Parametric Methods Research Articles

Convergence of a stabilized parametric finite element method of the Barrett–Garcke–Nürnberg type for curve shortening flow

The parametric finite element methods of the Barrett–Garcke–Nürnberg (BGN) type have been successful in preventing mesh distortion/ degeneration in approximating the evolution of surfaces under various geometric flows, including mean curvature flow, Willmore flow, Helfrich flow, surface diffusion, and so on. However, the rigorous justification of convergence of the BGN-type methods and the characeterization of the particle trajectories produced by these methods still remain open since this class of methods was proposed in 2007. The main difficulty lies in the stability of the artificial tangential velocity implicitly determined by the BGN methods. In this paper, we give the first proof of convergence of a stabilized BGN method for curve shortening flow, with optimal-order convergence in L 2 L^2 norm for finite elements of degree k ≥ 2 k \geq 2 under the stepsize condition τ ≤ c h k + 1 \tau \leq c h^{k+1} (for any fixed constant c c ). Moreover, we give the first rigorous characterization of the particle trajectories produced by the BGN-type methods for one-dimensional curves, i.e., we prove that the particle trajectories produced by the stabilized BGN methods converge to the particle trajectories determined by a system of geometric partial differential equations which differs from the standard curve shortening flow by a tangential motion. The characterization of the particle trajectories also rigorously explains, for one-dimensional curves, why the BGN-type methods could maintain the quality of the underlying evolving mesh.

Investigating thermal conduction dynamics in ternary Carreau–Yasuda nanofluids under convective boundary conditions and exponential heat source/sink effects

The analysis of the Carreau–Yasuda nanofluid (CYNF) across a stretching surface has practical applications in several fields, such as heat exchange, engineering, and material science. Scholars may discover novel perspectives for increasing heat transfer performance, establishing advanced materials, and enhancing the efficiency of multiple engineering systems by studying the behavior of CYNF in this particular instance. The energy transfer through trihybrid CYNF flow with the effect of magnetic dipole across a stretching sheet is examined in the present study. The ternary hybrid nanofluid (THNF) has been prepared by the addition of ternary nanoparticles (NPs) in the water (50%) and ethylene glycol (50%). Titanium dioxide (TiO2), silicon dioxide (SiO2), and aluminum oxide (Al2O3) are used in base fluid. The fluid velocity and heat transfer are examined under the impact of Darcy–Forchheimer, chemical reaction, convective condition, activation energy, and exponential heat source. The fluid flow has been stated in form of velocity, energy, and concentration equations. The set of modeled equations is simplified to non-dimensional form of ordinary differential equations (ODEs) by using similarity substitutions. Numerically, the system of lowest order ODEs is calculated through the parametric continuation method. It has been noticed that the temperature field augments against the variation of viscous dissipation and heat source term. Furthermore, the magnetic dipole has significant impact to enhance the thermal curve of THNF whereas falloffs the velocity profile. It can be noticed that the energy propagation rate enhances with the rising numbers of ternary NPs, from 1.22% to 5.98% (nanofluid), 2.30% to 9.61% (hybrid nanofluid), and 3.23% to 11.12% (trihybrid nanofluid).

Group sequential testing of a treatment effect using a surrogate marker.

The identification of surrogate markers is motivated by their potential to make decisions sooner about a treatment effect. However, few methods have been developed to actually use a surrogate marker to test for a treatment effect in a future study. Most existing methods consider combining surrogate marker and primary outcome information to test for a treatment effect, rely on fully parametric methods where strict parametric assumptions are made about the relationship between the surrogate and the outcome, and/or assume the surrogate marker is measured at only a single time point. Recent work has proposed a nonparametric test for a treatment effect using only surrogate marker information measured at a single time point by borrowing information learned from a prior study where both the surrogate and primary outcome were measured. In this paper, we utilize this nonparametric test and propose group sequential procedures that allow for early stopping of treatment effect testing in a setting where the surrogate marker is measured repeatedly over time. We derive the properties of the correlated surrogate-based nonparametric test statistics at multiple time points and compute stopping boundaries that allow for early stopping for a significant treatment effect, or for futility. We examine the performance of our proposed test using a simulation study and illustrate the method using data from two distinct AIDS clinical trials.

