Constituent Parameters Research Articles

Inverse Identification of Constituent Elastic Parameters of Ceramic Matrix Composites Based on Macro–Micro Combined Finite Element Model

Accurate material performance parameters are the prerequisite for conducting composite material structural analysis and design. However, the complex multiscale structure of ceramic matrix composites (CMCs) makes it extremely difficult to accurately obtain their mechanical performance parameters. To address this issue, a CMC micro-scale constituents (fiber bundles and matrix) elastic parameter inversion method was proposed based on the integration of macro–micro finite element models. This model was established based on the μCT scan data of a plain-woven CMC tensile specimen using the chemical vapor infiltration (CVI) process, which could reflect the real microstructure and surface morphology characteristics of the material. A BP neural network was used to predict the multiscale stiffness, considering the influence of the porous structure on the macroscopic stiffness of the material. The inversion process of the constituent elastic parameters was established using the trust-region algorithm combined with an improved error function. The inversion results showed that this method could accurately invert the CMC constituent elastic parameters with excellent robustness and anti-noise performance. Under four different degrees of deviation in the initial iteration conditions, the inversion error of all parameters was within 1%, and the maximum inversion error was only 2.16% under a 10% high noise level.

Leveraging Deep Learning and Generative AI for Predicting Rheological Properties and Material Compositions of 3D Printed Polyacrylamide Hydrogels.

Artificial intelligence (AI) has the ability to predict rheological properties and constituent composition of 3D-printed materials with appropriately trained models. However, these models are not currently available for use. In this work, we trained deep learning (DL) models to (1) predict the rheological properties, such as the storage (G') and loss (G") moduli, of 3D-printed polyacrylamide (PAA) substrates, and (2) predict the composition of materials and associated 3D printing parameters for a desired pair of G' and G". We employed a multilayer perceptron (MLP) and successfully predicted G' and G" from seven gel constituent parameters in a multivariate regression process. We used a grid-search algorithm along with 10-fold cross validation to tune the hyperparameters of the MLP, and found the R2 value to be 0.89. Next, we adopted two generative DL models named variational autoencoder (VAE) and conditional variational autoencoder (CVAE) to learn data patterns and generate constituent compositions. With these generative models, we produced synthetic data with the same statistical distribution as the real data of actual hydrogel fabrication, which was then validated using Student's t-test and an autoencoder (AE) anomaly detector. We found that none of the seven generated gel constituents were significantly different from the real data. Our trained DL models were successful in mapping the input-output relationship for the 3D-printed hydrogel substrates, which can predict multiple variables from a handful of input variables and vice versa.

An integrated multi-attributive border approximation area comparison (MABAC) method for evaluating resilience and knowledge sharing of suppliers in pythagorean fuzzy environment

This paper aims to determine the resilience scores of suppliers by considering the importance of knowledge sharing and incorporating a set of criteria that affect the supply chain’s resilience, along with knowledge sharing. In order to achieve this goal, we propose a new multi-criteria decision-making (MCDM) method named MULTIMABAC to evaluate suppliers’ resilience. This method is based on the multi-attributive border approximation area comparison (MABAC) method. The new method consists of criteria weighting, expert weighting, and supplier evaluation. In the criteria weighting section, we extend the BWM (Best–Worst method) using Pythagorean fuzzy sets (PFSs). The weight of experts is determined by incorporating the average concept into the MABAC method. The resilience score of suppliers is calculated using an extended MABAC method with ideal positive and negative solutions. All indices are aggregated using the ordinal priority approach (OPA) to obtain a unique ranking. Additionally, we extend the MULTIMABAC method using PFSs to address uncertainty in supply chain decision-making. Results and sensitivity analysis demonstrate that the proposed method effectively facilitates knowledge sharing in selecting resilient suppliers. Therefore, organizations can use the proposed method to evaluate and choose resilient suppliers. Moreover, the MULTIMABAC method demonstrates robustness to variations in the weights of its constituent parameters.

Identifying Bayesian optimal experiments for uncertain biochemical pathway models

Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.

