Function Of Separation Research Articles

Model Reduction of Cooperative Systems Using Separable Energy Functions

In this paper, we develop a model reduction method for cooperative nonlinear systems via their variational systems. Inspired by scalable stability analysis of cooperative systems based on separable functions, we provide novel L1 differential observability and L∞ differential reachability functions and study them by using vector-valued functions. These vector-valued functions characterize not-so-important state variables. By truncating them, model reduction is performed, which preserves cooperativity and stability properties.

Metal-organic framework-based high-performance column chip for micro gas chromatography: hybrid function for simultaneous preconcentration and separation of volatile organic compounds.

Numerous attempts have been made to replace commercial bulky gas chromatography (GC) systems with compact GC systems for monitoring volatile organic compounds in indoor air. However, recently developed compact GC systems are still too bulky in terms of user convenience, portability, and on-site analysis. Hence, an advanced miniaturization of compact GC systems is needed. Importantly, the small and high-performance gas pretreatment chip devices should be developed for compact GC systems. This paper reports the development of a metal-organic framework (MOF)-coated hybrid micro gas chromatography column chip (hybrid GC chip) capable of preconcentration and separation on harmful volatile organic compounds at low-concentration in one single chip device. The hybrid GC chip, 2 cm × 2 cm in size, was fabricated using a microelectromechanical systems process. The synthesized MOF-5 particles were coated on the inner wall of the fabricated hybrid GC chip and acted as an adsorbent and a stationary phase. The developed hybrid GC chip afforded high preconcentration factors with 1033-1237 and high separation resolutions with 3.8-13.3. The developed column showed good performance as a gas preconcentrator and separation column, and is the first device to perform both roles in one single chip device.

Three-body interaction of gold nanoparticles: the role of solvent density and ligand shell orientation.

Molecular dynamics simulations are used to study the effective interactions of alkanethiol passivated gold nanoparticles in supercritical ethane at two- and three-particle levels with different solvent densities. Effective interaction is calculated as the potential of mean force (PMF) between two nanoparticles, and the three-body effect is estimated as the difference in PMFs calculated at the two- and three-particle levels. The variation in the three-body effect is examined as a function of solvent density. It is found that effective interaction, which is completely repulsive at very high solvent concentrations, progressively turns attractive as solvent density declines. On the other hand, the three-body effect turns out to be repulsive and increases exponentially with decreasing solvent density. Further, the structure of the ligand shell is analyzed as a function of nanoparticle separation, and its relationship with the three-body effect is investigated. It is observed that the three-body effect arises when the ligand shell begins to deform due to van der Waals repulsion between ligand shells. The study provides a deep insight into good understanding of the solvent evaporation-assisted nanoparticle self-assembly and can aid in experiments.

Numerical solution of box constrained separable convex quadratic programming problems

In this paper, minimization problems with a separable convex quadratic objective function subject to a linear equality constraint/linear equality constraints, and bounds on the variables (box constraints) are considered. A necessary and sufficient condition for a feasible solution to be an optimal solution to each of these problems has been established. A convergent polynomial algorithm for solving the problem with a linear equality constraint and bounded variables is proposed, and some numerical results, obtained by this algorithm, are presented.

An Energy-efficient Multi Objectives MILP Model for Integrated Process Planing and Scheduling in RMS

The integration of process planning and scheduling(IPPS) is a crucial manufacturing strategy to optimize manufacturing resources utilization, which combines process planning and scheduling as an integrated function instead of two separated functions performing sequentially. Reconfigurable manufacturing system(RMS) is one of the latest manufacturing paradigms to meet unstable periodic market changes and environmental challenges. However, IPPS was seldom studied in context of RMS before. This paper proposed a multi objectives mixed integer linear programming model to solve IPPS problem in context of RMS, to optimize the makespan and total energy consumption in manufacturing. To be more realistic, the model considers transportation and reconfiguration factors, simultaneously meeting process planning flexibility.

Repeated load triaxial tests of WickGrid™ stabilized base materials

In road construction, geogrids are frequently used to stabilize the base course through their lateral confinement applied to the base materials. Wicking fabrics are also used to improve road performance by installing them at the road base and subgrade interface to remove water from the road and reduce the build-up of pore water pressure while providing a separate function to mitigate the weak subgrade intrusion into the base course. However, the combined use of these two materials on the road has not been reported. This study aims to investigate the effectiveness of using an innovative geosynthetic composite called WickGrid™ in stabilizing the base materials. WickGrid™ is made by high stiffness biaxial polypropylene (PP) geogrid, heat bonded to a continuous filament nonwoven geotextile having wicking properties. To achieve the objectives, repeated load triaxial tests were conducted on sand materials to evaluate the improvement of the base materials in accordance with the AASHTO T 307-99(2021). The specimens were stabilized with WickGrid™ placed at the mid-height of the specimens. Meanwhile, unstabilized specimens and specimens stabilized with biaxial geogrids were also tested for comparison. The outcomes of these tests would allow for quantifying the beneficial effects of WickGrid™ on the improvement of road bases.