A-079 Investigation of gut microbial flavonoid catabolites in metabolic diseases

Abstract Background Flavonoids, a secondary metabolite found in plant-based diets, have gained attention in the last decade for their antioxidative properties in high-fat diet induced obesity animal models and human studies. Previous work showed that certain gut microbiota members can metabolize flavonoids into monophenolic acids (MPAs), and some of these MPAs have similar effects to insulin when administered orally. Our objective is to explore the microbiome connection between flavonoid-rich diets and improved metabolic parameters in obesity. We hypothesize that flavonoid-supplemented diets are most effective in the presence of gut microbiota capable of MPA formation and that these bacterial metabolites are the main bioactive compounds responsible for the improved metabolic parameters Methods To investigate our hypothesis, we fed male and female C57BL/6 mice a high-fat control diet (HFD) or the same diet supplemented with 1% MPA or 1% berry extract for 12 weeks. Each group was further divided into subgroups that received regular drinking water (RW) or antibiotic water (ABW) to suppress gut microbial communities. A range of metabolic parameters, such as body weight, lean and fat mass, glucose and insulin tolerance, histological assessment of liver cells, quantification of liver injury biomarkers, transcriptional changes in metabolically active tissues, and glucose and lipid metabolism pathways, were monitored. Additionally, 16s sequencing was performed on the cecal microbial composition of each group. Results Mice on HFD control with RW or ABW showed prominent hepatic steatosis and an increase in liver lipids accumulation demonstrated by a significant decrease in the activation of lipid metabolism pathways. Similarly, mice on HFD with 1% MPA and RW or ABW exhibited a significant reduction in hepatic steatosis and an increase in the activation of the lipid metabolism pathways, regardless of gut microbiota presence. In line with our hypothesis, mice on HFD with 1% berry extract and RW showed a similar reduction in hepatic steatosis to both 1% MPA groups. However, mice on HFD with 1% berry extract and ABW displayed noticeable hepatic steatosis, indicating the loss of the positive effect provided by berry extract metabolites once gut microbiota was depleted by antibiotics. Insulin tolerance test revealed a gut microbial effect as well in the HFD with 1% berry extract, illustrated by a significant reduction in glucose levels after insulin injection and an increase in insulin sensitivity. In contrast, the HFD control mice experienced an increase in glucose levels after insulin injection and insulin resistance. Although there were notable decreases in fasting blood glucose, there were no significant effects on fat and lean mass, insulin, glucagon, or liver injury marker levels. Finally, the mice on HFD with 1% berry extract had significantly more diverse cecal microbial communities than those fed HFD or HFD with 1% MPA. Conclusions Feeding mice a flavonoid-rich diet in the presence of intact gut microbiome can improve metabolic parameters associated with metabolic diseases, potentially preventing, or reversing these diseases. Our findings suggest promising avenues for treatment, such as dietary flavonoid supplementation with concurrent gut microbial probiotic therapy.

Optical pump magnetometers parametric correction method based on three-axis coil arrays

Optical pump magnetometers (OPMs), based on the SERF principle, are increasingly prevalent in biomagnetic field detection and localization studies. However, cross-axis crosstalk is an unavoidable consequence of the non-ideal factors introduced during the development of the sensor array, whether during the initial design or manufacturing process. This study introduce a novel method to compensate for cross-axis crosstalk by a set of three-axis coils.The gain matrix of each sensor in an OPM array is calibrated by the magnetic field generated by the activation of the three-axis coils. The results showed that the correlation coefficients between the corrected coil distribution field measurements and the true values improved substantially, exceeding 0.99. Similarly, the similarity between the forward-field simulation and the measured results for the localization of a physical model of a current dipole improved significantly, from 0.78 to 0.96. This work presents the first report of the simultaneous calibration of a large OPM array comprising 64 OPMs. The suppression of cross-axis crosstalk errors improves the sensor measurement accuracy and the consistency of the array. This method is expected to further improve the accuracy of OPM array-based source localization.

Using Dynamic Stereo X-ray Imaging for In Vivo Acromioclavicular Joint Kinematics Assessment: A Preliminary Investigation.

