Desired Quantities Research Articles

Deep energy-pressure regression for a thermodynamically consistent EOS model

In this paper, we aim to explore novel machine learning (ML) techniques to facilitate and accelerate the construction of universal equation-Of-State (EOS) models with a high accuracy while ensuring important thermodynamic consistency. When applying ML to fit a universal EOS model, there are two key requirements: (1) a high prediction accuracy to ensure precise estimation of relevant physics properties and (2) physical interpretability to support important physics-related downstream applications. We first identify a set of fundamental challenges from the accuracy perspective, including an extremely wide range of input/output space and highly sparse training data. We demonstrate that while a neural network (NN) model may fit the EOS data well, the black-box nature makes it difficult to provide physically interpretable results, leading to weak accountability of prediction results outside the training range and lack of guarantee to meet important thermodynamic consistency constraints. To this end, we propose a principled deep regression model that can be trained following a meta-learning style to predict the desired quantities with a high accuracy using scarce training data. We further introduce a uniquely designed kernel-based regularizer for accurate uncertainty quantification. An ensemble technique is leveraged to battle model overfitting with improved prediction stability. Auto-differentiation is conducted to verify that necessary thermodynamic consistency conditions are maintained. Our evaluation results show an excellent fit of the EOS table and the predicted values are ready to use for important physics-related tasks.

Biological, pathological, and multifaceted therapeutic functions of exosomes to target cancer.

Exosomes, small tiny vesicle contains a large number of intracellular particles that employ to cause various diseases and prevent several pathological events as well in the human body. It is considered a "double-edged sword", and depending on its biological source, the action of exosomes varies under physiological conditions. Also, the isolation and characterization of the exosomes should be performed accurately and the methodology also will vary depending on the exosome source. Moreover, the uptake of exosomes from the recipients' cells is a vital and initial step for all the physiological actions. There are different mechanisms present in the exosomes' cellular uptake to deliver their cargo to acceptor cells. Once the exosomal uptake takes place, it releases the intracellular particles that leads to activate the physiological response. Even though exosomes have lavish functions, there are some challenges associated with every step of their preparation to bring potential therapeutic efficacy. So, overcoming the pitfalls would give a desired quantity of exosomes with high purity.

Optimal estimation of the local time and the occupation time measure for an α-stable Lévy process

A novel theoretical result on estimation of the local time and the occupation time measure of an α-stable Lévy process with $\alpha \in (1,2)$ is presented. The approach is based upon computing the conditional expectation of the desired quantities given high frequency data, which is an ${L^{2}}$-optimal statistic by construction. The corresponding stable central limit theorems are proved and a statistical application is discussed. In particular, this work extends the results of [20], which investigated the case of the Brownian motion.

ВОПРОСЫ ПРИМЕНЕНИЯ ЧИСЛЕННЫХ И ВЫЧИСЛИТЕЛЬНЫХ МЕТОДОВ В МАТЕМАТИЧЕСКОМ И ТЕОРЕТИКО-ИГРОВОМ МОДЕЛИРОВАНИИ

Computational methods are widely used in mathematical modeling to identify sequences necessary for the desired quantities. Logical and computational operations are used in programming to create computer programs that work with the model in a technical plane. Numerical methods are equally widespread in problems of game theory, where they are used to calculate optimal strategies, display winning probabilities and calculate the effectiveness of selected methods, in order to proceed to a solution using computational methods, the original problem is replaced by a computational algorithm. Mathematical modeling is a theoretical tool and allows you to describe in theory the trends of a particular phenomenon or process, track development trends, outline the relationships and behavior of objects within the desired model. Mathematical modeling can be attributed to experimental scientific methods, since using computer technologies and tools it allows you to conduct a huge number of experiments with models, which in turn allows you to work through many scenarios with different introductory ones.

