Chain Matrix Research Articles

Development and Verification of a Multi-Physics Transport Code of Molten Salt Reactor Fission Products

The transport of fission products in molten salt reactors has attracted much attention. However, few codes can completely describe the transport characteristic, though the migration of fission products in the molten salt reactor is essential to estimate the source term, decay heat, and radiation shielding. This study built a program named ThorFPMC (Thorium Fission Products Migration Code) that can handle the multi-physics transport characteristic based on the flow burnup code ThorMODEc (Thorium MOlten Salt Reactor Specific DEpletion Code). A problem-related depletion chain compression method was applied to decrease the order of the solve matrix. The matrix exponential and splitting methods were applied to solve the steady state and transient calculation, respectively. Error analysis showed that for a specific problem, the simplified depletion chain matrix index method could solve the fission products migration equation with an arbitrary time-step with high speed (s) and high precision (10−4); the splitting method could reach a precision of 10−2 level for the full fuel depletion chain, multi-nodes, and transient problems. Compared to the Strang splitting method, the perturbation splitting method has higher precision and less time consumption. In summary, the developed programmer could describe the migration effect of fission products in molten salt reactors, which provides a significant tool for the design of molten salt reactors.

Electromagnetic Response of a Uniformly Moving Resistive Sheet

ABSTRACTBy using the finite‐difference time‐domain (FDTD) method and a two‐port network model, this letter investigates the response of a uniformly moving resistive sheet to illuminating electromagnetic plane waves, at normal incidence. The numerical Doppler frequency shifts and scattering matrix are matched with curve‐fitting expressions. The effects of motion on the impedance matrix, the chain matrix, and the continuity equations are analyzed. Moreover, the total energy of the transferred and reflected waves are studied for the cases of a resistive sheet moving toward or away from the plane wave source.

Forecasting Non-stationary Time Series Using Deep Learning in a Fuzzy Time Series Framework and its Application to Stock Markets

Non-stationary time series prediction is challenging due to its dynamic and complex nature. Fuzzy time series models offer a promising solution for forecasting such data, but a key challenge lies in partitioning the universe of discourse, which significantly impacts forecasting accuracy. Traditional fuzzy time series models often use equal-length interval partitioning, which is more suited for stationary data and limits their adaptability to non-stationary time series. This paper introduces a novel variable-length interval partitioning method designed specifically for non-stationary time series. The developed method combines a Long Short-Term Memory (LSTM) Autoencoder with K-means clustering, enabling dynamic, data-driven partitioning that adapts to the changing characteristics of the data. The LSTM Autoencoder encodes the time series, which is clustered using K-means, and intervals are defined based on cluster centers. Furthermore, the Variable Length Interval Partitioning-based Fuzzy Time Series model (VLIFTS) is developed by incorporating this partitioning method and the concepts of Markov chain and transition probability matrix. In this model, fuzzy sets are viewed as states of a Markov chain, and transition probabilities are used in the forecasting phase. The model is validated on stock market indices Nifty 50, NASDAQ, S&P 500, and Dow Jones. Stationarity and heteroscedasticity are tested using Augmented Dickey-Fuller (ADF) and Levene's tests respectively. Statistical forecast accuracy metrics Root Mean Squared Error (RMSE) and Mean Absolute Percent Error (MAPE) show that VLIFTS significantly improves forecasting accuracy over traditional models. This hybrid approach enhances fuzzy time series modelling and can be applied to various non-stationary time series forecasting problems.

Characterization of Riemerella anatipestifer Strains Isolated from Various Poultry Species in Poland.

