Matrix Solution Research Articles

Chromium partitioning induced evolution of texture, grain boundary constitution, and corrosion behavior of Cu[sbnd]Cr coatings electrodeposited at different temperatures

The corrosion behavior of CuCr coating electrodeposited at three different temperatures (15 °C, 30 °C, 45 °C) was examined. Cr is partitioned in the grain boundaries and as a solute in the Cu matrix. The formation of Cr-oxide at the grain boundaries enhanced the corrosion resistance of the coatings, whereas the presence of Cr as solute significantly altered the matrix micro-texture. The 15 °C deposited coating exhibited the lowest corrosion rate due to higher Cr solute, smaller grain size, (111) surface texture, and a higher fraction of coincidence site lattices. The 45 °C deposited coating exhibited the highest corrosion rate due to lower Cr solute, higher energy (110) surface texture, and higher fraction of high energy grain boundaries.

Анализ свойств региональных геодезических ГНСС-сетей, построенных относительным методом. Общий случай

In this paper, we continue to study the geodetic stations adjusted coordinates’ statistical properties in permanently operated networks. Analytical reconstructing the variance-covariance matrix of a real vector solution obtained from processing a redundant set of the same GNSS observation session’s measured vectors is performed. The mentioned matrix is compared with the same one, of the vector solution, resulted on the measured lines’ total set basis. Common properties of both arrays are revealed, as well as discrepancies in features for different types of solutions. As a suitable tool for studying the latter, the corresponding absolute way is offered

Application of Non-Linear Evolution Stochastic Equations with Asymptotic Null Controllability Analysis

This paper investigated system of stochastic differential equations with prominence on disparities of drift parameters. These problems were solved analytical by adopting the Ito’s method of solution and three different investment solutions were obtained consequently. The necessary conditions were achieved which govern various drift parameters in assessing financial markets. Therefore, the impressions on each solution of investors in financial markets were analyzed graphically. Secondly, stock price data of Transco, LTD were analyzed which covariance matrix were considered and analysis were logically extended to stochastic vector differential equation where control measures were incorporated that would help in predicting different stock price processes, and the result obtained by exploring the properties of the fundamental matrix solution where asymptotic null controllability results were obtained by the singularity of the controllability matrix a function of the drift. Finally, the effects of the significant parameters of stochastic variables were successfully discussed.

Soliton equations: admitted solutions and invariances via B\"acklund transformations

A couple of applications of B\"acklund transformations in the study of nonlinear evolution equations is here given. Specifically, we are concerned about third order nonlinear evolution equations. Our attention is focussed on one side, on proving a new invariance admitted by a third order nonlinear evolution equation and, on the other one, on the construction of solutions. Indeed, via B\"acklund transformations, a {\it B\"acklund chart}, connecting Abelian as well as non Abelian equations can be constructed. The importance of such a net of links is twofold since it indicates invariances as well as allows to construct solutions admitted by the nonlinear evolution equations it relates. The present study refers to third order nonlinear evolution equations of KdV type. On the basis of the Abelian wide B\"acklund chart which connects various different third order nonlinear evolution equations an invariance admitted by the {\it Korteweg-deVries interacting soliton} (int.sol.KdV) equation is obtained and a related new explicit solution is constructed. Then, the corresponding non-Abelian {\it B\"acklund chart}, shows how to construct matrix solutions of the mKdV equations: some recently obtained solutions are reconsidered.

A Surface Imprinted Polymer EIS Sensor for Detecting Alpha-Synuclein, a Parkinson's Disease Biomarker.