Formulation and Numerical Solution of Plane Problems of the Theory of Elasticity in Strains

This article is devoted to the formulation and numerical solution of boundary-value problems in the theory of elasticity with respect to deformations. Similar to the well-known Beltrami–Michell stress equations, the Saint-Venant compatibility conditions are written in the form of differential equations for strains. A new version of plane boundary-value problems in strains is formulated. It is shown that for the correctness of plane boundary value problems, in addition to the usual conditions, one more special boundary condition is required using the equilibrium equation. To discretize additional boundary conditions and differential equations, it is convenient to use the finite difference method. By resolving grid equations and additional boundary conditions with respect to the desired quantities at the diagonal nodal points, we obtained convergent iterative relations for the internal and boundary nodes. To solve grid equations, the elimination method was also used. By comparing with the Timoshenko–Goodyear solution on the tension of a rectangular plate with a parabolic load, the validity of the formulated boundary value problems in strains and the reliability of the numerical results are shown. The accuracy of the results has been increased by an average of 15%.

Estimation of Continuous Distribution of Iterated Fission Probability Using an Artificial Neural Network with Monte Carlo-Based Training Data

The Monte Carlo neutron transport method is used to accurately estimate various quantities, such as k-eigenvalue and integral neutron flux. However, in the case of estimating a distribution of a desired quantity, the Monte Carlo method does not typically provide continuous distribution. Recently, the functional expansion tally (FET) and kernel density estimation (KDE) methods have been developed to provide a continuous distribution of a Monte Carlo tally. In this paper, we propose a method to estimate a continuous distribution of a quantity in all phase-space variables using a fully connected feedforward artificial neural network (ANN) model with Monte Carlo-based training data. As a proof of concept, a continuous distribution of iterated fission probability (IFP) was estimated by ANN models in two distinct fissile systems. The ANN models were trained on the training data created using the Monte Carlo IFP method. The estimated IFP distributions by the ANN models were compared with the Monte Carlo-based data that include the training data. Additionally, the IFP distributions by the ANN models were also compared with the adjoint angular neutron flux distributions obtained with the deterministic neutron transport code PARTISN. The comparisons showed varying degrees of agreement or discrepancy; however, it was observed that the ANN models learned the general trend of the IFP distributions from the Monte Carlo-based training data.

A phase-time-path-difference approach for online wave direction and wave number estimation from measured ship motions in zero and forward speed using a single inertial measurement unit

This study investigates the potential capability of a relatively new and unexplored signal-based approach for shipboard wave estimation. The approach uses the phase-time-path-differences (PTPDs) from an array of shipboard sensors to uniquely resolve the wave propagation direction and wave number. We derive a kinematic PTPD model accounting for forward vessel speed and assess its theoretical foundation to model the sensor delays on a rigid body. The forward-speed PTPD model is structurally equivalent to the zero-speed model considered in previous works, thus retaining the same observability results provided by a noncollinear array of a minimum of three sensors. Moreover, based on the outlined theory and PTPD model, we propose a methodology to estimate the main wave propagation direction and wave number online by employing a fast Fourier transform (FFT), an unscented Kalman filter (UKF), and a rigid-body measurement transformation based on a single inertial measurement unit (IMU). Provided that the vessel in question can be considered a rigid body, a single IMU is sufficient to obtain the desired wave quantities instead of three IMUs, as initially proposed in our previous work. Additionally, our methodology incorporates a novel frequency threshold to avoid distorted wave components caused by the effect of vessel filtering. The performance of our PTPD method is evaluated on data collected from a wave tank and full-scale experiments involving a vessel with zero and non-zero forward speed. The results show very good agreement with the reference wave values reported from a commercial wave radar and wave buoys operating in proximity to the vessel, indicating that our proposed method is competitive with existing wave measurement technology in terms of accuracy and online performance while being cheap, easy to install, flexible, and robust against environmental influences.

Comparing the effect of different agricultural land-use systems on biodiversity

This article develops a calculation method to compare the total effect of different agricultural land-use systems on biodiversity. The results of the calculations offer a clear indication of which land-use system provides the highest biodiversity possible to produce a desired quantity of agricultural output. Furthermore, the method can be used to compare all types of farming systems. To give a numerical example, the method is used here to compare the data currently available for conventional agriculture and organic agriculture. Given that the currently available data lack information regarding second moments, a statistical test is not possible. Under this caveat, the results indicate that conventional agriculture allows for more biodiversity in the production of a desired quantity of agricultural goods because its higher productivity helps to spare land, which can then be used as a natural habitat.