Riemerella anatipestifer (R. anatipestifer) is one of the common pathogens found in poultry flocks, resulting in serious economic losses for the poultry industry due to high mortality, reduced growth rate, poor feed conversion, increased condemnations, and high treatment costs. The aim of this study was to phenotypically characterize phylogenetic relationships and assess the presence of resistance gene strains of R. anatipestifer obtained from various poultry species in Poland. A total of 57 isolates of Riemerella were included in this study. A polymerase chain reaction (PCR) and matrix assisted laser desorption ionization mass spectrometry (MALDI-TOF MS) were used for identification of the strains. The phylogenetic relationship of the R. anatipestifer isolates was determined by analysing the rpoB gene sequence. The susceptibility to antibiotics was evaluated by minimum inhibitory concentration (MIC) in liquid media. All of the field strains of R. anatipestifer were grouped into one of two clades resulting from rpoB gene sequencing. High MIC50 and MIC90 values were obtained for gentamycin, amikacin, and colistin. Low MIC50 and MIC90 values were obtained for amoxicillin cefuroxime, cefoperazone, piperacillin, and trimethoprim/sulfamethoxazole. Among the resistance genes, tet(X) and ermF were identified most frequently. This is the first phenotypic characterization of R. anatipestifer strains obtained from poultry flocks in Poland.

The Dynamic Behavior of a Single Semiflexible Ring Chain in a Linear Polymer Matrix

We studied the dynamic behavior of a single semiflexible ring in linear chain matrix based on a coarse-grained model using the molecular dynamics simulation approach. We found that that ring chains’ hollow centers are frequently filled with linear chains. However, as the rigidity of the linear chains increases, the linear chains arranged parallel to each other and the ring chain are temporary caged. As a result, the swing movement in the normal direction of the ring is significantly limited, and the relaxation time in the normal direction increases significantly. Our findings can help to understand the physical mechanism of the movement of the ring chain in ring–linear polymer blends at the microscopic level.

Prediction of the dynamics of land use land cover using a hybrid spatiotemporal model in Iran.

Human activities are prone to be the main drivers of land use land cover (LULC) changes, which have cascading effects on the environment and ecosystem services. The main objective of this study is to assess the historical spatiotemporal distributions of LULC changes as well as estimated future scenarios for 2035 and 2045 by considering the explanatory variables of LULC changes in Zanjan province, Iran. The LULC time-series technique was applied using three Landsat images for the years 1987, 2002, and 2019. Multi-layer Perceptron Artificial Neural Network (MLP-ANN) is applied to model the relationships between LULC transitions and explanatory variables. Future land demand was calculated using a Markov chain matrix and multi-objective land optimization in a hybrid simulation model. Validation of the model's outcome was performed using the Figure of Merit index. The residential area in 1987 was 6406.02ha which increased to 22,857.48ha in 2019 with an average growth rate of 3.97%. Agriculture increased annually by 1.24% and expanded to 149% (890,433ha) of the area occupied in 1987. Rangeland showed a decline concerning its area, with only about 77% (1,502,201ha) of its area in 1987 (1,166,767ha) remaining in 2019. Between 1987 and 2019, the significant net change was a conversion from rangeland to agricultural areas (298,511ha). Water bodies were 8ha in 1987, which increased to 1363ha in 2019, with an annual growth rate of 15.9%. The projected LULC map shows the rangeland will further degrade from 52.43% in 2019 to 48.75% in 2045, while agricultural land and residential areas would be expanded to 940,754ha and 34,727ha in 2045 from 890,434ha and 22,887ha in 2019. The findings of this study provide useful information for the development of an effective plan for the study area.

Constraint Release Rouse Mechanisms in Bidisperse Linear Polymers: Investigation of the Release Time of a Short-Long Entanglement

