Detecting confined fluid behavior by SFA: Past, present, and future

Since Israelachvili and co-workers in 1970s first developed apparatuses to detect van der Waals forces of molecularly smooth mica surfaces confined down to 1.5 nm, these advanced surface techniques have been vastly explored and extended to detect a variety of intermolecular forces in fluids and biomolecular systems [1]. The Surface Forces Apparatus(SFA) is one representative, which has been widely applied in the cutting-edge fields. The most fascinating advantage of SFA is on its capability to detect the force at atomic level。

Evolution of nonmetallic inclusions in 80-t 9CrMoCoB large-scale ingots during electroslag remelting process

In combination with theoretical calculations,experiments were conducted to investigate the evolution behavior of nonmetallic inclusions(NMIs)during the manufacture of large-scale heat-resistant steel ingots using 9CrMoCoB heat-resistant steel and CaF_(2)–CaO–Al_(2)O_(3)–SiO_(2)–B_(2)O_(3)electroslag remelting(ESR)-type slag in an 80-t industrial ESR furnace.The main types of NMI in the consumable electrode comprised pure alumina,a multiphase oxide consisting of an Al_(2)O_(3)core and liquid CaO–Al_(2)O_(3)–SiO_(2)–MnO shell,and M_(23)C_(6)carbides with an MnS core.The Al_(2)O_(3)and MnS inclusions had higher precipitation temperatures than the M_(23)C_(6)-type carbide under equilibrium and nonequilibrium solidification processes.Therefore,inclusions can act as nucleation sites for carbide layer precipitation.The ESR process completely removed the liquid CaO–Al_(2)O_(3)–SiO_(2)–MnO oxide and MnS inclusion with a carbide shell,and only the Al_(2)O_(3)inclusions and Al_(2)O_(3)core with a carbide shell occupied the remelted ingot.The M_(23)C_(6)-type carbides in steel were determined as Cr_(23)C_(6)based on the analysis of transmission electron microscopy results.The substitution of Cr with W,Fe,or/and Mo in the Cr_(23)C_(6)lattice caused slight changes in the lattice parameter of the Cr_(23)C_(6)carbide.Therefore,Cr_(21.34)Fe_(1.66)C_(6),(Cr_(19)W_(4)C_(6),Cr_(18.4)Mo_(4.6)C_(6),and Cr_(16)Fe_(5)Mo_(2)C_(6)can match the fraction pattern of Cr_(23)C_(6)carbide.The Al_(2)O_(3)inclusions in the remelted ingot formed due to the reduction of CaO,SiO_(2),and MnO components in the liquid inclusion.The increased Al content in liquid steel or the higher supersaturation degree of Al_(2)O_(3)precipitation in the remelted ingot than that in the electrode can be attributed to the evaporation of CaF_(2)and the increase in CaO content in the ESR-type slag.

Preliminary Report on the Anurans of Mount Hilong-hilong, Agusan Del Norte, Eastern Mindanao, Philippines

Mount Hilong-hilong is a key biodiversity area, spanning several municipalities in the provinces of the Caraga Region （Agusan del Norte, Agusan del Sur, Surigao del Norte and Surigao del Sur）, northeastern Mindanao Island, southern Philippines. The Hilong-hilong massif remains one of the most signiifcant forested areas in Mindanao, threatened with habitat modification （forest removal, degradation） and other anthropogenic disturbances related to renewable resource extraction. Amphibians are key indicator species for environmental quality and are useful focal taxa for conservation efforts. Relying on historical museum database information and new survey work on Mount Hilong-hilong, we provide species accounts and describe microhabitat preferences of the anurans （frogs and toads） present in the area. Twenty-seven species representing seven anuran families were studied in detail at elevations between 700 to 1300 meters above sea level; 16 of these species are Mindanao faunal region endemics. Qualitative overlap in microhabitat use was observed, suggesting that, for the species recorded, intact forest may ensure species persistence to some levels of anthropogenic disturbance. A more extensive herpetofaunal survey is needed to fully estimate the herpetofaunal diversity of Mount Hilong-hilong. Because amphibians represent ifne-scale indicators of environmental quality and microendemism, we recommend appropriate ifne-scaled regional strategies geared towards the conservation of amphibians in the Caraga area, northeast Mindanao Island.