Integrating Statistical Simulation and Optimization for Redundancy Allocation in Smart Grid Infrastructure

It is a critical issue to allocate redundancy to critical smart grid infrastructure for disaster recovery planning. In this study, a framework to combine statistical prediction methods and optimization models for the optimal redundancy allocation problem is presented. First, statistical simulation methods to identify critical nodes of very large-scale smart grid infrastructure based on the topological features of embedding networks are developed, and then a linear integer programming model based on generalized assignment problem (GAP) for the redundancy allocation of critical nodes in smart grid infrastructure is presented. This paper aims to contribute to the field by employing a general redundancy allocation problem (GRAP) model from high-order nonlinear to linear model transformation. The model is specifically implemented in the context of smart grid infrastructure. The innovative linear integer programming model proposed in this paper capitalizes on the logarithmic multiplication property to reframe the inherently nonlinear resource allocation problem (RAP) into a linearly separable function. This reformulation markedly streamlines the problem, enhancing its suitability for efficient and effective solutions. The findings demonstrate that the combined approach of statistical simulation and optimization effectively addresses the size limitations inherent in a sole optimization approach. Notably, the optimal solutions for redundancy allocation in large grid systems highlight that the cost of redundancy is only a fraction of the economic losses incurred due to weather-related outages.

A general continuum modelling approach for variable shear stress as a separable function in stored bulk solids

Granular materials stored in a bin exert pressure on its walls. The Janssen equation describes how this pressure changes with depth in a deep column of granular material. However, it does not address any change in lateral stress throughout stored bulk materials. In this work, we present a general approach for vertical and arbitrary lateral stress in the horizontal (radial) direction. The vertical and lateral stress are described by independent one-dimensional functions whose product describes the stress state throughout the entire bin. Our approach is general, and able to accommodate numerically defined functions such as splines. We are planning future experiments to determine the form of the radial stress. We report on the mathematical analysis here. This research will potentially impact the designing of bulk storage structures, inform grain drying processes and aid in inventory management of stored grain.

METHOD OF COMPILING THE GLOSSARY OF THE CONCEPTUAL MODEL OF AERODROME TECHNICAL FLIGHT SUPPORT ON THE BASIS OF THE TERMS AND DEFINITIONS GIVEN IN REGULATORY AND LEGAL ACTS, STANDARDS AND OTHER REGULATORY DOCUMENTS

The article proposes a method of establishing a direct connection between the conceptual model of aerodrome technical support for flights in the aviation military unit of the Air Force of the Armed Forces of Ukraine and the requirements of the relevant regulatory acts for aerodrome technical support for flights. The concepts of ground handling, aviation ground support and airfield management are closest to the concept of aerodrome technical flight support.
 An example of the using the proposed methodology for improving the regulatory framework in the field of aerodrome technical support for flights is given.
 The methodology takes into account that the subject activity is complex and may contain thousands of separate functions, which are distributed among a significant number of types of performers.
 The proposed method is convenient for the use of computers, such as electronic spreadsheets, and allows you to bring the development of the conceptual model of airfield technical support for flights (for the aviation military unit) to the level of practical suitability for the development of relevant standardized terms and definitions for the description of aerodrome technical support for flights as a complex activity, as well as to facilitate the harmonization of aerodrome technical support regulations with relevant regulatory documents of partner countries.

Topological equivalence classification and enumeration of n-input linearly separable Boolean functions

A perceptron, as an “artificial neuron”, plays a crucial role in the research of neural networks. As a mathematical model of linearly separable Boolean functions, perceptron neurons can realize any nonlinearly separable Boolean function. Linearly separable Boolean functions can be divided into a smaller number of the same functional classes. These functional classes are equivalent to the core basic units (AND, NOT, OR, and other components) that make up the circuit. Therefore, the research on the classification problem of linearly separable Boolean functions is very important. In this study, a novel algorithm, called a topological equivalence classification algorithm, is proposed for classifying balanced linearly separable Boolean functions. By the proposed algorithm, the total number of the topological equivalence classes and the number of balanced linearly separable Boolean functions in each of the topological equivalence classes are obtained. According to the one-to-one correspondence between n-input balanced linearly separable Boolean functions and n-1-input linearly separable Boolean functions, we get the topological classification of linearly separable Boolean functions. As for n=9, the accurate results of classification and counting the number of linearly separable Boolean functions are obtained: There are 53,063,448 topological equivalence classes, and the total number of linearly separable Boolean functions is 144,380,202,286,068,288.