Despite a wide set of experimental data and a large number of studies, the quantitative description of the relaxation mechanisms involved in the disorientation process of bidisperse blends is still under discussion. In particular, while it has been shown that the relaxation of self-unentangled long chains diluted in a short chain matrix is well approximated by a Constraint Release Rouse (CRR) mechanism, there is no consensus on the value of the average release time of their entanglements, τobs, which fixes the timescale of the CRR relaxation. Therefore, the first objective of the present work is to discuss the different approaches proposed to determine this time and compare them to a large set of experimental viscoelastic data, either newly measured (poly(methyl-)methacrylate and 1,4-polybutadiene blends) or coming from the literature (polystyrene and polyisoprene blends). Based on this large set of data, it is found that with respect to the molar mass of the short chain matrix, τobs follows a power law with an exponent close to 2.5, rather than 3 as previously proposed. While this slight change in the power law exponent does not strongly affect the values of the constraint release times, the results obtained suggest the universality of the CRR process. Finally, we propose a new description of τobs, which is implemented in a tube-based model. The accurate description of the experimental data obtained provides a good starting point to extend this approach to self-entangled binary blends.

Healable carbon fiber reinforced epoxy composites: Synchronous healing of matrix and interface damage

AbstractThermosetting composites are a range of high‐performance materials with important applications in multiple fields. However, inaccessible damage such as microcracks ingress from the environment may appear with long term use. Over time, the micro‐damage further expands and leads to material failure. The damage includes the interface damage and the matrix damage. Therefore, the healing technology of composites needs to respond and heal the micro‐damage of the interface and resin in the composites synchronously. In this work, siloxane dynamic bond was introduced into matrix and interface respectively. Specifically, a new type of crosslinker containing polysiloxane was synthesized and used to cure epoxy resin. Polyhedral oligomeric silsesquioxane was modified on the surface of carbon fibers (CFs) by sizing method. The prepared resin showed great mechanical properties and thermal stability, while the mechanical strength and surface activity of the CFs were also improved. Due to siloxane chain exchange reaction, matrix and interface could healing synchronously with good healing efficiency. Furthermore, the composites could retain about 65% of their original mechanical properties after healing multiple times. Composites realized synchronous healing of the resin matrix and the interface and laid foundation for extending the service life and performance of advanced resin‐based composites.

Intrinsic stiffening and dynamic softening by cholesterol.

The stiffness of a lipid bilayer is the key mechanical constant for understanding membrane reshaping in the cell. Cholesterol, present in the plasma membrane at high levels, has ambiguous effects on stiffness that appears to depend strongly on the lipid matrix. Inferred from long length and time scale experiments (e.g., X-ray diffraction and GUV fluctuations) cholesterol has little effect on a simple di-oleoyl-phosphatidylcholine (DOPC) bilayer. Inferred from dynamic experiments limited to nanosecond timescales (e.g., neutron spin echo) DOPC bilayers appear stiffer. In a saturated chain matrix like DMPC, both x-ray and neutron spin echo experiments indicate stiffening.

Analytical formulas for calculating the electrical characteristics of multiparameter arbitrary configurational homogenous ladder networks

SummaryMost electrical systems are represented as ladder networks made up of resistances, inductances and capacitances. Electrical characteristics of these networks, such as voltages, currents and equivalent impedance, are difficult to compute because they require solving multiple differential‐algebraic equations. Further, circuit simulator‐based modelling is a time‐consuming and tedious process for simulating large networks with multiparameters present in various configuration. This paper presents generalised analytical formulae for computing the electrical properties of any multiparameter arbitrary section homogenous ladder network that can be reduced to series and shunt impedances. Circuit principles, chain matrix decomposition and linear transformation are used to derive the symbolic expressions. Simply plugging the values of the series and shunt impedances, as well as the number of sections, into the derived expression yields the impedance. Thereafter, the calculated impedances can be used to calculate the nodal voltages and mesh currents. Simulation results of a six‐section homogeneous ladder network are presented and compared with those of other existing techniques to validate the derived expressions. The derived expressions eliminate the need for recursive relations, complex integro‐differential equations, large state‐space matrices and simulator‐based circuit modelling, which are all clearly advantageous.