Low-Frequency Flexural Wave Acoustic Insulation Characteristics of a Double-Layer Metamaterial Plate

The effects of geometric parameters and constituent material parameters on the flexural wave bandgap of a locally resonant double-layer metamaterial plate are investigated using the finite element method. Reasonable parameters are obtained through optimization; then, a double-layer plate made of ABS material with added aluminum mass blocks is designed. The results show that it can open the low-frequency (112--124 Hz) flexural wave bandgap at a subwavelength size. The metamaterial plate is modeled as an equivalent spring-mass system, and the frequency range with negative density for the equivalent dynamic mass is consistent with the width of the flexural wave bandgap. The bandgap generation mechanism is further elucidated. Sound insulation performance of double-layer metamaterial panels verified in an acoustic chamber. The maximum amplitude of STL is achieved at the 112 Hz point in the bandgap frequency range, which indicates that the low-frequency bandgap can effectively reduce vibration and noise. These results demonstrate that this novel double-layer metamaterial plate has wide applications in engineering.

Superhydrophobic-based corrosion mitigation systems and their effectiveness on dissimilar and similar friction stir spot-weld joint aerospace alloys

Traditional rivets have been a longstanding choice for securing aircraft components as permanent fasteners. A new technique called friction stir spot welding (FSSW) is gaining popularity as a rivet replacement. Despite its advantages, dissimilar FSSW joints are susceptible to corrosion, which can lead to property degradation. This study explored the potential of superhydrophobic (SH) coating as a corrosion inhibitor for FSSW joint aerospace alloys, particularly aluminum alloy (AA). The study involved a comparison of the corrosion resistance between SH-coated dissimilar (AA2024-T3 and AA7075-T6) and similar (AA2024-T3 and AA2024-T3) FSSW joint specimens. Notably, SH-coated dissimilar FSSW joint specimens demonstrated an exceptional average water contact angle (WCA) of 156°, confirming their SH nature. The study further incorporated the design of experiments (DOE) and statistical analysis to optimize constituent parameters and enhance the performance of SH coating. Durability testing indicated that SH-coated dissimilar FSSW joint specimens exhibited high resistance to acetone without influencing the WCA of 148°. Only a slight decrease in the WCA was observed when specimens were submerged in an acidic solution, where the WCA reduced to 142° from the original WCA of 158°. Also, specimens were stable and maintained the WCA of 154° when heat-treated at the temperature of 230 ℃. Moreover, a cross-cut adhesion test revealed that less than 5 % of the coating material was lost in SH-coated dissimilar FSSW joint specimens. Additionally, the electrochemical study evaluated the anti-corrosion performance of SH-coated dissimilar FSSW joint specimens, where the specimens were immersed for 6 days in 3.5 wt% NaCl solution. After 6 days in 3.5 wt% NaCl solution, specimens remained very close to SH, maintaining the WCA above 140°. Also, the electrochemical results demonstrated that the anti-corrosion performance of the SH-coated dissimilar FSSW joint specimens was enhanced with a lower corrosion rate of 37.56 mpy and current density of 0.428 µA/cm² as compared to the uncoated specimens. The variations in corrosion rates and current densities of the SH-coated dissimilar and similar FSSW joint specimens were very minimal, which proves that the application of SH coating on the FSSW joint specimens is suitable to alleviate corrosion-related problems in FSSW joints.

A numerical model simulating cyclic behavior of high-strength steel

High-strength steel (HSS) can effectively and economically design structural members that withstand large forces induced by extreme events such as earthquakes. To evaluate the seismic performance of HSS members and structures using nonlinear finite element (FE) analyses, using an accurate material model is important, which can simulate the inelastic cyclic behavior of the HSS. The peculiar material behavior of HSS needs to be considered in the model. This study used a combined hardening model to construct the material model. The configuration of the model and the constituent model parameter values are determined to precisely simulate the low-cycle fatigue (LCF) behavior of HSS. An efficient particle swarm optimization (PSO) algorithm is used to determine parameter values with LCF test data of 54 individual HSS coupons. In additions, to conveniently determine the model parameter values with only monotonic tensile test data instead of conducting expensive LCT tests, empirical equations are proposed. It is shown that the LCF curves of HSS can be accurately simulated using the constructed material model with the proposed equations.