Determination of Wood Moisture Properties Using a CT-Scanner in a Controlled High-Temperature Environment

Theaim of the present work wasto analyze moisture flow and moisture content data for high-temperature drying by usingan advanced image- processing algorithm.Since wood starts to shrink below the fibre saturation point during drying, the size and shape of wood will change. The dry wood image was thoroughly transformed to the shape of the wet wood image prior to calculating the dry weight moisture content. The use of the image- processing algorithm for the dry weight moisture content on density data from the CT-scanning during drying in a controlled high-temperature environment showed that this method is a powerful tool for analyzing the moisture flow inside the wood piece. Furthermore, the new CT-scanner together with the climate chamber gave unique results, as it has not been possible to study high-temperature drying with this method before.

Hybrid Surface Mesh Adaptation for Climate Modeling

Solution-driven mesh adaptation is becoming quite popular for spatial error control in the numerical simulation of complex computational physics applications,such as climate modeling.Typically,spatial adaptation is achieved by element subdivision (h adaptation) with a primary goal of resolving the local length scales of interest.A sec- ond,less-popular method of spatial adaptivity is called'mesh motion'(r adaptation); the smooth repositioning of mesh node points aimed at resizing existing elements to capture the local length scales.This paper proposes an adaptation method based on a combination of both element subdivision and node point repositioning (rh adaptation). By combining these two methods using the notion of a mobility function,the proposed approach seeks to increase the flexibility and extensibility of mesh motion algorithms while providing a somewhat smoother transition between refined regions than is pro- duced by element subdivision alone.Further,in an attempt to support the requirements of a very general class of climate simulation applications,the proposed method is de- signed to accommodate unstructured,polygonal mesh topologies in addition to the most popular mesh types.

A Data Analysis Framework for Earth System Simulation within an <i>In-Situ</i>Infrastructure

This paper presents a generic procedure to implement a scalable and high performance data analysis framework for large-scale scientific simulation within an in-situ infrastructure. It demonstrates a unique capability for global Earth system simulations using advanced computing technologies (i.e., automated code analysis and instrumentation), in-situ infrastructure (i.e., ADIOS) and big data analysis engines (i.e., SciKit-learn). This paper also includes a useful case that analyzes a globe Earth System simulations with the integration of scalable in-situ infrastructure and advanced data processing package. The in-situ data analysis framework can provides new insights on scientific discoveries in multiscale modeling paradigms.

Development of Cold-Chain for Postharvest Loss Reduction of Chinese Cabbage(Brassica campestris L.ssp.Pekinensis)

Cold-chain is a well-known method for reducing postharvest losses and low-cost cooling technology has not previously been tested as part of postharvest handling in Cambodia.The objective of this study was to measure postharvest loss,quality changes,and safety concerns of Chinese cabbage(Brassica campestris L.ssp.pekinensis),during transportation using a cold-chain and compared to current farmers’employing ambient-chain practices.The quality and safety of Chinese cabbage were further evaluated by using ambient storage and Coolbot-powered cold chamber storage with and without modified atmosphere packaging(MAP).The samples were transported from farm sources in Battambang Province to a Phnom Penh specialty wholesale market.Postharvest loss was evaluated by measuring weight loss and visual quality measurements,in addition to various physiochemical and nutritional quality measurements.In addition,food safety was evaluated by quantifying total coliforms and Enterobacteriaceae,as well as the Salmonella spcies,and Escherichia coli.The results revealed that the cold-chain avoided postharvest loss,as indicated by produce weight gain of 14%on market arrival due to rehydration while inside the ice box during transport.In contrast,the traditional practice of ambient transport(28-31°C,62-78%relative humidity)resulted in very high postharvest loss,comprising 11%weight loss and 10%visual quality loss,for a total loss of 21%.Moreover,leaf yellowing found no marked influence on shelf life as L*,a*and b*values did not greatly differ with treatment.The total soluble solids(TSS),titratable acidity(TA),pH and vitamin C content were not significantly affected during storage.Food safety indicators(coliforms,Enterobacteriaceae,Salmonella and Escherichia coli)were lower in cold-chain storage than ambient-chain with lower counts of coliform bacteria,Enterobacteriaceae,and Salmonella spp.than traditionally handled produce.Escherichia coli was detected only in cold-chain produce.MAP had no effect on these food safety indicators.