Human exhaled air diagnostic markers for respiratory tract infections in subjects receiving mechanical ventilation

Diagnosing respiratory tract infections (RTIs) in critical care settings is essential for appropriate antibiotic treatment and lowering mortality. The current diagnostic method, which primarily relies on clinical symptoms, lacks sensitivity and specificity, resulting in incorrect or delayed diagnoses, putting patients at a heightened risk. In this study we developed a noninvasive diagnosis method based on collecting non-volatile compounds in human exhaled air. We hypothesized that non-volatile compound profiles could be effectively used for bacterial RTI diagnosis. Exhaled air samples were collected from subjects receiving mechanical ventilation diagnosed with or without bacterial RTI in intensive care units at the Johns Hopkins Hospital. Truncated proteoforms, a class of non-volatile compounds, were characterized by top-down proteomics, and significant features associated with RTI were identified using feature selection algorithms. The results showed that three truncated proteoforms, collagen type VI alpha three chain protein, matrix metalloproteinase-9, and putative homeodomain transcription factor II were independently associated with RTI with the p-values of 2.0 × 10−5, 1.1 × 10−4, and 1.7 × 10−3, respectively, using multiple logistic regression. Furthermore, a score system named ‘TrunScore’ was constructed by combining the three truncated proteoforms, and the diagnostic accuracy was significantly improved compared to that of individual truncated proteoforms, with an area under the receiver operator characteristic curve of 96.9%. This study supports the ability of this noninvasive breath analysis method to provide an accurate diagnosis for RTIs in subjects receiving mechanical ventilation. The results of this study open the doors to be able to potentially diagnose a broad range of diseases using this non-volatile breath analysis technique.

Accurate Design Method for Millimeter Wave Distributed Amplifier Based on Four-Port Chain (ABCD) Matrix Model

This article presents a matrix-based model suitable for millimeter-wave (mm-wave) distributed amplifier (DA) design, based on four-port chain (ABCD) formalism. Using this model, an algorithmic design methodology for DA, built upon a loss-compensation technique, is also provided that maximizes its bandwidth (BW) for a given flatness goal. The design approach provides fast and accurate design space exploration (DSE) plots that enable one to examine the tradeoffs between gain, BW, power consumption ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {P}_{\mathrm {DC}}$ </tex-math></inline-formula> ), and the size and number of Gm-cells, and arrive at the optimum desired design. Its benefit is demonstrated by means of a computer-automated design (CAutoD) example where 55-nm CMOS STMicroelectronics (ST) process is used and DAs with BWs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ge80$ </tex-math></inline-formula> GHz were desired to be sized; reporting 216 feasible DA options to explore from. The global optimum DA amplifying frequencies up to 100 GHz was then implemented as a circuit prototype. The measured DA provided 6.7-dB power gain while requiring a power consumption ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {P}_{\mathrm {DC}}$ </tex-math></inline-formula> ) of 30 mW from a 1.2-V supply. The chip occupied a total area of 0.83 mm2. Compared to state-of-the-art FET-based small-signal DAs, the fabricated circuit reports the highest gain-bandwidth product (GBP) per <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {P}_{\mathrm {DC}}$ </tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {GBP}} \mathord {\left /{ {\vphantom {{\mathrm {GBP}} \mathrm {P}_{\mathrm {DC}}}} }\right. } \mathrm {P}_{\mathrm {DC}}$ </tex-math></inline-formula> ) of 6.01 GHz/mW while being power-efficient.

SOLVING SYSTEMS OF SECOND-ORDER MATRIX EQUATIONS

Matrix equations and systems of matrix equations are widely used in control system optimization problems. However, there are developed only methods for solving the most popular matrix equations, like Riccati and Lyapunov equations .There is no universal approach to solving all problems of this class. The previously considered method of solving systems of algebraic equations over a field of real numbers [1] is summarized in this paper and a scheme for systems of polynomial matrix equations of the second degree with many unknowns is proposed. Also, the recurrent formula for developing a solution into a chain matrix fraction is given. The convergence of the proposed method is investigated. The results of numerical experiments that confirm the validity of theoretical calculations and the effectiveness of the proposed scheme are presented.