Parkinson's Disease (PD) is a debilitating neurodegenerative disease, causing loss of motor function and, in some instances, cognitive decline and dementia in those affected. The quality of life can be improved, and disease progression delayed through early interventions. However, current methods of confirming a PD diagnosis are extremely invasive. This prevents their use as a screening tool for the early onset stages of PD. We propose a surface imprinted polymer (SIP) electroimpedance spectroscopy (EIS) biosensor for detecting α-Synuclein (αSyn) and its aggregates, a biomarker that appears in saliva and blood during the early stages of PD as the blood-brain barrier degrades. The surface imprinted polymer stamp is fabricated by low-temperature melt stamping polycaprolactone (PCL) on interdigitated EIS electrodes. The result is a low-cost, small-footprint biosensor that is highly suitable for non-invasive monitoring of the disease biomarker. The sensors were tested with αSyn dilutions in deionized water and in constant ionic concentration matrix solutions with decreasing concentrations of αSyn to remove the background effects of concentration. The device response confirmed the specificity of these devices to the target protein of monomeric αSyn. The sensor limit of detection was measured to be 5 pg/L, and its linear detection range was 5 pg/L-5 µg/L. This covers the physiological range of αSyn in saliva and makes this a highly promising method of quantifying αSyn monomers for PD patients in the future. The SIP surface was regenerated, and the sensor was reused to demonstrate its capability for repeat sensing as a potential continuous monitoring tool for the disease biomarker.

A K-means Supported Reinforcement Learning Framework to Multi-dimensional Knapsack

In this paper, we address the difficulty of solving large-scale multi-dimensional knapsack instances (MKP), presenting a novel deep reinforcement learning (DRL) framework. In this DRL framework, we train different agents compatible with a discrete action space for sequential decision-making while still satisfying any resource constraint of the MKP. This novel framework incorporates the decision variable values in the 2D DRL where the agent is responsible for assigning a value of 1 or 0 to each of the variables. To the best of our knowledge, this is the first DRL model of its kind in which a 2D environment is formulated, and an element of the DRL solution matrix represents an item of the MKP. Our framework is configured to solve MKP instances of different dimensions and distributions. We propose a K-means approach to obtain an initial feasible solution that is used to train the DRL agent. We train four different agents in our framework and present the results comparing each of them with the CPLEX commercial solver. The results show that our agents can learn and generalize over instances with different sizes and distributions. Our DRL framework shows that it can solve medium-sized instances at least 45 times faster in CPU solution time and at least 10 times faster for large instances, with a maximum solution gap of 0.28% compared to the performance of CPLEX. Furthermore, at least 95% of the items are predicted in line with the CPLEX solution. Computations with DRL also provide a better optimality gap with respect to state-of-the-art approaches.

Engineering of the Fano resonance spectral response with non-Hermitian metasurfaces by navigating between exceptional point and bound states in the continuum conditions.

We address the engineering of Fano resonances and metasurfaces, by placing it in the general context of open non-Hermitian systems composed of coupled antenna-type resonators. We show that eigenfrequency solutions obtained for a particular case of scattering matrix are general and valid for arbitrary antenna radiative rates, thanks to an appropriate transformation of parametric space by simple linear expansion and rotation. We provide evidence that Parity-Time symmetry phase transition path and bound states in continuum (BIC) path represent the natural axis of universal scattering matrix solutions in this parametric coupling-detuning plane and determine the main characteristics of Fano resonance. Specifically, we demonstrate the control of asymmetry and sharpness of Fano resonance through navigation between BIC and PT-symmetric phase transition exceptional point. In particular, we demonstrate a fully symmetric Fano resonance in a system of two coupled bright and dark mode resonators. This result goes beyond current wisdom on this topic and demonstrates the universality of scattering matrix eigenfrequency solutions highlighted in our study. The validity of our approach is corroborated through comparison with experimental and full 3D numerical simulations results published in the literature making it thus possible to grasp a large body of experimental work carried out in this field. The detrimental impact of absorption losses on the contrast of the Fano resonance, which must be two orders of magnitude lower than the radiative losses, is also evidenced.