Optimising the manufacturing of a β-Ti alloy produced via direct energy deposition using small dataset machine learning

Successful additive manufacturing involves the optimisation of numerous process parameters that significantly influence product quality and manufacturing success. One commonly used criteria based on a collection of parameters is the global energy distribution (GED). This parameter encapsulates the energy input onto the surface of a build, and is a function of the laser power, laser scanning speed and laser spot size. This study uses machine learning to develop a model for predicting manufacturing layer height and grain size based on GED constituent process parameters. For both layer height and grain size, an artificial neural network (ANN) reduced error over the data set compared with multi linear regression. Layer height predictions using ANN achieved an R2 of 0.97 and a root mean square error (RMSE) of 0.03 mm, while grain size predictions resulted in an R2 of 0.85 and an RMSE of 9.68 μm. Grain refinement was observed when reducing laser power and increasing laser scanning speed. This observation was successfully replicated in another α + β Ti alloy. The findings and developed models show why reproducibility is difficult when solely considering GED, as each of the constituent parameters influence these individual responses to varying magnitudes.

A COMPREHENSIVE MODEL FOR EVALUATING THE DEVELOPMENT OF FREIGHTING AND ADJUNCTION FLEET SHIPPING COMPANIES: INTERNATIONAL AND NATIONAL ASPECTS

Topicality. Today, the transport system, especially the water transport of Ukraine, is in a state of turbulence in the business environment, therefore the development of the transport market and the dynamics of the military influence of maritime transport are subject to significant fluctuations. In modern conditions, when analyzing the work of shipping companies along with the desire to maximize current financial indicators of operation, issues related to the long-term planning of the fleet of shipping companies, taking into account the trends of changes in the state of the freight market and the dynamics of the military influence on the economy of Ukraine, are also important. Aim and tasks. The purpose of this work is to develop a system model of the efficiency indicators of the shipping company's vessels used in sea tramp transportation, taking into account the changing trends of the freight market in international and national aspects. Tasks, the work, to substantiate the model for evaluating the efficiency of the use of sea vessels, which allows making the optimal choice of a vessel for chartering the fleet of a shipping company, taking into account the dynamics of military influence and changes in freight rates. Materials and methods. This study is based on the analysis of the distribution of the freight market in relation to the formation of cargo flows for the global shipping security system. Methods are applied: graphic method, statistical analysis, economic analysis, system analysis on the example of chartering the fleet of shipping companies. Research results. The authors of the study conducted an analysis of the distribution of the international market of freight rates, taking into account various constituent parameters. Values of freight rates and fuel prices for vessels, equipment and others are subject to random fluctuations. Of practical interest is the development of the model proposed by the authors for the development of projects for the chartering and operation of vessels to possible random fluctuations in the business environment. Therefore, there is a need to review the approaches used to regulate transport and the need to develop a comprehensive model for evaluating the performance indicators of the chartering of sea vessels by shipping companies in terms of strategic goals of transport safety. The proposed sequence of substantiation of critical values of freight rates is universal when evaluating the development of shipping companies through chartering, replenishment of shipowners' vessel. Conclusions. So, for a shipping company, the income is formed on the basis of freight rates, therefore, cash receipts from replenishment of the fleet are based on critical freight rates (flight charter). It should be noted that fleet chartering is most often focused on its own commercial operation (with the possible participation of companies engaged in the commercial management of ships). Thus, the system model of fleet chartering is, on the one hand, one of the options for implementing the shipping company's development strategies, and on the other hand, it is a replacement of old vessels to ensure the maintenance of existing market positions.