Antimicrobial Resistance of Staphylococcus sp. Isolated from Cheeses.

Simple SummaryAs far as it is known, studies dealing with antimicrobial resistance in certain species of staphylococci, in particular, S. chromogenes and S. simulans, isolated from products made from unpasteurized milk are limited. In addition to that, little attention was paid to the resistance of staphylococcal isolates from regional sheep and goat cheeses. At this level, works are published that focus on the evaluation of resistance from only one sheep product, Bryndza. Other studies are only focused on the evaluation of resistance from raw sheep’s or goat’s milk. The study contributes to the knowledge of the possible spread of antimicrobial resistance from the farm to the final consumer in this area.S. aureus and some species of coagulase-negative staphylococci, including S. chromogenes and S. simulans, commonly cause intramammary infections. However, little attention was paid to the antimicrobial resistance of these species with respect to their occurrence in dairy products, for example, popular sheep and goat cheeses made from unpasteurized milk. The aim of this study was to investigate such sheep and goat cheeses for the occurrence and antimicrobial resistance of the relevant staphylococci species. The staphylococcal isolates were identified by polymerase chain reaction (130 isolates) and matrix assisted laser desorption/ionization time-of-flight mass spectrometry. The most common species of S. aureus (56 isolates) were identified, as well as S. chromogenes (16 isolates) and S. simulans (10 isolates). Antimicrobial resistance to penicillin, oxacilin, ceftaroline, teicoplanin, gentamicin, erythromycin, tetracycline and ofloxacin was subsequently determined in these species using the agar dilution method. The highest resistance was confirmed in all species, especially to penicillin (91%) and erythromycin (67%). The highest sensitivity was confirmed to ofloxacin (83%). Due to the high incidence of penicillin and oxacilin-resistant staphylococci, the mecA gene was detected by polymerase chain reaction, which was confirmed only in S. aureus isolates (19%). Our study shows that the tested strains (77%) were resistant to more than one antibiotic at a time.

A Regional Perspective on Social Exclusion in European Regions: Context, Trends and Policy Implications

Social exclusion has become a popular topic in the policy agendas of European governments, especially after the global financial crisis of 2007–2009 hit the continent as hard as it could. The existing literature highlights the presence of spatial patterns in social exclusion, although previous contributions consist of local or national level studies, lacking a broader continental perspective. This work resorts to regional data covering 20 EU countries and aims to characterise the nature of spatial patterns, distinguishing between spatial heterogeneity and pure spatial autocorrelation. Using the Spatial Markov Chain Matrix, we find that the strong clusterisation process unfolded by previous studies tends to become less intense if the role of socio-economic covariates is taken into account. Socio-economic factors represent in other words a containment cage that reduces the extent of neighbour influence. Net of the covariates, we identify clusters of regions in Southern Europe where high levels of social exclusion constitute a structural problem, calling for long-term public intervention. The policy implications of our findings are then outlined.

Determination of Uropathogenic Escherichia coli in Urine by an Immunobiosensor Based Upon Antigen-Antibody Biorecognition with Fluorescence Detection and Bead-Injection Analysis

Uroinfections, which are mainly caused by pathogenic bacteria, especially uro-pathogenic E. coli (UPEC), are among the major health problems in the modern world. The current gold standard for the identification of uroinfection causing pathogens in urine is microbiological cultivation, but uro-pathogens are also determined with quantitative polymerase chain reaction (qPCR) and matrix assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry. However, it takes several hours or days to obtain the results. A promising option for rapid identification of pathogens is the application of biosensors. The aim of the current study was to develop a biosensor for the fast detection of UPEC in urine samples allowing achieving a limit of detection below 103 CFU/mL, essential for the diagnostics of recurrent uroinfections and related diseases. First, the pathogens were attached onto a single use column and specifically detected using E. coli antibodies conjugated with a fluorescence marker. The bead-injection analysis platform for fluidics provided E. coli limits of detection and quantification in 150 µL urine samples <3 and <5 cells/mL, respectively. The time of analysis was 17 min. The biosensor results were in good correlation with the results obtained with other methods, indicating that the complex urine matrix of UPEC patients did not affect the biosensor measurements.