Turbulence Modelling of a Single-Phase Flow Cyclone Gasifier

The current work aims to make a foundation for an engineering design of a cyclone gasifier to be able not only to predict its flow field with a suitable accuracy but also to investigate a large number of design alternatives with limited computer resources. A good single-phase flow model that can form the basis in an Euler-Lagrange model for multi-phase flow is also necessary?for modelling the reacting flow inside a cyclone gasifier. The present paper provides an objective comparison between several popular turbulence modelling options including standard k-ε and SST with curvature corrections, SSG-RSM and LES Smagorinsky models, for the single-phase flow inside cyclone separators/gasifiers that can serve as a guide for further work on the reacting multi-phase flow inside cyclone gasifiers and similar devices. A detailed comparison between the models and experimental data for the mean velocity and fluctuating parts of the velocity profiles are presented. Furthermore, the capabilities of the turbulence models to capture the physical phenomena present in a cyclone gasifier that?affects the design process are investigated.

The physics and astrophysics of Type Ia supernova explosions

Asupernowt is a transient astronomical event of spectacular peak brightness that is associ-a ted with an exploding star. Supernovae exhibit a range of observational characteristics that historically h^ts led to a rich set of classsifications and sub-clmssifications. Despite the complex- ity of the obscrwttionally-based supernova, taxonomy, we now believe that all supernovae are caused by just one of two basic inechanisms： （i） the collapse of the core of a inassive star late in its litb, or （ii） a runaway thermonuclear explosion in a white dwarf. The former is terlned tile cor^-collapsc mechanism, and is powered by gravitational energy

On the Bifurcations and Multiple Endemic States of a Single Strain HIV Model Dedicated to Professor Toshikazu Sunada on the Occasion of his 60th Birthday

The dynamics of a single strain HIV model is studied. The basic reproduction number R0 used as a bifurcation parameter shows that the system undergoes transcritical and saddle-node bifurcations. The usual threshold unit value of R0 does not completely determine the eradication of the disease in an HIV infected person. In particular, a sub-threshold value Rc is established which determines the system＇s number of endemic states： multiple if Rc 〈 Ro 〈 1, only one if Rc=Ro = 1, and none if R0 〈 Rc 〈 1.

Benchmarking graph neural networks for materials chemistry

Graph neural networks(GNNs)have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications.To date,a number of successful GNNs have been proposed and demonstrated for systems ranging from crystal stability to electronic property prediction and to surface chemistry and heterogeneous catalysis.However,a consistent benchmark of these models remains lacking,hindering the development and consistent evaluation of new models in the materials field.Here,we present a workflow and testing platform,MatDeepLearn,for quickly and reproducibly assessing and comparing GNNs and other machine learning models.We use this platform to optimize and evaluate a selection of top performing GNNs on several representative datasets in computational materials chemistry.From our investigations we note the importance of hyperparameter selection and find roughly similar performances for the top models once optimized.We identify several strengths in GNNs over conventional models in cases with compositionally diverse datasets and in its overall flexibility with respect to inputs,due to learned rather than defined representations.Meanwhile several weaknesses of GNNs are also observed including high data requirements,and suggestions for further improvement for applications in materials chemistry are discussed.

A SPARSE-GRID METHOD FOR MULTI-DIMENSIONAL BACKWARD STOCHASTIC DIFFERENTIAL EQUATIONS

A sparse-grid method for solving multi-dimensional backward stochastic differential equations （BSDEs） based on a multi-step time discretization scheme [31] is presented. In the multi-dimensional spatial domain, i.e. the Brownian space, the conditional mathe- matical expectations derived from the original equation are approximated using sparse-grid Gauss-Hermite quadrature rule and （adaptive） hierarchical sparse-grid interpolation. Error estimates are proved for the proposed fully-discrete scheme for multi-dimensional BSDEs with certain types of simplified generator functions. Finally, several numerical examples are provided to illustrate the accuracy and efficiency of our scheme.