Performance analysis of electromagnetic vibration energy harvester under square excitation

Harvesting energy from surrounding vibrations is an effective method for powering various low-power electronic devices. The energy output performance of vibration energy harvesters under some special excitations remains to be investigated. In this paper, a vibration energy harvester (VEH) system based on a rack and pinion mechanism and a bevel gear set is proposed, and a detailed electromechanical coupling modeling of the system under general excitation and square wave excitation is carried out. The energy output characteristics of the proposed system under square wave excitation are explored through simulation and experiment. When the energy output is the target, the priority of adjusting the constituent parameters in the equivalent mass parameter, equivalent damping parameter and equivalent stiffness parameter of the system can be obtained based on the simulation results. The experimental results under periodic square wave excitation force show that the maximum output power of the proposed system is 2.7 W at an amplitude of 4 kN and a frequency of 3 Hz. This is a 55 % increase in the maximum power compared to the sinusoidal excitation under the same conditions. It is demonstrated that the proposed system has great potential to power various low-power electronic devices.

Strong Intervalley Scattering-Induced Renormalization of Electronic and Thermal Transport Properties and Selection Rule Analysis in 2D Tellurium.

The electron-phonon interaction (EPI) and phonon-phonon interactions are ubiquitous in promising two-dimensional (2D) semiconductors, determining both electronic and thermal transport properties. In this work, based on ab initio calculations, the effects of intervalley scattering on EPI and higher-order four-phonon interactions of α-Te and β-Te are investigated. Through the proposed selection rules for scattering channels and calculations of full electron-phonon scattering rates, we demonstrate that multiple nearly degenerate local valleys/peaks produce more scattering channels, resulting in stronger intervalley scattering over intravalley scattering. The lattice thermal conductivities of α-Te and β-Te are decreased by as much as 10.9% and 30.8% by considering EPI under the carrier concentration of 2 × 1013 cm-2 (n-type) at 300 K compared to those limited by three-phonon scattering, respectively. However, when further considering four-phonon scattering, EPI reduces the lattice thermal conductivities by 2.6% and 19.4% for α-Te and β-Te, respectively. Furthermore, it is revealed that the four-phonon interaction is more dominant in phonon transport for α-Te than that for β-Te due to the presence of an acoustic-optical phonon gap in α-Te. Finally, we demonstrate strong intervalley scattering induces significant renormalization effects from EPI on all the constituent parameters of thermoelectric performance. Our results show the contributions of intervalley scattering to the electronic properties as well as thermal transport properties in band-convergent thermoelectric materials are essential and highlight the potential of monolayer tellurium as a promising candidate for advanced thermoelectric applications.

Development and Evaluation of the Plankton Biological Integrity Index (P-IBI) in Dry and Wet Seasons for Dianchi Lake

As an important component of lake ecosystems, plankton are often used as indicators to evaluate the health of aquatic ecosystems, such as lakes and reservoirs. The plankton integrity index (P-IBI) is a highly utilized method for evaluating the ecological health of the lakes. This study took Dianchi Lake, located in the Yangtze River Basin, as the research object and analyzed the phytoplankton, zooplankton communities, and environmental factors at 11 sampling points in this lake during the wet season (July) in 2022 and the dry season (February) in 2023. The P-IBI was established to evaluate the health status of this lake ecosystem. The results showed that a total of 83 species of phytoplankton and 31 species of zooplankton were identified in Dianchi Lake, and the number of plankton species in the dry season was significantly higher than that in the wet season. The P-IBI evaluation results for the two hydrological periods were generally “good”. Linear regression analysis showed that there was a certain negative correlation between the P-IBI value and the comprehensive trophic level index (TLI), and the evaluation results were generally in line with the actual situation of the water body. Redundancy analysis (RDA) showed that there was a significant correlation between the P-IBI and its constituent parameters and individual water quality environmental factors, such as total nitrogen (TN) and electrical conductivity (EC). In summary, by reducing errors caused by spatial and temporal changes across various hydrological periods, P-IBI represents a more scientifically rigorous technique for lake water ecological health assessments within a certain time range.