ENCAPSULATION OF VITAMIN D IN THE EXINE-ALGINATE-CHITOSAN MICROCAPSULE SYSTEM

The insufficiency of vitamin D, resulting from inadequate exposure to sunlight and/or insufficient dietary intake, remains a major public health concern on a global scale. In this study, vitamin D2 and D3 were microencapsulated using sporopollenin exine microcapsules extracted from Cedrus libani pollens. After loading vitamin D into the microcapsules, they were coated with chitosan, an edible, biocompatible, and mucoadhesive polysaccharide, and alginate (a food additive agent coded E401). Exine microcapsules were extracted by microwave irradiation-assisted chemical method, and structural and morphological examination of exine structures was performed by FT-IR, TGA, SEM, and SEM-EDX analyses. After loading vitamin D into microcapsules in an ethanol medium, the loaded microcapsules were immobilised into the alginate matrix in a calcium chloride solution. D2 and D3 were loaded into 100 mg of sporopollenin exine microcapsules, resulting in loading efficiencies of 31.5 mg and 16.0 mg, respectively. The vitamin D release performance of the microcapsules was examined depending on time and temperature after they were coated with a thin chitosan layer. The release of the highest amount of vitamin D2 and D3 occurred at a temperature of 37°C. Encapsulating vitamin D molecules in chitosan and alginate creates a barrier against degrading environmental conditions, which helps prevent the loss of vitamin D biological activity. This can improve vitamin D dietary supplements' storage, preservation, and marketing requirements.

Magnetic nanoparticles (MNPs) based additively manufactured memory devices

Magnetic nanoparticles (MNPs) in a suspension have been shown to change resistance by an order of magnitude based on an applied field. We have prepared the MNPs samples with matrices of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PDOT:PSS) or H2O, and Co MNPs and carried out the magnetoresistive measurements. A switch-based model is used to understand the mechanism for the change in resistance. We further propose devices for memory based on MNPs. Building blocks for these devices are then fabricated using additive manufacturing techniques and measurements of change in resistance under the influence of a magnetic field are conducted. Niche applications of additive manufacturing techniques to the fabrication of these devices are proposed. The device uses MNPs suspended in a soft matter matrix. The application of a magnetic field is used to move the MNPs to or away from electrical contacts. Depending on the change of position of the MNPs, a connection is either made or broken, which can act as a 1 or a 0. The measured change in resistance observed in such devices is more than an order of magnitude depending on the matrix solution. The proposed device and its manufacturing process could be feasible for magnetic memory and in-memory computing devices on any flexible substrates.

Degradation Characteristics of Glass Fiber Reinforced Plastics Using Unsaturated Polyester as Matrix in Weak Alkaline Aqueous Solution

Degradation Characteristics of Glass Fiber Reinforced Plastics Using Unsaturated Polyester as Matrix in Weak Alkaline Aqueous Solution

A Method for Industrial Robots to Grasp and Detect Parts of Instrument under 3D Visual Guidance

<p>Guiding industrial robots to complete grasping tasks through machine vision is an important part of achieving autonomous robot operation. This article explores the control method of industrial robots under 3D vision, focusing on the feature that two-dimensional vision can only perform color and pose recognition but lacks depth recognition. Firstly, a high-precision point cloud registration calibration matrix solution method is proposed. Then, an improved recognition model is designed to address the issue of how vision guides robots to grasp and detect. This model integrates feature extraction and object detection modules, and describes the parameters of each module. Finally, the effectiveness of the proposed method is verified in the assembly scene of gas instruments. Finally, experimental results show that, the method proposed in this article can limit the grasping accuracy to within 2 millimeters in guiding robots to grasp detection scenes, achieving the expected effect.</p> <p> </p>

Evaluation of commercial nanofiltration and reverse osmosis membrane filtration to remove per-and polyfluoroalkyl substances (PFAS): Effects of transmembrane pressures and water matrices.