A bridge for accelerating materials by design

Recent technical advances in the area of nanoscale imaging,spectroscopy and scattering/diffraction have led to unprecedented capabilities for investigating materials structural,dynamical and functional characteristics.In addition,recent advances in computational algorithms and computer capacities that are orders of magnitude larger/faster have enabled large-scale simulations of materials properties starting with nothing but the identity of the atomic species and the basic principles of quantum and statistical mechanics and thermodynamics.Along with these advances,an explosion of high-resolution data has emerged.This confluence of capabilities and rise of big data offer grand opportunities for advancing materials sciences but also introduce several challenges.In this perspective,we identify challenges impeding progress towards advancing materials by design(e.g.,the design/discovery of materials with improved properties/performance),possible solutions and provide examples of scientific issues that can be addressed using a tightly integrated approach where theory and experiments are linked through big-deep data.

Suppressing the liquid product crossover in electrochemical CO_(2)reduction

Coupling electrochemical CO_(2)reduction(CO_(2)R)with a renewable energy source to create high‐value fuels and chemicals is a promising strategy in moving toward a sustainable global energy economy.CO_(2)R liquid products,such as formate,acetate,ethanol,and propanol,offer high volumetric energy density and are more easily stored and transported than their gaseous coun-terparts.However,a significant amount(~30%)of liquid products from electrochemical CO_(2)R in a flow cell reactor cross the ion exchange membrane,leading to the substantial loss of system‐level Faradaic efficiency.This severe crossover of the liquid product has—until now—received limited attention.Here,we review promising methods to suppress liquid product crossover,including the use of bipolar membranes,solid‐state electrolytes,and cation‐exchange membranes‐based acidic CO_(2)R systems.We then outline the re-maining challenges and future prospects for the production of concentrated liquid products from CO_(2).

Inverse design of two-dimensional materials with invertible neural networks

The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery.However,thoroughly and efficiently sampling the entire design space in a computationally tractable manner remains a highly challenging task.To tackle this problem,we propose an inverse design framework(MatDesINNe)utilizing invertible neural networks which can map both forward and reverse processes between the design space and target property.This approach can be used to generate materials candidates for a designated property,thereby satisfying the highly sought-after goal of inverse design.We then apply this framework to the task of band gap engineering in two-dimensional materials,starting with MoS_(2).Within the design space encompassing six degrees of freedom in applied tensile,compressive and shear strain plus an external electric field,we show the framework can generate novel,high fidelity,and diverse candidates with near-chemical accuracy.We extend this generative capability further to provide insights regarding metal-insulator transition in MoS_(2)which are important for memristive neuromorphic applications,among others.This approach is general and can be directly extended to other materials and their corresponding design spaces and target properties.

A Sample-Wise Data Driven Control Solver for the Stochastic Optimal Control Problem with Unknown Model Parameters

In this work,an efficient sample-wise data driven control solver will be developed to solve the stochastic optimal control problem with unknown model parameters.A direct filter method will be applied as an online parameter estimation method that dynamically estimates the target model parameters upon receiving the data,and a sample-wise optimal control solver will be provided to efficiently search for the optimal control.Then,an effective overarching algorithm will be introduced to combine the parameter estimator and the optimal control solver.Numerical experiments will be carried out to demonstrate the effectiveness and the efficiency of the sample-wise data driven control method.

