric PDE surrogates without labeled data

The parametric surrogate models for partial differential equations(PDEs)are a necessary component for many applications in computational sciences,and the convolutional neural networks(CNNs)have proven to be an excellent tool to generate these surrogates when parametric fields are present.CNNs are commonly trained on labeled data based on one-to-one sets of parameter-input and PDE-output fields.Recently,residual-based deep convolutional physics-informed neural network(DCPINN)solvers for parametric PDEs have been proposed to build surrogates without the need for labeled data.These allow for the generation of surrogates without an expensive offline-phase.In this work,we present an alternative formulation termed deep convolutional Ritz method(DCRM)as a parametric PDE solver.The approach is based on the minimization of energy functionals,which lowers the order of the differential operators compared to residualbased methods.Based on studies involving the Poisson equation with a spatially parameterized source term and boundary conditions,we find that CNNs trained on labeled data outperform DCPINNs in convergence speed and generalization abilities.The surrogates generated from the DCRM,however,converge significantly faster than their DCPINN counterparts,and prove to generalize faster and better than the surrogates obtained from both CNNs trained on labeled data and DCPINNs.This hints that the DCRM could make PDE solution surrogates trained without labeled data possibly.

A dive into spectral inference networks: improved algorithms for self-supervised learning of continuous spectral representations

We propose a self-supervising learning framework for finding the dominant eigenfunction-eigenvalue pairs of linear and self-adjoint operators.We represent target eigenfunctions with coordinate-based neural networks and employ the Fourier positional encodings to enable the approximation of high-frequency modes.We formulate a self-supervised training objective for spectral learning and propose a novel regularization mechanism to ensure that the network finds the exact eigenfunctions instead of a space spanned by the eigenfunctions.Furthermore,we investigate the effect of weight normalization as a mechanism to alleviate the risk of recovering linear dependent modes,allowing us to accurately recover a large number of eigenpairs.The effectiveness of our methods is demonstrated across a collection of representative benchmarks including both local and non-local diffusion operators,as well as high-dimensional time-series data from a video sequence.Our results indicate that the present algorithm can outperform competing approaches in terms of both approximation accuracy and computational cost.