Multi-objective optimization for timber harvest management incorporating wildlife habitat goals

ContextThe forestry industry provides important goods, services and economic benefits, but timber harvest can adversely impact ecosystem services, including wildlife habitat. Timber harvest planning can integrate wildlife habitat quality through multi-objective optimization for timber harvest and wildlife habitat suitability.ObjectivesOur objective was to develop a method to find optimal solutions for timber harvest and wildlife habitat suitability individually and concurrently, then apply the method to Roosevelt elk (Cervus elaphus roosevelti) on Afognak Island, Alaska.MethodsWe developed three seasonal habitat suitability models using elk locations and landscape variables including historical timber harvest on Afognak Island, Alaska. We used threshold-accepting optimization over a 50-year planning horizon to maximize timber harvest yield and habitat suitability in each season, then used multi-objective goal-deviation optimization to simultaneously maximize timber harvest volume and seasonal habitat suitability.ResultsThe optimal solution for timber yield decreased seasonal average habitat suitability by 5.7%. Elk habitat suitability and corresponding optimal solutions varied seasonally; elk generally selected open landcovers and early- to mid-successional timber stands over late-successional and mature stands. Therefore, in the optimal solutions, stands were harvested before they reached maximum volume and few stands were harvested in early planning periods, resulting in a seasonal average loss of 17.5% yield. Multi-objective optimization decreased seasonal average suitability by 3.9% and yield by 1.4% compared to single-objective optimization.ConclusionsOur multi-objective optimization approach that incorporates data-driven habitat suitability models using open-source software can enable managers to achieve desired quantity and quality of wildlife habitat while providing for resource extraction.

Customizable optical tissue phantom platform for characterization of fluorescence imaging device sensitivity.

Optical tissue phantoms serve as inanimate and stable reference materials used to calibrate, characterize, standardize, and test biomedical imaging instruments. Although various types of solid tissue phantoms have been described in the literature, current phantom models are limited in that they do not have a depth feature that can be adjusted in real-time, they cannot be adapted to other applications, and their fabrication can be laborious and costly. Our goal was to develop an optical phantom that could assess the imaging performance of fluorescence imaging devices and be customizable for different applications. We developed a phantom with three distinct components, each of which can be customized. We present a method for fabricating a solid optical tissue that contains (1)an adjustable depth capability using thin film phantoms, (2)a refillable chip loaded with fluorophores of the user's choice in various desired quantities, and (3)phantom materials representative of different tissue types. This article describes the development of phantom models that are customizable, adaptable, and easy to design and fabricate.

A new variant of the fem for evaluation the strenght of structures of complex geometry with heterogeneous material structure

Structures are known to feature a complex geometry and a complex material structure which varies depending on their functional purpose, technological capabilities and design. The finite difference method, collocation method, variational method, theoretical and experimental method, finite element method, among other methods, are employed to calculate the stress-strain state (SST) of structures of complex geometry. The finite element method (FEM) has become the most common one of them all. The spline version of the finite element method (SV FEM) has proven to be an effective FEM method used to calculate the SST of structures of complex geometry. Unlike the conventional FEM method, a preliminary parametrization of the entire area of complex geometry using the parameters outlined by the canonical domain and a cubic approximation of the desired variables within each element yields consistent finite elements. The unbroken geometric continuity maintained between the elements along with the continuity of the desired quantities and their first derivatives along the line of contact of the elements makes it possible to obtain reliable results with just a small number of partitions into finite elements. One should note the application of the basics of the spline version of the finite element method in two-dimensional SV FEM-2 and three-dimensional SV FEM-3 arrangements. One should also make mention of the idea of producing a synthesized version of SV FEM. Two-dimensional and three-dimensional elements being synthesized into one element would usher in a new phase in the development of the SV FEM approach which would describe the complex structures of construction materials in addition to complex geometry. SV FEM has made it possible to evaluate the SST of structural elements of complex geometry along with the thin layers that penetrate it in all three directions. The versions of SV FEM that are available now serve to assess the SST of a broad range of various thin-walled and non-thin-walled structures of complex geometry, including multilayer structures with thin layers in all three directions, with hard and soft coatings along the edges and with local defects on the surfaces of structures. This paper presents a number of SST calculations of specific structures that have been carried out via the FEM versions described above.