Theoretical investigation of Rydberg states of He2 using the R-matrix method

Bound states of the He 2 molecule are determined from an electron-He 2 + collision calculation using the R-matrix method. The calculations are performed at a moderately dense grid of 35 internuclear separations are used to characterise He 2 Rydberg states with n ≤ 7 . Potential energy curves for singlet 1 Σ g + , 1 Σ u + , 1 Π g , 1 Π u and 1 Δ u and triplet 3 Σ g + , 3 Σ u + , 3 Π g , 3 Π u and 3 Δ u Rydberg states of He 2 as well as effective quantum numbers as a function of internuclear separation are calculated. Based on the potential energy curves of A 1 Σ g + , B 1 Π g , C 1 Σ u + , F 1 Π u and a 3 Σ u + , b 3 Π g , c 3 Σ g + , f 3 Π u , spectroscopic parameters ( T e , D e , R e , ω e , ω e x e , α e and B e ) of these states have been determined and compared with theoretical and experimental data available.

Stability analysis of complex-valued stochastic neural networks with multi-time-delay and parameter uncertainty

This paper investigates the examination of global asymptotic stability pertaining to uncertain complex-valued stochastic neural networks (UCVSNNs) with multiple time delays. This paper employs the real-imaginary separated activation function to establish an equivalence between complex-valued neural networks (CVNNs) and two real-valued activation functions, representing the real and imaginary parts respectively. This equivalence is utilised to analyse the original UCVSNNs model in a comprehensive manner. The global asymptotic stability of the studied UCVSNNs model is ensured by constructing an appropriate Lyapunov-Krasovskii generalised function and applying the I t o ^ formula, linear matrix inequality (LMI) and other analytical techniques to derive the system stability and other sufficient conditions. In the meanwhile, we provide two numerical examples to demonstrate the reliability and benefit of the discovered results.

Surface Casimir Densities on Branes Orthogonal to the Boundary of Anti-De Sitter Spacetime

The paper investigates the vacuum expectation value of the surface energy–momentum tensor (SEMT) for a scalar field with general curvature coupling in the geometry of two branes orthogonal to the boundary of anti-de Sitter (AdS) spacetime. For Robin boundary conditions on the branes, the SEMT is decomposed into the contributions corresponding to the self-energies of the branes and the parts induced by the presence of the second brane. The renormalization is required for the first parts only, and for the corresponding regularization the generalized zeta function method is employed. The induced SEMT is finite and is free from renormalization ambiguities. For an observer living on the brane, the corresponding equation of state is of the cosmological constant type. Depending on the boundary conditions and on the separation between the branes, the surface energy densities can be either positive or negative. The energy density induced on the brane vanishes in special cases of Dirichlet and Neumann boundary conditions on that brane. The effect of gravity on the induced SEMT is essential at separations between the branes of the order or larger than the curvature radius for AdS spacetime. In the considerably large separation limit, the decay of the SEMT, as a function of the proper separation, follows a power law for both massless and massive fields. For parallel plates in Minkowski bulk and for massive fields the fall-off of the corresponding expectation value is exponential.

Quantifying bark thickness and bark volume with alternative modeling procedures for eight species in the southeastern US

Bark thickness is one of the important tree characteristics to assess plant survival following silvicultural treatments or natural disturbances. With an increased number of catastrophic wildfires under changing climate, a reliable approach for modeling bark thickness and bark volume is critical to monitor forest health and quantify carbon stocking in sustainable forest management. The objective of the study was to compare the efficacy of three alternative quantifying approaches and statistical models for both bark thickness and volume. In approach I, bark thickness is quantified from the differences of inside- and outside-bark diameters, which are predicted with two separate stem taper functions. The second approach combines a stem taper function and a bark thickness model, while in the third approach, bark thickness is quantified via a single predictive model with bark thickness at breast height included. The statistical models examined include segmented polynomial regression models (SEG), variable-exponent models (VEM) and generalized additive models (GAM). A total number of 71,187 observations of 2,932 trees were queried from the LegacyTree database, including eight ecologically and economically important species in the southeastern US.Results show that all combinations of approaches and models can quantify bark thickness and bark volume without bias for all eight species examined. Approach III yields higher accuracy and precision levels than approaches I and II. The performance of the models varies among quantifying approaches and species. GAM and VEM generally produce a more reliable quantification than SEG. Although GAM may not be the optimal model for a given species, such a model can provide preliminary quantitative information about bark thickness and bark volume when there is no existing model for the target species. The results of this work provide additional insights for forest ecologists and practitioners to select appropriate modeling approaches for quantifying bark thickness and volume.

