A THEORETICAL APPROACH TO THERMAL PROPERTY OF ARRAY OF CYLINDERS EMBEDDED IN HOMOGENEOUS MATRIX

We investigate in this article the thermal coliductivity of array Of cylinders embedded in a homogeneous matrix. Using Green's function, we confirm that the method invented by Rayleigh can be generalized to deal with thermal property of these systems. A technique for calculating effective thermal conductivities of these systems is proposed. As an example, we consider a system with square symmetry, and a neat formula for effective thermal conductivity is derived. We show that the method also includes the proof of Keller theorem.

Mathematical Study of A Memory Induced Biochemical System

In this work, to study the effect of memory on a bi-substrate enzyme kinetic reaction, we have introduced an approach to fractionalize the system, considering it as a threecompartmental model. Solutions of the fractionalized system are compared with the corresponding integer-order model. The equilibrium points of the fractionalized system are derived analytically. Their stability properties are discussed from numerical aspect. We determine the changes of the substances due to the changes of "memory effect". The effect is discussed critically from the perspective of product formation. We have also analyzed the memory induced system with a control measure in view of optimizing the product. Our numerical result reveals that the solutions of the fractionalized system, when it is free from memory, are in good agreement with the integer-order system.It is noticed that the effect of memory influences the reaction in the forward direction and assists in yielding the product more quickly. However, an extensive use of memory makes the system slower, but introduction of a control input makes the reaction faster. It is possible to overcome the slowness of the reaction due to the undue effect of memory by appropriate use of a control measure.

Analytical Approach to Clusters in near Critical CO2

Clusters greatly influence thermophysical properties of near critical gases. The cluster structures of supercritical fluids in general and Carbon Dioxide especially are important for the advanced supercritical fluid technologies and analytics development. The paper extends to near critical densities the developed earlier methods to extract the clusters’ properties from Online Electronic Database of NIST on thermophysical properties of fluids. This Database contains a hidden knowledge of cluster fractions’ properties in real gases. The discovered earlier linear chain clusters dominate at intermediate densities. Their properties can be extrapolated to high density gases, thus opening the way to study large 3D clusters in near critical zone. The potential energy density of a gas, cleared from the chain clusters’ contribution, reflects only the 3D clusters’ characteristics. A series expansion of this value by the Monomer Fraction density discovers properties of n-particle 3D clusters. The paper demonstrates a discrete row of 3D clusters’ particle numbers and gives estimations for bond energies of these clusters.

Improving the electrochemical stability of AZ31 Mg alloy in a 3.5wt.%NaCl solution via the surface functionalization of plasma electrolytic oxidation coating

The unique interactions between hexadecanoic acid(HA)and albumin(ALB)molecules on the surface of the porous layer of AZ31 Mg alloy were exploited to fabricate a novel hybrid composite film with excellent electrochemical stability in a 3.5 wt.%Na Cl solution.Herein,the inorganic layer(IL)obtained by plasma electrolytic oxidation of AZ31 Mg alloy in an alkaline-phosphate-WO_(3)electrolyte was soaked in an organic solution composed of ALB and HA for 10 and 24 h at 60℃.Although albumin and HA may coexist on the same surface of IL,the higher reactivity of ALB molecules would prevent the formation of a thick layer of HA.The donor-acceptor complexes formed due to the unique interactions between ALB and/or HA and IL surface would reduce the area exposed to the corrosive species which in turn would efficiently protect the substrate from corrosion.The porous structure of the IL would provide preferable sites for the physical and chemical locking triggered by charge-transfer phenomena,leading to the inhomogeneous nucleation and crystal growth of a flowery flakes-like organic layer.DFT calculations were performed to reveal the primary bonding modes between the ALB,HA,and IL and to assess the mechanistic insights into the formation of such novel hybrid composites.

Analysis of Influential element of Building Economy in Zhengzhou, Henan

With the rapid development of Chinese society, building economy developped more and more rapidly and management job is increasingly onerous. In this paper, using rough set to Henan province Zhengzhou city building economy undertakes an analysis, extracting the important attribute information and removing of miscellaneous information, getting restricting the building economy development influence factor, and analysis of building economy of the intrinsic law of development, the developers were reasonable guiding to promote business buildings and building economic development, helping government departments to carry out economic decisions.

Structural modeling of a typical gas turbine system

This paper presents an approach for the structural modeling and analysis of a typical gas turbine system. This approach has been applied to the systems and subsystems, which are integral parts of a typical gas turbine system. Since a gas turbine system performance is measured in terms of fluid flow ene：rgy transformations across its various assemblies and subassemblies, the performance of such subsystems affects the overall performance of the gas turbine system. An attempt has been made to correlate the associativity of such subsystems contributing to overall gas turbine system functional evaluation using graph theoretic approach. The characteristic equations at the system level as well as subsystem level have been developed on the basi.s of associativity of various factors affecting their performance. A permanent function has been proposed for the functional model of a gas turbine system, which further leads to selection, identification and optimal evaluation of gas turbine systems.

Optimal promotional policy of an innovation diffusion model incorporating the brand image in a segment-specific market

This paper addresses the problem of determining the optimal promotional policy for a diffusion model in a segment-specific market under the assumption that the additional demand of the new product also improves brand image in the form of goodwill of the firm.The model is framed with the assumption that the firm uses the mass and differentiated promotion effort for each segment.The differentiated promotional efforts target each market segment independently and the mass promotional effort reaches different segments with a fixed spectrum.We derive the optimal promotional effort policy for each segment using maximumprinciple and also analyze the stability of the dynamical system by constructing a Lyapunov function through the graph theoretic approach.The analysis gives a deep insight into how the promotional effort should be planned by the decision makers keeping in mind the financial constrains without hindering the promotional effort at the end of the planning period.

Knowledge-guided machine learning reveals pivotal drivers for gasto-particle conversion of atmospheric nitrate

Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle conversion process.This process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO_(3)^(-))).The mechanism betweenε(NO_(3)^(-))and its drivers is highly complex and nonlinear,and can be characterized by machine learning methods.However,conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient factors.It urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact ofε(NO_(3)^(-)).Here we introduce a supervised machine learning approachdthe multilevel nested random forest guided by theory approaches.Our approach robustly identifies NH4 t,SO_(4)^(2-),and temperature as pivotal drivers forε(NO_(3)^(-)).Notably,substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual results.Furthermore,our approach underscores the significance of NH4 t during both daytime(30%)and nighttime(40%)periods,while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest analysis.This research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.