Effect of ozone nanobubbles on the microbial ecology of pond water and safety for jade perch (Scortum barcoo)

The microbial composition and diversity in aquaculture pond ecosystems are closely related to animal productivity and disease outbreaks. Interventions that alter the bacteria found in pond water can therefore affect the productivity of these systems. Ozone nanobubbles have recently been shown to reduce pathogens, improve dissolved oxygen, and influence fish innate immunity. However, little is known about the effect of nanobubble treatment on the microbial community of aquaculture ponds. This study investigated the impact of ozone macrobubbles (O3MB) and nanobubbles (O3NB) on the microbial ecology of pond water and fish health. We successfully eliminated between 90.9 and 99.4% of the heterotrophic bacteria and 90.1 to 95.2% of the bacterial DNA in our small pond water ecosystems after treatment with 0.15 mg/L ozone. According to the shotgun metagenomic sequencing, ozone macro- and nanobbuble treatments reduced the relative abundance of all bacteria in our water sample, including the dominant bacterial species, as well as Cyanobacteria. The top ten bacterial species in the community changed and were more evenly distributed within the water sample. The bacterial richness of the ozone-treated water samples declined slightly, but over 6000 species were still identified 24 h after the treatment. We also observed a rebound in the bacterial community 24 h after the ozone treatments. The advantage of the nanobubble delivery of ozone over macrobubble delivery of this gas was that the former took significantly less time to deliver the desired quantity of gas while it also greatly increased the dissolved oxygen in the water. Further, we assessed the impact of ozone nanobubbles on jade perch, and no effects were found on the fish at an exposure dose of 0.15 mg/L. This study provides preliminary information on potential applications of nanobubble technology for “resetting” microbial communities, which may be useful during disease outbreaks.

Critical Analysis of Published Physical Property Data for Aqueous Potassium Hydroxide. Collation into Detailed Models for Alkaline Electrolysis

Physical properties of aqueous KOH solutions are crucial to the design and operation of alkaline electrolyzers but have been scarcely and sometimes unreliably reported. Obtaining published data for various properties currently requires time-consuming searches and subsequent interpretation, interpolation, and extrapolation. This work collates and critically analyzes published data for a range of physical properties relevant to alkaline electrolysis, including the density, viscosity, conductivity, surface tension, oxygen/hydrogen solubility, oxygen/hydrogen diffusivity, and water vapor pressures of aqueous KOH solutions, as a function of temperature, KOH molarity, and pressure. Correlation functions, in the form of excel spreadsheets, have been developed to allow interpolation of the most reliable data and computation of desired quantities at specific temperatures, pressures, and KOH concentrations. Composite models incorporating these properties have been developed for automated computation of (i) diffusive gas crossover and (ii) gas production volumes, including (iii) water vapor content, and associated (iv) dissolved gas concentrations in the liquid electrolyte, as a function of KOH concentration, temperature, pressure, current density, and separator thickness and porosity. These spreadsheets are provided in the Supporting Information, as tools and reference points for researchers and practitioners in alkaline electrolysis.

Particles’ distribution enhancing in aluminum-based composites produced by upward friction stir processing

A new variant of friction stir processing named upward friction stir processing (UFSP) is a promising approach to control particles’ distribution and promote a more uniform distribution over a larger processed area. This variant involves using two sheets with functional particles between them to produce metallic composites. A spacer is used to ensure the desired quantity and uniform distribution of the particles and prevent sputtering. This technique promotes an upward flow to introduce more particles with a uniform distribution in the processed volume, avoiding discrete holes or grooves. This study involved enhancing the particles’ distribution by varying process parameters. The resulting trial with the best particles’ distribution was characterized by means of light microscopy, eddy current testing, microhardness mapping, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. The study revealed that UFSP can improve the particles’ distribution in the stir zone of metallic composites, especially when multi-passes are performed towards the retreating side of the plates. The process parameters that produced an improvement in particles’ distribution were six passes with an offset of 1 mm towards the retreating side, the tool rotation and processing speed of 900 rev/min, and 180 mm/min, respectively, and a spacer’s thickness of 0.5 mm. The resulting hardness and electrical conductivity profiles show that the UFSP technique can significantly affect material’s properties, including mechanical strength, particularly when processing with tool offset towards the retreating side. Furthermore, the hardness increased by about 22% in composites produced with the addition of reinforcement particles. However, for some aluminum alloys, the properties decreased under such conditions. These findings highlight the potential of UFSP for producing functionalized materials with tailored properties, while also underscoring the importance of careful parameters selection to optimize the material´s performance.Graphical