Symposium Title: Increasing the Rigor, Reproducibility, and Impact of Psychophysiological Research: Power, Pipelines, Reliability, and Open Access Advantages

Grid-based location prediction algorithms for predicting future locations of mobile device users have been proposed and evaluated. Most of the studies, however, ignore the fact that prediction accuracy is highly dependent on the user’s current behavior (i.e., whether current user behavior complies with previous behavior patterns), the type of predicted location (e.g., home, shopping center, work), the confidence in the prediction, and the error of the location sensor. In this paper we propose four methods for providing a dynamic and effective confidence area for each predicted location provided by any grid-based location prediction algorithm. We define confidence area as the extended area defined by radius around the predicted location (cell) that the user might be in, and confidence as the level of assurance that the user will be in the predicted area. We applied the proposed radius prediction methods on the output of three representative location prediction algorithms (frequent cells, Markov chain model and matrix factorization) using three different datasets, and compared the methods with the previously proposed fixed radius approach. Our results demonstrate the ability to dynamically determine a confidence radius that increases prediction accuracy while maintaining a small average radius.

Survivability and Disaster Recovery Modeling of Cellular Networks Using Matrix Exponential Distributions

Cellular network design must incorporate disaster response and repair scenarios. Requirements for high reliability and low latency often fail to incorporate network survivability for mission critical services. This paper defines a practical modeling approach using a Markov chain Matrix Exponential (ME) model. Transient and the steady state representations of system repair models, namely, fast and slow (i.e., crew-based) repairs for networks consisting of a multiple repair crews have been analyzed. Failures are exponentially modeled as per common practice, but ME distributions describe the more complex recovery processes. The model used in this paper is evaluated for varying number of repair crews, base stations, repair models and squared coefficient of variation values. This model is scalable for larger networks, calculates the restoration and network availability times, consists of asymptotic approximations to estimate network availability and determines the optimal number repair crews required. This ME model shows how survivable networks can be designed based on controlling numbers of crews, times taken for individual repair stages in the repair process, and the balance between fast and slow repairs.

Interpenetrating Lattices with Tailorable Energy Absorption in Tension

This paper introduces the chain lattice, a hierarchical porous structure comprising two interpenetrating cellular solids. One constituent toughens the material and prevents catastrophic localized failure while the other serves as a porous matrix which densifies to absorb energy during tensile loading. Through tension testing, we demonstrate 3D-printed plastic chain lattices that exhibit delocalized damage and an order of magnitude increment in strain-to-failure over the fully dense base material. These experiments validate a micromechanics-based model of tensile specific energy absorption, which we then use in a parametric study on the effects of chain geometry and matrix properties on tensile behavior. We find that ceramic chain lattices can achieve an order of magnitude improvement in tensile specific energy absorption over the fully dense material, in line with the improvement seen when forming monolithic ceramics into fiber-reinforced ceramic matrix composites. The experiments and analysis highlight the ability of the chain lattice to impart damage-tolerance to 3D-printable materials that are normally brittle and flaw-sensitive.

Using the Markov chain to predict the outcome of an election in a given situation

This paper used Markov Chain, a mathematical model that is often utilized in predicting, to forecast the result of the election process of a person in a specific circumstance. In the way of modeling, starting from the tree diagram to the model of Markov Chain and related transition matrix, this paper aimed at the election result, which relates the object of study, a specific candidate. The research data are collected from the prompt of the research question and the calculation of coding based on the transition matrix. The result is, when it is the 64th year of that specific candidate running for the reelection campaign, he will be likely to retire from politics.