Acromioclavicular joint (ACJ) disruption occurs frequently in athletes engaged in contact sports. However, the current understanding of ACJ biomechanics during muscle-driven functional activities and the influence of different treatment approaches (eg, reconstruction surgery vs nonoperative methods) on ACJ kinematics and stability remains limited. The absence of precise in vivo biomechanical measurement modalities for scapular and clavicular kinematics contributes significantly to this lack of understanding. The purposes of this study were to determine whether dynamic stereo x-ray (DSX) imaging can be used to evaluate the in vivo kinematics of the ACJ and to provide preliminary comparative data on ACJ kinematics, range of motion, and isometric strength of surgically reconstructed or nonoperatively treated ACJ shoulders and their uninjured contralateral shoulders. It was hypothesized that ACJ kinematics could be measured successfully using DSX and that surgically and nonoperatively treated shoulders would show abnormal 3-dimensional (3-D) ACJ kinematics compared with the uninjured contralateral. Controlled laboratory study. In this cross-sectional study, 11 participants who had undergone unilateral ACJ reconstruction surgery and 3 patients who received nonoperative treatment were enrolled. ACJ kinematics were assessed during active forward flexion, scaption, and abduction through high-speed DSX imaging, complemented by 3-D bone models obtained via computed tomography (CT) scans. To gauge kinematic differences, a 1-dimensional statistical parametric mapping method was employed, which compared outcomes in the index limb to those in the uninjured counterpart. In addition, the range of motion and isometric strength at various abduction angles were analyzed, employing a repeated-measures analysis of variance to compare the affected and uninjured sides. Leveraging a combination of DSX imaging and patient-specific CT bone models, ACJ kinematics was measured successfully during movements along anatomic planes. Preliminary findings from this investigation revealed no detectable differences between the surgically reconstructed and uninjured sides in ACJ biomechanics, shoulder range of motion, and isometric strength outcomes. However, on average, the nonoperatively treated shoulders demonstrated increased internal rotation, upward rotation, and posterior tilting of the scapula relative to the clavicle (no statistical analyses were performed due to the small sample size). DSX imaging is a promising tool for evaluating potential in vivo kinematic abnormalities in the ACJ during muscle-driven activities, laying the groundwork for further investigations in both ACJ-reconstructed and nonreconstructed patients. This study furnished essential data for conducting power analyses and designing future studies with an adequate sample size to investigate the impact of different treatment approaches on shoulder girdle mechanics. With its potential for accurately characterizing shoulder girdle kinematics post-ACJ injury, DSX imaging can offer valuable insights for future clinical studies, facilitating informed decisions regarding the short- and long-term impacts of treatment choices on shoulder health and function.

Phased optimization of multi-thermal fluid flooding for enhanced oil recovery

Multi-thermal fluid flooding has become a critical technique in heavy oil reservoirs and is widely applied in China. Owing to the complexity of the displacement process and the involvement of multiple injection media, conventional optimization methods are unable to achieve economic and efficient development of heavy oil. In this study, an efficient phased parametric optimization method for multi-thermal fluid flooding was developed using horizontal wells in heavy oil reservoirs. This method utilizes particle swarm optimization (PSO), which targets the net present value for the phased optimization of multi-thermal fluid flooding. A comparison between the phased optimization and non-phased optimization shows that the cumulative oil production increased by 16.74% and the net present value increased by 39.66% with phased optimization. The effectiveness of the phased optimization method was verified using a field model. Optimal injection parameter charts were developed for each phase of the multi-thermal fluid flooding under different reservoir parameter conditions. The charts illustrate that the optimal gas injection rate and chemical mass concentration tend towards zero during the steam flooding initiation phase under varying reservoir parameter conditions. Conversely, all the media injection rates increased during the thermal fluid control and steam breakthrough phases. Additionally, with an increase in the crude oil viscosity, effective formation thickness, and original oil saturation, the optimal injection rate of the various media increases. However, with an increase in the permeability and reservoir depth, the optimal injection rate of the various media decreases.