European standards for undergraduate medical education in general practice; a blueprint - for action

ABSTRACT There is compelling evidence that general practice (GP) is the most effective form of healthcare. However, healthcare policy appears independent of evidence and GP is woefully under-resourced in all countries, and this affects recruitment. Recruitment to GP is proportional to the quantity and quality of undergraduate experience and national and transnational guidelines can improve undergraduate experiences by defining both the desired quantity and quality. There is good evidence that these professionally developed guidelines can be effective in changing Government policy if they are used as a touchstone to collaborate with policymakers. EURACT (European Academy of Teachers in General Practice / Family Medicine) have therefore developed transnational guidelines covering the European region. The guidelines cover the desired quantity, quality and support for undergraduate experience. Three main design principles have been used. Firstly, it is democratic. Secondly it is evidence-based, using extensive literature searching, situational analysis and surveys. Finally, it adopts a ‘principles-based approach’. Generalist medicine is articulated as a series of interconnected principles that integrate and then re-focus specialist medicine to achieve the enhanced patient-orientated outcomes of primary-care. This way of articulating generalist practice delivers general principles, which can be used as learning outcomes, that are adaptable to a wide range of learning environments. Most clinical learning documents are irrelevant and are destined for dusty drawers or forgotten digital files. We therefore encourage primary care educators to use these guidelines to work with policy-makers at all levels to advocate for change, strengthening primary care education at local, national and international levels.

Review on Various Regeneration Techniques in Dragon Fruit (Hylocereus spp.)

Dragon fruit (Hylocereus spp.) is a recently introduced super fruit in India, gaining popularity both in the rural and urban areas because of its attractive colour, delicious taste, high nutritive and medicinal values. It is adaptable to humid as well as semi-arid tropical and subtropical conditions. The growing acceptability of the fruit along with its immense antioxidants and medicinal properties has led to high demand of its cultivation followed quality planting materials in desired quantity. Dragon fruit can be propagated by various ways both sexually via seeds as well as asexually via stem cuttings, grafting and also via micropropagation. In this review various methods of propagation of dragon fruit are described along with some propagation aspects related to dragon fruit on which very limited information is available. Potential areas on dragon fruit propagation that require further research to generate more data in order to improve the techniques are also discussed.

Use of critical response spectrum for design of multi-story steel buildings under multi-component seismic excitation

During an earthquake, buildings are simultaneously excited by three-components of ground motion (two horizontal and one vertical) orientations of which are not known apriori. To take account for the uncertainty in the direction of incidence of earthquake waves, a structure is required to be designed such that it is safe for all directions of incidence. For this purpose, the combined effects of two horizontal components of motion are commonly determined using simplified methods such as the percentage rules (e.g. 100%+30%, 100%+40%), square root of the sum of squares (SRSS) and Complete Quadratic Combination (CQC-3) rules. The modern building codes recommend to estimate the orthogonal response quantities for the individual horizontal component of motion using response spectrum superposition methods with design spectrum in the principal direction of motion. In the present paper, a new method is proposed to evaluate the maximum response of multi-story buildings under the simultaneous action of two horizontal components of ground motion using the concept of critical response spectrum. The critical response spectrum is computed using the resultant response of a bidirectional single degree of freedom system at each time step under the simultaneous action of the two horizontal components of motion. For an illustration of the proposed method, steel building asymmetric in the plan is analysed using critical response spectra of the three different pairs of recorded ground motion and the results obtained are validated by comparison with the exact time-history solutions. The exact response is taken as the maximum of the responses estimated by applying the two horizontal time-histories of ground acceleration at different angles from 0 to 180 degree with respect to the structural x-axis. On the other hand, in the response spectrum method, two values of the desired response quantity are obtained by applying the critical response spectrum along the x- and y-directions of the structure and are combined using SRSS method. It has been found that the use of the critical spectrum provides a very convenient method for estimating the maximum response under multi-component excitation without the need for computation of critical incident angle of incidence.