A novel wall parameter estimation approach based on transmission line method from through-wall radar measurements

A novel technique for estimation of the wall’s relative permittivity, thickness and conductivity from signals measured from the through-wall radar is presented in this paper. The retrieval process is built around an electromagnetic circuit model that represents the wall structure as a transmission line segment. The propagation of the radar signal through the wall is equivalently described as a pair of voltage and current waves that propagate along the transmission line, using analogies between the electrical parameters of the transmission line segment and the constituent parameters of the materials of the wall structure. The synthetic time domain signal obtained analytically and the real through wall imaging time domain signal were correlated to estimate the wall parameters of the layers that comprise the wall structure. The effectiveness of the proposed estimation technique has been demonstrated through simulation and experimentation on two actual building walls. A comparison with conventional methods revealed consistent results between the estimated wall parameters using the proposed technique and those obtained through traditional approaches. These findings highlight the potential of the proposed method in enhancing the accuracy of real-time target localization and tracking in through-wall imaging applications.

Geometry of adipocyte packing in subcutaneous tissue contributes to nonlinear tissue properties captured through a Gaussian process surrogate model.

Subcutaneous tissue mechanical response is governed by the geometry and mechanical properties at the microscale and drives physiological and clinical processes such as drug delivery. Even though adipocyte packing is known to change with age, disease, and from one individual to another, the link between the geometry of the packing and the overall mechanical response of adipose tissue remains poorly understood. Here we create 1200 periodic representative volume elements (RVEs) that sample the possible space of Laguerre packings describing adipose tissue. RVE mechanics are modeled under tri-axial loading. Equilibrium configuration of RVEs is solved by minimizing an energetic potential that includes volume change contributions from adipocyte expansion, and area change contributions from collagen foam stretching. The resulting mechanical response across all RVE samples is interpolated with the aid of a Gaussian process (GP), revealing how the microscale geometry dictates the overall RVE mechanics. For example, increase in adipocyte size and increase in sphericity lead to adipose tissue softening. We showcase the use of the homogenized model in finite element simulations of drug injection by implementing a Blatz-Ko model, informed by the GP, as a custom material in the popular open-source package FEBio. These simulations show how microscale geometry can lead to vastly different injection dynamics even if the constituent parameters are held constant, highlighting the importance of characterizing individual's adipose tissue structure in the development of personalized therapies.

A Methodology to Design Broadband Negative Inductors with Tight Tolerance for Microwave Applications

Introduction. Non-Foster elements (NFEs) mimic behavior of hypothetical negative inductors or capacitors in a certain frequency band. NFEs are used to compensate reactance of conventional inductors and capacitors that allows designing broadband microwave devices. To realize NFEs, active circuits referred to as negative impedance converters (NICs) are employed to convert the load impedance into the negative input impedance. The conversion error, caused by non-optimal choice of NIC parameters and non-idealities of NIC components, limits the accuracy and operating bandwidth of NFEs. The necessity to account for many factors, which indirectly and oppositely impact the final result, and unavailability of a universal design methodology complicate the design of NFEs significantly. As a result, broadband NFE characteristics differ from the target ones remarkably that limits practical applications.Aim. Elaboration of a design methodology to compensate the Linvill’s NIC conversion error and realize high-accuracy broadband negative inductors.Materials and methods. Influence of NIC constituent parameters on the negative inductor frequency characteristics is considered. The performed analysis and the identified relationships allowed us to propose a step-by-step methodology to design negative inductors having tight tolerance over a broad frequency band. The use of a transmission line section instead of a lumped inductor in the NIC load when realizing negative inductors of high absolute values is shown to be advantageous as this allows providing better tolerance and wider bandwidth.Results. In order to demonstrate possibilities enabled by the proposed methodology, simulation results are presented for the GHz-range negative inductors with a set of inductance and tolerance values.Conclusion. The results obtained show that the proposed methodology makes it possible to compensate the conversion error without any numerical optimization and therefore to reduce the deviation of the negative inductance from the target value in the given frequency range or to broaden the bandwidth for a given tolerable deviation of the negative inductance.