Bispectrum Analysis of Thermal Images for the Classification of Retinal Vascular Diseases

In the field of medicine, thermal imaging provides a non-invasive means to diagnose diseases by displaying temperature variations. Specifically, thermal images of the ocular region are proving effective in diagnosing retinal diseases like diabetic retinopathy (DR), diabetic macular edema (DME), age-related macular degeneration (AMD), and wet AMD. This study explores the use of bispectrum analysis on ocular thermal images. The bispectrum, derived from third-order statistics (cumulants), is estimated using parametric (ARMA) and non-parametric methods (direct and indirect). Various entropy measures are extracted as features from these bispectrum images. The effectiveness of these features is evaluated through experiments employing different machine learning algorithms: discriminant analysis (DA), K-nearest neighbour (KNN), decision tree (DT), and Naïve Bayes Classifier (NBC). The results indicate that the KNN classifier achieves the highest accuracy when using features extracted via the indirect method of bispectrum estimation. Meanwhile, the DT classifier performs exceptionally well with bispectrum features extracted through the direct method, outperforming other feature extraction approaches.

Gyrotactic micro-organism dusty fluid flow over an extended surface

The conventional magnetohydrodynamics (MHD) equations are modeled for the dusty fluid flow past a stretching sheet embedded in a porous medium. These equations are coupled with motile microorganisms, temperature and concentration equations for both fluid and dust particles dynamical equations. The two-phase dusty fluid flow is subjected to magnetic effect, porosity factor, 2nd order chemical reaction, Brownian diffusion, thermophoretic effect and Arrhenius activation energy. An appropriate transformation is used to reset the system of partial differential equations (PDEs) into non-linear system of ODEs (ordinary differential equations). The flow equations are numerically solved by using the parametric continuation method (PCM). For validity of the applied methodology, the results are compared using numerical and graphical results of Matlab built-in package “BVP4c”. Both results show accuracy up to four decimal places. Furthermore, from graphical results, it can be noticed that the fluid particles interaction parameter causes an attractive force among fluid particles which prevents the dust particles phase flow. This technique can be used for biological separations, filters, fluid dust separators and crude oil purification.

Derivation of Russian-specific reference intervals for complete blood count, iron markers and related vitamins.

This study aimed to establish reference intervals (RIs) for Russian adults for hematological parameters including related iron markers and vitamins. Sources of variation of reference values (RVs) and needs for secondary exclusion were explored for proper derivations of RIs. Following the harmonized protocol of the IFCC Committee on Reference Intervals and Decision Limits (C-RIDL), 506 healthy Russians (age 18-80; 46% male) were recruited. Complete blood counts (CBC) and leukocyte differentials, iron markers, vitamin B12, and folate were measured by Beckman Coulter's analyzers. Sources of variation were analyzed by multiple regression analysis, and ANOVAs, and the need for partitioning RVs was decided accordingly. Two schemes of excluding latent anemia were compared: (1) latent abnormal values exclusion method (LAVE) based on associations among CBC parameters, or (2) explicit exclusion of individuals with either ferritin or iron below the respective lower limit of the manufacturer. RIs were determined by the parametric method using two-parameter Box-Cox formula. Gender-specific RIs were required for most analytes, while age-specific RIs were set only for ferritin in females. A BMI-related increase in RVs was prominently observed for reticulocyte parameters, hence we chose to exclude individuals with BMI>28 kg/m2 when establishing the RIs. The LAVE method was more effective in excluding individuals with latent anemia, than exclusion based on low ferritin and/or iron values. International comparison revealed that Russian RIs featured a lower side shift of platelet counts. Similar to African countries, Russian RIs for total leukocyte and neutrophil counts were lower compared to most of other countries. RIs for the Russian population for 34 hematological and related parameters were established using up-to-date methods proposed by C-RIDL. Reducing the influences of latent anemia and obesity on RIs was crucial for erythrocyte parameters. Low levels of Russian RIs observed for platelet and neutrophil counts need further investigation.

Design, synthesis, and evaluation of some metal ion complexes of mannich base derived from 2-Mercaptobenzimidazole as potential antimicrobial agents