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are now widely found in aquatic ecosystems, including sources of drinking water and portable water, due to their increasing prevalence. Among different PFAS treatment or separation technologies, nanofiltration (NF) and reverse osmosis (RO) both yield high rejection efficiencies (>95%) of diverse PFAS in water; however, both technologies are affected by many intrinsic and extrinsic factors. This study evaluated the rejection of PFAS of different carbon chain length (e.g., PFOA and PFBA) by two commercial RO and NF membranes under different operational conditions (e.g., applied pressure and initial PFAS concentration) and feed solution matrixes, such as pH (4-10), salinity (0- to 1000-mM NaCl), and organic matters (0-10mM). We further performed principal component analysis (PCA) to demonstrate the interrelationships of molecular weight (213-499 g·mol-1 ), membrane characteristics (RO or NF), feed water matrices, and operational conditions on PFAS rejection. Our results confirmed that size exclusion is a primary mechanism of PFAS rejection by RO and NF, as well as the fact that electrostatic interactions are important when PFAS molecules have sizes less than the NF membrane pores. PRACTITIONER POINTS: Two commercial RO and NF membranes were both evaluated to remove 10 different PFAS. High transmembrane pressures facilitated permeate recovery and PFAS rejection by RO. Electrostatic repulsion and pore size exclusion are dominant rejection mechanisms for PFAS removal. pH, ionic strength, and organic matters affected PFAS rejection. Mechanisms of PFAS rejection with RO/NF membranes were explained by PCA analysis.

Boosting Circularly Polarized Luminescence from Alkyl-Locked Axial Chirality Scaffold by Restriction of Molecular Motions.

Boosting the circularly polarized luminescence of small organic molecules has been a stubborn challenge because of weak structure rigidity and dynamic molecular motions. To investigate and eliminate these factors, here, we carried out the structure-property relationship studies on a newly-developed axial chiral scaffold of bidibenzo[b,d]furan. The molecular rigidity was finely tuned by gradually reducing the alkyl-chain length. The environmental factors were considered in solution, crystal, and polymer matrix at different temperatures. As a result, a significant amplification of the dissymmetry factor glum from 10-4 to 10-1 was achieved, corresponding to the situation from (R)-4C in solution to (R)-1C in polymer film at room temperature. A synergistic strategy of increasing the intramolecular rigidity and enhancing the intermolecular interaction to restrict the molecular motions was thus proposed to improve circularly polarized luminescence. The though-out demonstrated relationship will be of great importance for the development of high-performance small organic chiroptical systems in the future.

Dynamic Time‐Dependent Emission in Solution and Stable Dual Emission in Solid Matrix Exhibited by a Single‐Component Fluorescence System

AbstractOrganic luminescent materials with time‐dependent emission colors show promising applications in the fields of chemical sensing, high‐resolution bioimaging, and high‐security information encryption. Herein a time‐dependent fluorescence system based on a spirocyclic scaffold‐bridged cyanostilbene dimer (SDCS) as the single component in a mixed CH3CN/H2O solvent is presented. Specifically, the original orange‐emitting nanoparticles prepared from SDCS by reprecipitation can transform into green‐emitting nanosheets over time driven by supramolecular self‐assembly. It is worth noting that such a transformation rate can be controlled by tuning the water fraction. Based on these unique properties, fluorescent binary codes are developed, enabling time‐dependent information encryption with a higher level of security. Moreover, the dual color can be individually fixed by solid matrices such as hydrogel or powder. The obtained luminescent powders are successfully used in two‐color fluorescence imaging of latent fingerprints. This work demonstrates the use of a supramolecular strategy to control multiple emissions in a single‐component system for multifunctional applications.