MEHW‐SVM multi‐kernel approach for improved brain tumour classification

AbstractThe human brain, the primary constituent of the nervous system, exhibits distinctive complexities that present considerable difficulties for healthcare practitioners, specifically in categorizing brain tumours. Magnetic resonance imaging is a widely favoured imaging modality for detecting brain tumours due to its extensive range of image characteristics and utilization of non‐ionizing radiation. The primary objective of the current investigation is to differentiate between three distinct classifications of brain tumours by introducing a novel methodology. The utilization of a combined feature extraction technique that integrates novel global grey level co‐occurrence matrix and local binary patterns is employed, thereby offering a comprehensive representation of the structural and textural information contained within the images. Principal component analysis is used to improve the model's efficiency for effective feature selection and dimensionality reduction. This study presents a novel framework incorporating four separate kernel functions, Minkowski–Gaussian, exponential support vector machine (SVM), histogram intersection SVM, and wavelet kernel, into a SVM classifier. The ensemble kernel employed in this study is specifically designed to classify glioma, meningioma, and pituitary tumours. Its implementation enhances the model's robustness and adaptability, surpassing the performance of conventional single‐kernel SVM approaches. This study substantially contributes to medical image classification by utilizing innovative kernel functions and advanced machine‐learning techniques. The findings demonstrate the potential for enhanced diagnostic accuracy in brain tumour cases. The presented approach shows promise in effectively addressing the intricate challenges associated with classifying brain tumours.

Bayesian hybrid index and genomic cline estimation with the R package gghybrid.

Admixture, the interbreeding of individuals from differentiated source populations, is now known to be a widespread phenomenon. Genomic studies of natural hybridisation can help to answer many questions on the impacts of admixture on adaptive evolution, reproductive isolation, and speciation. When a large variety of admixture proportions between two source populations exist, both geographic and genomic cline analysis are suitable methods for inferring biased, restricted or excessive gene flow at individual loci into the foreign genomic background, providing evidence for reproductive isolation, selection across an environmental transition, balancing selection, and adaptive introgression. Genomic cline analysis replaces geographic location with genome-wide hybrid index and is therefore useable in circumstances that violate geographic cline assumptions. Here, I introduce gghybrid, an R package for simple and flexible Bayesian estimation of Buerkle's hybrid index and Fitzpatrick's logit-logistic genomic clines using bi-allelic data, suitable for both small and large datasets. gghybrid allows any ploidy and uses Structure input file format. It has separate functions for hybrid index and cline estimation, treating each individual and locus respectively as an independent analysis, making it highly parallelisable. Admixture proportions from other software can alternatively be used in cline analysis, alongside parental allele frequencies. Parameters can be fixed and samples pooled for statistical model comparison with AIC or waic. Here, I describe the functions, pipeline, and statistical properties of gghybrid. Simulations reveal that model comparison with waic is preferred, and use of Bayesian posterior distributions and p values to select candidate non-null loci is problematic and should be avoided.

A state-based inverse reinforcement learning approach to model activity-travel choices behavior with reward function recovery

Behaviorally oriented activity-travel choices (ATC) modeling is a principal part of travel demand analysis. Traditional econometric and rule-based methods require explicit model structures and complex domain knowledge. While several recent studies used machine learning models, especially adversarial inverse reinforcement learning (IRL) models, to learn potential ATC patterns with less expert-designed settings, they lack a clear representation of rational ATC behavior. In this study, we propose a data-driven IRL framework based on the maximum causal approach to minimize f-divergences between expert and agent state marginal distributions, which provides a more sample-efficient measurement. In addition, we specify a separate state-only reward function and derive an analytical gradient of the f-divergence objective with respect to reward parameters to ensure good convergences. The method can recover a stationary reward function, which assures the agent to get close to the expert behavior when training from scratch. We validate the proposed model using cellular signaling data from Chongqing, China by comparing with baseline models (behavior cloning, policy-based, and reward-based models) in aspects of policy performance comparison, reward recovery, and reward transfer tasks. The experiment results indicate that the proposed model outperforms existing methods and is relatively less sensitive to the number of expert demonstrations. Qualitative analyses are provided on the fundamental ATC preferences on different features given the reward function recovered from the observed mobility trajectories, and on the learning behaviors under different choices of f-divergence.