This study aims to prepare new compounds and investigate them spectroscopically and biologically against selected types of positive and negative bacteria and fungi to demonstrate their biological effectiveness. The prepared ligand combining formaldehyde, indole, sulfa benzamide, and 2-mercapto benzimidazole, a Mannich base ligand (L) was synthesized. The six metal ions including Cobalt (II), Nickel (II), Copper (II), Palladium (II), Platinum (IV), and gold (III) have interacted with the ligand and formed new complexes. Different spectroscopic methods, including C.H.N.S., FTIR, UV- Range visible, 1HNMR, 13CNMR, mass spectra, magnetic moment, and molar conductivity were used to suggest the new geometry of the complexes. The result from the infrared spectrum showed that the ligand behaves as tridentate with all prepared complexes. Conductivity analysis revealed the electrolytic nature of palladium, platinum, and gold ions complexes and non-electrolytes. The antibacterial activity of the compounds that were produced was tested using an agar-well diffusion procedure towards two strains of gram-positive as well as two strains of gram-negative bacteria and fungi (Staphylococcus, Streptococcus, Escherichia coli, Pseudomonas aeruginosa, and Candida albicans) respectively at 0.02M. The standard (∆Eo) and (∆Hfo) of ligand and six complexes were calculated using the program Hyper chem 8.0.7. The research established that complexes are more stable than ligands. Calculated HOMO and LUMO and vibration frequencies using (parametric method 3 (PM3)) to find out the active sites in the ligand showed that they can coordinate and note the extent to which the results of theoretical vibrational frequencies are close to the process when calculating the vibrational frequency of the active aggregates.

Evaluation of the Performance of Kernel Non-parametric Regression and Ordinary Least Squares Regression

Researchers need to understand the differences between parametric and nonparametric regression models and how they work with available information about the relationship between response and explanatory variables and the distribution of random errors. This paper proposes a new nonparametric regression function for the kernel and employs it with the Nadaraya-Watson kernel estimator method and the Gaussian kernel function. The proposed kernel function (AMS) is then compared to the Gaussian kernel and the traditional parametric method, the ordinary least squares method (OLS). The objective of this study is to examine the effectiveness of nonparametric regression and identify the best-performing model when employing the Nadaraya-Watson kernel estimator method with the proposed kernel function (AMS), the Gaussian kernel, and the ordinary least squares (OLS) method. Additionally, it determines which method yields the most accurate results when analyzing nonparametric regression models and provides valuable insights for practitioners looking to apply these techniques in real-world scenarios. However, criteria such as generalized cross-validation (GCV), mean square error (MSE), and coefficient determination are used to select the most efficient estimated model. Simulated data was used to evaluate the performance and efficiency of estimators using different sample sizes. The results favorable the simulation illustrate that the Nadaraya-Watson kernel estimator using the proposed kernel function (AMS) exhibited favorable and superior performance compared to other methods. The coefficients of determination indicate that the highest values attained were 98%, 99%, and 99%. The proposed function (AMS) yielded the lowest MSE and GCV values across all samples. Therefore, this suggests that the model can generate precise predictions and enhance the performance of the focused data.

Exploring exhaled breath volatile organic compounds in occupational asthma: a pilot cross-sectional study

Occupational asthma (OA) is divided into allergic asthma (AA) and irritant-induced asthma (IIA). IIA can be divided further into three different phenotypic subtypes. Volatile organic compounds (VOCs) in exhaled breath can reflect metabolic changes in the body, and a wide range of them have been associated with various diseases in the last two decades. This is the first known study to explore breath VOCs in subjects with OA, aimed to identify potential biomarkers to distinguish OA from healthy controls, as well as between different OA subgroups. In a cross-sectional investigation, exhaled breath from 40 patients with OA and 45 respiratory healthy healthcare workers were collected with ReCIVA® Breath Sampler. Samples were analyzed through an untargeted approach using thermal desorption-gas chromatography mass spectrometry (TD-GC-MS), and VOCs were identified according to tier classification. The data underwent analysis using both non-parametric and parametric statistical methods. 536 VOCs were identified. Significance (p<0.05) was observed in several emitted VOCs. Among these, compounds such as 1-hexadecanol, 2,3-butanediol, xylene, phenol, acetone, 3-methylhexane, methylcyclohexane, and isoprene have biological implications or are associated with exposures linked to OA. These VOCs may reflect metabolic changes in the body and the microbiome, as well as external exposures due to occupation.&#xD;In particular, 1-hexadecanol, 2,3-butanediol, xylene and phenol are associated with reduced nicotinamide adenine dinucleotide (NADH) and production of reactive oxygen species (ROS), mechanisms that can be linked to asthmatic diseases and therefore suggests its potential as biomarkers. This study demonstrates that VOCs detected in exhaled breath could serve as indicators of occupational exposure and enhance diagnostic accuracy for asthma.&#xD.