Determination of average growth rate based on statistical relationships using geomorphological and geotechnical variables in predictive debris flow simulations

Growth rate is a crucial constituent parameter for determining basal entrainment rates in debris flow numerical modeling frameworks. This study presents the development of a multiple-regression model to estimate site-specific average growth rates in advance of debris flow event occurrences. A range of geomorphological and geotechnical variables influencing entrainment was selected through an in-depth literature review. A comprehensive dataset from 35 debris flow events in the Gyeonggi province of South Korea was employed to identify statistically significant variables related to the average growth rate. Through correlation analysis, three variables were ultimately applied as predictors for the multiple regression analysis: channel path length, basin area with slope inclinations of 30 % or greater, and median of soil grain-size distribution. The proposed model was statistically validated by demonstrating high coefficients of determination and meeting key assumptions of no-multicollinearity, no-autocorrelation, normality, and homoscedasticity. In a case study of a historical debris flow event that occurred during the 2011 Mt. Woomyeon landslide disaster, the practical relevance of the developed model was tested by comparing two parallel simulation results using the DAN3D numerical model. Different average growth rates were applied to the two parallel simulations, respectively; the target average growth rate calculated using field-measurement data and the predicted average growth rate derived by applying the multiple regression model. The results revealed that the proposed model provided reasonable predictions of debris flow velocities and heights, with only minor discrepancies between the target and predicted average growth rates. Notably, measured average growth rates of immediately adjacent debris flow events significantly differed from that of the case study event. This highlights that adopting entrainment data from neighboring events for debris flow simulations can result in significantly erroneous debris flow hazard predictions. Finally, the limitations and contributions of this study and future research directions are discussed.

Pan-Immune-Inflammation Index as a Biomarker for Rheumatoid Arthritis Progression and Diagnosis.

Introduction Rheumatoid arthritis (RA) is a chronic autoimmune condition that causes systemic inflammation and affects multiple joints. It is characterized by joint warmth, swelling, pain, and the formation of invasive synovial tissue known as pannus, which contributes to cartilage and bone degradation.Pan-immune-inflammation value (PIV), a marker derived from complete blood count parameters, has shown promise in predicting prognosis in various cancer types and pediatric conditions associated with immune abnormalities. This study aims to explore the relationship between RA, characterized by chronic inflammation and immune system involvement, and PIV, potentially shedding light on novel insights into RA's clinical implications. Methods One hundred four participants, including 64 RA patients (both newly diagnosed and established cases) and 40 healthy controls, were included in the study. Exclusion criteria for RA patients included acute infection, cancer, diabetes, or chronic illness, while control participants were excluded for inflammatory disorders, active infection, diabetes, or malignancy. We assessed disease severity using Disease Activity Score 28 (DAS 28) and obtained complete blood count values, including neutrophil, lymphocyte, platelet, monocyte, and red cell distribution width. C-reactive peptide (CRP) and erythrocyte sedimentation rate were also added.Statistical analyses included correlation assessments, t-tests, Mann-Whitney U tests, and multivariate linear regression. A multiclass receiver operating characteristic analysis determined optimal PIV cut-off values for distinguishing control, remission, and active RA groups, with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and odds ratios calculated. Results This study comprised a cohort of 104 participants, with a median age of 43.5±17.5. The Remission group was significantly younger than the Control group (p=0.006)but not compared to the Active RA group (p=0.393). CRPlevels were significantly higher in the Active RA group (p<0.001). Neutrophil counts were highest in the Active RA group (p<0.001), as were monocyte counts. Lymphocyte counts were significantly lower in the Active RA group (p<0.001). There were no significant differences in sedimentation rate, hemoglobin, platelet count, and mean platelet volume. PIVwas significantly elevated in the Active RA group (p<0.001) and higher in the Remission group than in the Control group (p=0.001).A PIV value of 353.48 exhibited 71.4% sensitivity, 86.2% specificity, 86.2% PPV, 71.4% NPV, and 78.13% test accuracy for distinguishing active rheumatoid arthritis (p<0.001).A PIV value exceeding 353.48 substantially increased the likelihood of a patient belonging to the active rheumatoid arthritis group, with a 14.62-fold higher probability.Furthermore, the study explored the relationship between clinical and laboratory variables and disease activity in RA patients, finding significant differences inPIV among DAS groups (p=0.025). Conclusions The PIV offers a notable advantage as its constituent parameters are routinely assessed in rheumatoid arthritisand involve cost-effective and straightforward tests. We demonstrated that PIV serves as a valuable marker for distinguishing between remission and active RA when compared to healthy individuals. Additionally, it proved to be an effective tool for assessing disease activity in patients with active rheumatoid arthritis.