Efficient hydraulic and thermal simulation model of the multi-phase natural gas production system with variable speed compressors

The natural gas production system is a very complex system, involving pipelines, compressors and valves with different structures and calculation parameters. It can be divided into two different parts: gas production and collection pipe networks. Fluid flow in the gas production pipe network is multi-phase flow. Fluid flow in the gas collection pipe network is single-phase flow. It is imperative to consider gas production and collection pipe networks in conjunction to ensure accurate operation simulation. The complexity of the network structures and flow statuses can cause computational instability or even divergence problems in simulations when coupling these two types of pipe networks. To address this issue, an automatic and practical node numbering method is proposed for the large-scale pipe network, which can ensure the full rank of the solution matrix and computational stability. On the other hand, due to the need for frequent adjustments of the pipe networks to satisfy the production demand, computational boundary treatments of constant inlet/outlet pressure, constant flow rate or constant pressure ratio for the compressor stations are not suitable. To address this issue, the characteristic curves of compressors are coupled into the simulation of pipe networks to represent the variable-speed centrifugal and reciprocating compressors.The accuracy and robustness of the proposed models and numerical methods are validated through comparisons with software results and field data. The simulation time is only within 0.5 s for the natural gas production system with 210 nodes and 176 pipelines. Our model has relative deviations within 3% in both pressure and flow rate compared to field data and within 10% compared to software.

A Systematic Investigation of the Effects of Standard‐Sample Concentration Mismatch during Fe Isotope Measurement by MC‐ICP‐MS

Advances in multi‐collector inductively coupled plasma‐mass spectrometry (MC‐ICP‐MS) have led to the widespread use of iron (Fe) isotopes to elucidate the (bio)geochemical history of a range of environments. To generate Fe isotope ratio measurements, standard‐sample bracketing (SSB) is commonly used to correct for instrumental mass bias inherent to MC‐ICP‐MS. However, SSB is only accurate when sample and isotope standard Fe concentrations match, in addition to the bulk solution matrix. When the Fe concentrations differ, Fe isotope ratio measurement results may be inaccurate, a phenomenon known as the "self‐induced matrix effect." This study systematically characterised the self‐induced matrix effect for dry plasma Fe isotope ratio measurements on three MC‐ICP‐MS instruments and three introduction systems. Our extensive dataset indicates that: (1) the degree of mass bias is consistent regardless of MC‐ICP‐MS front‐end design, (2) the degree of mass bias becomes less reproducible as the concentration difference between the sample and bracketing standard increases, and (3) this applies to both pure Fe solutions and solutions from geological materials. This study reinforces the requirement to match bracketing standard and sample concentrations within 10% and provides a correction method for that fall beyond the recommended concentration range to subsequently allow for proper concentration matching during SSB.

Enhanced mechanical properties of high pressure solidified CoCrFeNiMo0.3 high entropy alloy via nano-precipitated phase

CoCrFeNiMo0.3 high entropy alloy was prepared by vacuum arc melting. The effects of different solidification pressures on the microstructure and mechanical properties of CoCrFeNiMo0.3 high entropy alloy were investigated. All ambient pressure (0.1 MPa) Mo0.3, 4 GPa Mo0.3 and 7 GPa Mo0.3 alloys exhibit two kinds of structures. The FCC structure is the solid solution matrix, and the precipitated phase enrich in Cr and Mo (σ phase) is distributed in the grain boundary of the matrix phase. With the increase of the test pressure, the size of the precipitated phase gradually decreases and the volume fraction increases obviously, and tends to form nano-scale particles. Meanwhile, the precipitated phase gradually diffused into the matrix due to the increase of the pressure. The results of nanoindentation tests show that the hardness of the alloy increases significantly with the increase of solidification pressure, especially that of 7 GPa Mo0.3 alloy, which is about 2 times that of 0.1 MPa Mo0.3 alloy. The strengthening mechanism of CoCrFeNiMo0.3 high entropy alloy with increased pressure is mainly nano-precipitated strengthening. This present findings not only pave the way for designing the structure and property regulation of high pressure solidified HEAs but also promote the application orientation of various property combinations. It provides the theoretical basis for its application and is expected to be used as structural materials in various fields.