A FIRST-ORDER NUMERICAL SCHEME FOR FORWARD-BACKWARD STOCHASTIC DIFFERENTIAL EQUATIONS IN BOUNDED DOMAINS

We propose a novel numerical scheme for decoupled forward-backward stochastic differ- ential equations （FBSDEs） in bounded domains, which corresponds to a class of nonlinear parabolic partial differential equations with Dirichlet boundary conditions. The key idea is to exploit the regularity of the solution （Yt,Zt） with respect to Xt to avoid direct ap- proximation of the involved random exit time. Especially, in the one-dimensional case, we prove that the probability of Xt exiting the domain within At is on the order of O（（△t）ε exp（--1/（△t）2ε））, if the distance between the start point X0 and the boundary is 1 g at least on the order of O（△t）^1/2-ε ） for any fixed c 〉 0. Hence, in spatial discretization, we set the mesh size △x - （9（（At）^1/2-ε ）, so that all the interior grid points are sufficiently far from the boundary, which makes the error caused by the exit time decay sub-exponentially with respect to △t. The accuracy of the approximate solution near the boundary can be guaranteed by means of high-order piecewise polynomial interpolation. Our method is developed using the implicit Euler scheme and cubic polynomial interpolation, which leads to an overall first-order convergence rate with respect to △t.

Use of the Spatial kD-Tree in Computational Physics Applications

The need to perform spatial queries and searches is commonly encountered within the field of computational physics.The development of applications ranging from scientific visualization to finite element analysis requires efficient methods of locating domain objects relative to general locations in space.Much of the time,it is possible to form and maintain spatial relationships between objects either explicitly or by using relative motion constraints as the application evolves in time.Occasionally,either due to unpredictable relative motion or the lack of state information,an application must perform a general search(or ordering)of geometric objects without any explicit spatial relationship information as a basis.If previous state information involving domain geometric objects is not available,it is typically an involved and time consuming process to create object adjacency information or to order the objects in space.Further,as the number of objects and the spatial dimension of the problem domain is increased,the time required to search increases greatly.This paper proposes an implementation of a spatial k-d tree(skD-tree)for use by various applications when a general domain search is required.The skD-tree proposed in this paper is a spatial access method where successive tree levels are split along different dimensions.Objects are indexed by their centroid,and the minimum bounding box of objects in a node are stored in the tree node.The paper focuses on a discussion of efficient and practical algorithms for multidimensional spatial data structures for fast spatial query processing.These functions include the construction of a skD-tree of geometric objects,intersection query,containment query,and nearest neighbor query operations.

Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning

Genome engineering for materials synthesis is a promising avenue for manufacturing materials with unique properties under ambient conditions.Biomineralization in diatoms,unicellular algae that use silica to construct micron-scale cell walls with nanoscale features,is an attractive candidate for functional synthesis of materials for applications including photonics,sensing,filtration,and drug delivery.Therefore,controllably modifying diatom structure through targeted genetic modifications for these applications is a very promising field.In this work,we used gene knockdown in Thalassiosira pseudonana diatoms to create modified strains with changes to structural morphology and linked genotype to phenotype using supervised machine learning.An artificial neural network(NN)was developed to distinguish wild and modified diatoms based on the SEM images of frustules exhibiting phenotypic changes caused by a specific protein(Thaps3_21880),resulting in 94% detection accuracy.Class activation maps visualized physical changes that allowed the NNs to separate diatom strains,subsequently establishing a specific gene that controls pores.A further NN was created to batch process image data,automatically recognize pores,and extract pore-related parameters.Class interrelationship of the extracted paraments was visualized using a multivariate data visualization tool,called CrossVis,and allowed to directly link changes in morphological diatom phenotype of pore size and distribution with changes in the genotype.

Tensor factorization for elucidating mechanisms of piezoresponse relaxation via dynamic Piezoresponse Force Spectroscopy

Spatially resolved time and voltage-dependent polarization dynamics in PbTiO3 thin films is explored using dynamic piezoresponse force microscopy(D-PFM)in conjunction with interferometric displacement sensing.This approach gives rise to 4D data sets containing information on bias-dependent relaxation dynamics at each spatial location without long-range electrostatic artifacts.To interpret these data sets in the absence of defined physical models,we employ a non-negative tensor factorization method which clearly presents the data as a product of simple behaviors allowing for direct physics interpretation.Correspondingly,we perform phase-field modeling finding the existence of‘hard’and‘soft’domain wall edges.This approach can be extended to other multidimensional spectroscopies for which even exploratory data analysis leads to unsatisfactory results due to many components in the decomposition.