Application of periodic structural wave impeding block for vibration isolation in foundation

The wave impeding board (WIB) is frequently integrated beneath dynamic machinery, tracks, and subgrades to counteract vibrations emanating from artificial sources. However, conventional WIBs have exhibited a limited isolation frequency band due to their dependence on the soil cut-off frequency of soil. Furthermore, the vibration sources typically encompass intricate frequency components spanning low, medium, and high frequencies. To overcome the technical limitations of WIBs relying on the cut-off frequency of soil, a new periodic structural wave impeding board (PSWIB) is proposed based on the principles of phononic crystals. Theoretical and numerical analyses demonstrate that PSWIB exhibits bandgap characteristics, with the attenuation range achieved by finite periodic structures aligning with the bandgap of an infinite PSWIB. Maximum amplitude reductions of 47 dB and 65 dB are achieved within the vibration attenuation range. Compared to traditional WIB, PSWIB surpasses the constraints imposed by the cut-off frequency of soil and allow for the design of constituent parameters based on the characteristics of the vibration source, enabling effective isolation of the target frequency vibrations.

Hydromagnetic flow of micropolar nanofluids with co-effects of thermal radiation and chemical reaction over an inclined permeable stretching surface

BackgroundMany investigations have been conducted by researchers across the globe to examine the behavior of fluids with respect to the influence of some constituent parameters and novel results have been obtained. However, the combined effect of thermal radiation and chemical reaction on micropolar nanofluid flow over an inclined stretching surface has not been well elucidated. This article, therefore, employed the mathematical model of Buongiorno for hydromagnetic micropolar nanofluids to study the effect of thermal radiation and chemical reaction on such fluids. The model examined the influence of thermophoresis, Brownian motion and the angle of inclination to the stretching surface on the fluid flow. The set of governing equations were transformed into ordinary differential equations using some similarity transformations and then numerically simplified through Chebyshev collocation method on MATHEMATICA software.ResultsThe graphs thus derived were used to interpret the effect of some physical parameters on the fluid flow. It was observed among other results obtained, that thermal radiation, Brownian motion and thermophoresis enhanced the temperature profile of the flow while the inclination angle and chemical reaction declined the velocity and concentration, respectively.ConclusionsThese parameters tested on the various profiles proved observably effective on micropolar nanofluids and should be considered whenever improvement or decrease in the profiles are needed.

Monte Carlo samplers for efficient network inference.

Accessing information on an underlying network driving a biological process often involves interrupting the process and collecting snapshot data. When snapshot data are stochastic, the data's structure necessitates a probabilistic description to infer underlying reaction networks. As an example, we may imagine wanting to learn gene state networks from the type of data collected in single molecule RNA fluorescence in situ hybridization (RNA-FISH). In the networks we consider, nodes represent network states, and edges represent biochemical reaction rates linking states. Simultaneously estimating the number of nodes and constituent parameters from snapshot data remains a challenging task in part on account of data uncertainty and timescale separations between kinetic parameters mediating the network. While parametric Bayesian methods learn parameters given a network structure (with known node numbers) with rigorously propagated measurement uncertainty, learning the number of nodes and parameters with potentially large timescale separations remain open questions. Here, we propose a Bayesian nonparametric framework and describe a hybrid Bayesian Markov Chain Monte Carlo (MCMC) sampler directly addressing these challenges. In particular, in our hybrid method, Hamiltonian Monte Carlo (HMC) leverages local posterior geometries in inference to explore the parameter space; Adaptive Metropolis Hastings (AMH) learns correlations between plausible parameter sets to efficiently propose probable models; and Parallel Tempering takes into account multiple models simultaneously with tempered information content to augment sampling efficiency. We apply our method to synthetic data mimicking single molecule RNA-FISH, a popular snapshot method in probing transcriptional networks to illustrate the identified challenges inherent to learning dynamical models from these snapshots and how our method addresses them.