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.

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.

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.

Development of Extraction and Detection Method for a Chemotherapeutic Drug with Phenytoin in Biological Samples

Dexamethasone is classified as a corticosteroid and is commonly used among cancer patients to decrease the amount of swelling around the tumor. Among patients with cancer, in particular brain tumors, seizures can become a daily routine in their everyday lives. To counteract the seizures, an antiepileptic drug such as phenytoin is administered to act as an anticonvulsant. Phenytoin and dexamethasone are frequently administrated concurrently to brain cancer patients. A previous study has shown that phenytoin serum concentration decreases when administrated concurrently with dexamethasone. Thus, it is important to monitor the concentration of these two drugs in biological samples to ensure that the proper dosages are administrated to the patients. This study aims to develop an effective extraction and detection method for dexamethasone and phenytoin. A reverse-phase high-performance liquid chromatography (HPLC) method with UV/Vis detection has been developed to separate phenytoin and dexamethasone at 219 nm and 241 nm respectively from urine samples. The mobile phase consists of a mixture of 0.01 M KH2PO4, acetonitrile, and methanol adjusted to pH 5.6 (48:32:20) and is pumped at a flow rate of 1.0 mL/min. Calibration curves were prepared for phenytoin and dexamethasone (r2 > 0.99). An efficient solid-phase extraction (SPE) method for the extraction of dexamethasone and phenytoin from urine samples was developed with the use of C-18 cartridges. The percent recovery for phenytoin and dexamethasone is 95.4% (RSD = 1.15%) and 81.1% (RSD = 3.56%) respectively.

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.

Upcycling municipal solid waste to sustainable hydrogen via two-stage gasification-reforming

As global municipal solid waste(MSW)quantities continue to escalate,serious socio-environmental challenges arise,necessitating innovative solutions.Waste-to-hydrogen(WTH)via two-stage gasification-reforming(TSGR)presents an emergent technology for MSW upcycling,offering to ease waste management burdens and bolster the burgeoning hydrogen economy.Despite early initiatives to advance TSGR technology,a cohesive and critical analysis of cutting-edge knowledge and strategies to enhance hydrogen production remains lacking.This review aggregates literature on MSW upcycling to hydrogen via TSGR,with a focus on optimizing process control and catalytic efficiency.It underscores technological avenues to augment hydrogen output,curtail catalyst costs,and refine system performance.Particularly,the review illuminates the potential for integrating chemical and calcium looping into TSGR processes,identifying opportunities,and pinpointing challenges.The review concludes with a summary of the current state of techno-economic analysis for this technology,presenting outstanding challenges and future research directions,with the ultimate goal of transitioning WTH from theoretical to practical application.

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.

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.

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.

Theoretical and experimental investigations on an X-shaped vibration isolator with active controlled variable stiffness

A novel X-shaped variable stiffness vibration isolator(X-VSVI)is proposed.The Runge-Kutta method,harmonic balance method,and wavelet transform spectra are introduced to evaluate the performance of the X-VSVI under various excitations.The layer number,the installation angle of the X-shaped structure,the stiffness,and the active control parameters are systematically analyzed.In addition,a prototype of the X-VSVI is manufactured,and vibration tests are carried out.The results show that the proposed X-VSVI has a superior adaptability to that of a traditional X-shaped mechanism,and shows excellent vibration isolation performance in response to different amplitudes and forms of excitations.Moreover,the vibration isolation efficiency of the device can be improved by appropriate adjustment of parameters.

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.

Preface: machine-learning approaches for computational mechanics

Machine-learning(ML) approaches have gained significant attention in recent years for their potential to revolutionize various scientific research fields,including computational mechanics,materials science,and biomedical engineering.In the field of computational mechanics,various neural network models such as feedforward neural networks,convolutional neural networks.

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.

Nexus between air travel and environmental degradation in BRICS Countries:A tourism perspective

International tourism is highly dependent on air travel,which enhances destination countries’economic growth and living standards while causing environmental degradation.This study investigates the effect of air travel on environmental degradation in BRICS nations.The study used feasible generalized least squares(FGLS)to evaluate the environmental Kuznets curve using data spanning 1995 to 2018.The study finds evidence of N-shaped EKC in the economies of the BRICS nations.We also find a positive relationship between tourism,urbanization,and environmental degradation(as measured by CO_(2) and SO_(2) emissions from civil aviation)and a negative relationship between economic development and environmental degradation.To achieve sustainable growth and environmental protection and promote green technology,the BRICS governments should devise policies that address the aviation industry’s short-and long-term needs.

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.

Modelling,Validation,and Calculations of Stratospheric Ozone Dynamics and Latitudinal Changes

This paper presents an engineering system approach of 2-D cylindrical model of mass balance calculations with convection,diffusion,and all potential photolysis,ozone generating and depleting chemical reactions considered.This model was developed,validated,and tested under different conditions for the stratospheric ozone.The calculated ozone concentrations and profile in the stratosphere at both the Equator and mid-latitudinal location of 40°S were found to exhibit a similar and close profile and peak value of the published measured data.The discrepancy between the calculations and measurements for the average ozone concentration was shown to be less than 1%and the variation of distributions to be less than 19%.The latitudinal changes of ozone concentrations,distribution,and peak of the layer were found to shift from 9.41 ppm at mid-altitude of z=30 km at the Equator,to 7.81 ppm at z=34.5 km at 40°S,to 5.78 ppm at higher altitude z=39 km at the South Pole.The total ozone abundances at strategic latitudes at 0°S,20°S,40°S,60°S,and 90°S,were found to remain stable and not much changed,from 305 DU to 335 DU,except a smaller value of 288 DU at the South Pole.The possible explanations of ozone profile change and peak shifting as affected by solar/UV radiation,latitudinal locations,and ozone-depleting reactions were discussed and elaborated.The 2-D ozone Model presented in this paper is a robust,efficient,executable,and validated model for studying the complex ozone phenomena in the stratosphere.

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.

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.