Numerical assessment of riverbank filtration using gravel back filter to improve water quality in arid regions

The main challenge of water resource management in high-stress regions, especially in developing countries, is in adopting non-traditional methods to obtain safe drinking water in desired quantities. In Egypt, the riverbank filtration (RBF) system is one of the most common solutions to solve rivers’ water quality issues. Several sites have been investigated, and the system has demonstrated tremendous potential. The drinking water plant in Embaba, Giza, Egypt, is considered in this study to improve the quality of the abstracted water through the vertical well system. The numerical code of MODFLOW and MT3D is used to simulate the impact of using the emplacement of the gravel-pack filter (GPF). Three different scenarios were investigated: the first consists of vertical GPF; the second is horizontal GPF for different geometries, depths, widths or thicknesses, lengths, and permeability of the filter material; and, the third is drilling a pipe filter through the riverbed for different pipe numbers, depths, and the material’s hydraulic conductivity. The results revealed that the riverbank filtration sharing (RBFS) rate was increased by increasing the filter width or thickness at the riverside, the filter pipe numbers, the length of the horizontal filter, and the permeability of filter material. At the same time, the thickness of the river bed decreased by increasing the filter width at the groundwater side. Also, the RBFS was increased by increasing the filter width or the thickness in the two directions and the pipe length. However, it returned to decrease again due to groundwater sharing. Thus, the RBF design should carefully consider the gravel-pack, pipe filter geometry, and permeability impact rate of RBFS.

Cr-enriched carbide induced stabilization of austenite to improve the ductility of a 1.7 GPa−press-hardened steel

Obtaining a desired quantity of retained austenite (RA) in press-hardened steels (PHSs) with a martensitic matrix is of great interest for the performance of safety critical components in vehicle bodies, as it improves the ductility/toughness without compromising strength. In this study, RA in a 1.7 GPa−PHS was stabilized via local chemical heterogeneity associated with Cr-enriched carbides. The Cr-rich carbide was introduced as a temporary reservoir of Cr and C into the pre-hot stamping microstructure by an annealing treatment. During the subsequent austenitization in hot stamping, the slow decomposition kinetics of Cr-rich carbide contributed to localized enrichment of Cr and C, which stabilized the austenite and resulted in a significant amount of RA with a core-shell structure after die quenching. Finally, sufficient RA ensured that the 1.7 GPa−PHS had a total elongation of ∼9.3%, which is about 43% higher than that of the 1.5 GPa−PHS grade 22MnB5 (∼6.5%).

CAS4DL: Christoffel adaptive sampling for function approximation via deep learning

The problem of approximating smooth, multivariate functions from sample points arises in many applications in scientific computing, e.g., in computational Uncertainty Quantification (UQ) for science and engineering. In these applications, the target function may represent a desired quantity of interest of a parameterized Partial Differential Equation (PDE). Due to the large cost of solving such problems, where each sample is computed by solving a PDE, sample efficiency is a key concern in these applications. Recently, there has been increasing focus on the use of Deep Neural Networks (DNN) and Deep Learning (DL) for learning such functions from data. In this work, we propose an adaptive sampling strategy, CAS4DL (Christoffel Adaptive Sampling for Deep Learning) to increase the sample efficiency of DL for multivariate function approximation. Our novel approach is based on interpreting the second to last layer of a DNN as a dictionary of functions defined by the nodes on that layer. With this viewpoint, we then define an adaptive sampling strategy motivated by adaptive sampling schemes recently proposed for linear approximation schemes, wherein samples are drawn randomly with respect to the Christoffel function of the subspace spanned by this dictionary. We present numerical experiments comparing CAS4DL with standard Monte Carlo (MC) sampling. Our results demonstrate that CAS4DL often yields substantial savings in the number of samples required to achieve a given accuracy, particularly in the case of smooth activation functions, and it shows a better stability in comparison to MC. These results therefore are a promising step towards fully adapting DL towards scientific computing applications.