Optimizing retailer prepayment installment in inventory systems with link-to-order discounts and power demand patterns of price and time: An effective decision support framework

Installment in the prepayment mechanism has emerged as a significant decision for many companies due to rising capital costs and increasing pressures from suppliers for various products. To motivate the companies about the prepayment mechanism, suppliers allow the frequency of installment of advance payment as a decision variable for the company and the per unit acquisition cost as a step-down function in exchange for an order-based prepayment regulation allowing the stepwise decreasing prepayment portion and duration as well. This paper investigates the optimal joint decision-making of a company’s prepayment, pricing, and stocking policies to maximize profits in the context of an order-based prepayment and discount regulation. A realistic approach is adopted in the study by considering the clients’ demand as a multiplicatively separable power function that accounts for both the price and the storage time of products. Theoretical expressions are derived to check when multiple or single installments are more profitable for the company. To achieve the maximum profit, a solution approach is introduced based on all the derived theoretical outcomes. The results of three numerical studies are documented and then the effects of order-based prepayment and discount regulation on optimal policies and maximum profit are further studied. The results of the study indicate that the profitability of the company can be enhanced through an increase in the frequency of installments, particularly in situations where the rate of interest charged is high. Finally, to draw up effective guidelines, a sensitivity investigation is conducted for all cost parameters.

In situ lipid-loading activates peripheral dendritic cell subsets characterized by cellular ROS accumulation but compromises their capacity to prime naïve T cells

Background and aimsDendritic cells (DCs), professional antigen-presenting cells, play an important role in pathologies by controlling adaptive immune responses. However, their adaptation to and functionality in hypercholesterolemia, a driving factor in disease onset and progression of atherosclerosis remains to be established. MethodsIn this study, we addressed the immediate impact of high fat diet-induced hypercholesterolemia in low-density lipoprotein receptor deficient (Ldlr−/−) mice on separate DC subsets, their compartmentalization and functionality. ResultsWhile hypercholesterolemia induced a significant rise in bone marrow myeloid and dendritic cell progenitor (MDP) frequency and proliferation rate after high fat diet feeding, it did not affect DC subset numbers in lymphoid tissue. Hypercholesterolemia led to almost immediate and persistent augmentation in granularity of conventional DCs (cDCs), in particular cDC2, reflecting progressive lipid accumulation by these subsets. Plasmacytoid DCs were only marginally and transiently affected. Lipid loading increased co-stimulatory molecule expression and ROS accumulation by cDC2. Despite this hyperactivation, lipid-laden cDC2 displayed a profoundly reduced capacity to stimulate naïve CD4+ T cells. ConclusionOur data provide evidence that in hypercholesterolemic conditions, peripheral cDC2 subsets engulf lipids in situ, leading to a more activated status characterized by cellular ROS accumulation while, paradoxically, compromising their T cell priming ability. These findings will have repercussions not only for lipid driven cardiometabolic disorders like atherosclerosis, but also for adaptive immune responses to pathogens and/or endogenous (neo) antigens under conditions of hyperlipidemia.

Pore structure regulation and continuous preparation with VNIPS process of membranes for bioseparation

Ultrafiltration technology has found widespread application in bioprocessing, particularly in protein concentration and buffer exchange for downstream processes. The demand for this technology has spurred research efforts to develop excellent membrane structures with superior separation functionality. However, challenges persist in transitioning laboratory research to industrial-scale continuous production processes while simultaneously optimizing membrane structure and performance through a single fabrication method. Thus, there is an urgent need for pilot-scale membrane preparation to improve membrane morphology and separation efficiency while ensuring scalability. This study investigates a continuous membrane fabrication method, systematically exploring the influence of different additives and vapor-assisted nonsolvent induced phase separation (VNIPS) on the structure and performance of polyethersulfone (PES) membranes. Evaluations of PES membranes with distinct structures were performed, employing flux decline, resistance analysis, and alkali resistance tests to distinguish their performance and bioseparation capabilities. The results showed that good sponge-like pore structure was obtained by VNIPS process, which realized the synergistic optimization of permeation and retention properties. Besides, the sponge-like porous structure membranes prepared by amphiphilic copolymer additives are more suitable for bioseparation.