A deep reinforcement learning model for dynamic job-shop scheduling problem with uncertain processing time

The dynamic job-shop scheduling problem (DJSP) is a type of scheduling tasks where rescheduling is performed when encountering the uncertainties such as the uncertain operation processing time. However, the current deep reinforcement learning (DRL) scheduling approaches are hard to train convergent scheduling policies as the problem scale increases, which is very important for rescheduling under uncertainty. In this paper, we propose a DRL scheduling method for DJSP based on the proximal policy optimization (PPO) with hybrid prioritized experience replay. The job shop scheduling problem is formulated as a sequential decision-making problem based on Markov Decision Process (MDP) where a novel state representation is designed based on the feasible solution matrix which depicts the scheduling order of a scheduling task, a set of paired priority dispatching rules (PDR) are used as the actions and a new intuitive reward function is established based on the machine idle time. Moreover, a new hybrid prioritized experience replay method for PPO is proposed to reduce the training time where samples with positive temporal-difference (TD) error are replayed. Static experiments on classic benchmark instances show that the make-span obtained by our scheduling agent has been reduced by 1.59% on average than the best known DRL methods. In addition, dynamic experiments demonstrate that the training time of the reused scheduling policy is reduced by 27% compared with the retrained policy when encountering uncertainties such as uncertain operation processing time.

Inductively coupled plasma optical emission spectroscopic determination of trace rare earth elements in highly refractory gadolinium zirconate (Gd2Zr2O7).

Gadolinium zirconate (Gd2Zr2O7) belongs to the category of burnable absorber (BA) material in nuclear reactors. The high neutron-absorption cross sections of Gd isotopes (155Gd and 157Gd) implement negative reactivity in the reactor core to control the excess reactivity of the fuel at the beginning of the fission cycle. The presence of other rare earth elements, which can come from the starting material and/or may be taken up during the synthesis steps, will affect the negative reactivity calculation. Thus the chemical quality assurance of Gd2Zr2O7 concerning the other trace rare earth impurities is indispensable. Here in this work, we decided to quantify thirteen trace rare earth elements in Gd2Zr2O7 by inductively coupled plasma optical emission spectroscopy (ICP-OES). One of the matrix elements viz., zirconium was separated via precipitation using d,l-mandelic acid. The trace rare earths were determined in the presence of gadolinium matrix in solution. It was found that all thirteen rare earth elements can be quantified in the range of 0.25 to 2.5 mg L-1 in the presence of 516 mg L-1 of Gd with a relative standard deviation of 1-3%. This corresponds to the determination of a minimum of 0.25 mg analyte per g of Gd2Zr2O7. The method detection limits of all thirteen rare earth elements range between 0.01 and 0.075 mg g-1. The proposed methodology was validated by analyzing synthetic standards and real samples with spike addition.

ФОРМИРОВАНИЕ УСТОЙЧИВОСТИ РАЗВИТИЯ ПРЕДПРИЯТИЯ ПРИ УПРАВЛЕНИИ В ТЕКУЩЕМ ПЕРИОДЕ СОВОКУПНОСТЬЮ МНОГОПРОДУКТОВЫХ ИННОВАЦИОННЫХ ПРОИЗВОДСТВ

Analyzing the sustainable development of innovative industries with the diversification of production for risky projects, this paper studies the possibilities and disadvantages of the traditional applica-tion of cost – volume – profit (CVP) analysis for assessing the implementation of multi-product production projects. The hypothesis is that the sustainable development of enterprises with multi-product production on the basis of the “corrective factors of formation” management model of the overall break-even point of the planned profit of the production with various characteristic initiating perturbations can be ensured by devel-oping a matrix of potential solutions “one initiating factor to one corrective management factor”. The model can to take into account deviations of constant and variable factors in the formation of the final planned prof-it for multi-product production, including the difference of clock productivity and planned periods for the production of individual products and changes in factors. The methodical approach is illustrated by examples of analytical calculations and generalized graphical representations of changes in the variable and fixed costs of one product at the expense of a possible change in variable costs of another product, and by adjusting the fixed costs at the expense of a possible change in the fixed costs of another product. The approach is largely universal under other boundary conditions for the pairs of initiating-correcting influence to assess the sus-tainability of the development of mono- and multi-product production.