Software Reliability Prediction by Adaptive Gated Recurrent Unit‐Based Encoder‐Decoder Model With Ensemble Empirical Mode Decomposition

ABSTRACTAccurate software reliability prediction is significant to software quality assurance. However, the rapid development and evolution of modern software pose more challenges for accurate software quality assessment. With the rapid development of machine learning and intelligent algorithms, data‐driven nonparametric models have gradually attracted increasing attention with outstanding prediction performance. However, we found that there may exist some prediction lags caused by the autocorrelation and nonstationarity of software fault data in the prediction of nonparametric models affecting their performance. To address this problem, we proposed an adaptive gated recurrent unit‐based encoder‐decoder model (ED‐GRU) with ensemble empirical mode decomposition (EEMD), effectively reducing prediction lags and performing accurate software fault number prediction. The autocorrelation and nonstationarity of fault data are first reduced by using first‐order difference and EEMD to clearly characterize the changing trend of the data. The frequency‐specific ED‐GRU networks are then combined to adaptively learn the nonlinear fluctuation trend of fault data under different frequency scales and obtain accurate prediction final results after aggregation. Experiments on eight public datasets showed that the proposed EEMD‐ED‐GRU‐PF model could effectively reduce the prediction lags and achieve the best prediction performance compared with four nonparametric and five parametric baseline methods in all datasets. Therefore, the proposed method can effectively and stably reduce the prediction lag to significantly improve the prediction accuracy. In this way, developers can accurately evaluate the current quality of the software and provide valuable guidance for software development and maintenance.

Python-based numerical study on bolted joints between Fe-SMA and steel plates for structural reinforcements

In order to rehabilitate the performance of deteriorated steel structures, the active prestressed reinforcement employing new intelligent material, Iron-based shape memory alloy (Fe-SMA), has received extensive attentions. To reveal the mechanical properties of bolted joints between Fe-SMA and steel plates, a parametric method to establish finite element model via secondary development of ABAQUS based on Python scripts was proposed. It greatly reduces modelling costs and improves modeling efficiency. Besides, the proposed numerical models were verified against the experimental results by failure mode, final deformation, load-displacement characteristics, ultimate load-bearing capacity and initial stiffness. It is demonstrated that the numerical simulation results are in good agreement with the experimental results. What is more, a parametric study on thickness and geometry of steel plates, as well as the end distance, edge distance and thickness of Fe-SMA plate was conducted to verify the applicability of current leading specifications for steel structures. It is evident from the results that the thickness of Fe-SMA should be controlled to prevent failure of parent steel structures. Furthermore, similar to steel plates, the thickness of Fe-SMA plate can be considered linear and the failure of bolt can be avoided, as described in Chinese code GB 50017–2017 and Eurocode 3 Part 1–8. However, the minimum allowable edge distance in Chinese code GB 50017–2017 and the ultimate load-bearing capacity calculated by Eurocode 3 Part 1–8 is conservative for bolted joints between Fe-SMA and steel plates. Hence, it is urgent to formulate design specifications for this kind of bolted joints for the scientific and economical application of Fe-SMA in structural active reinforcements.

A Robust Translational Motion Compensation Method for Moving Target ISAR Imaging Based on Phase Difference-Lv’s Distribution and Auto-Cross-Correlation Algorithm

Translational motion compensation constitutes a pivotal and essential procedure in inverse synthetic aperture radar (ISAR) imaging. Many researchers have previously proposed their methods to address this requirement. However, conventional methods may struggle to produce satisfactory results when dealing with non-stationary moving targets or operating under conditions of low signal-to-noise ratios (SNR). Aiming at this challenge, this article proposes a parametric non-search method that contains two main stages. The radar echoes can be modeled as polynomial phase signals (PPS). In the initial stage, the energy of the received two-dimensional signal is coherently integrated into a peak point by leveraging phase difference (PD) and Lv’s distribution (LVD), from which the high-order polynomial coefficients can be obtained accurately. The estimation of the first-order coefficients is conducted during the second stage. The auto-cross-correlation function for range profiles is introduced to enhance the accuracy and robustness of estimation. Subsequently, a novel mathematical model for velocity estimation is proposed, and its least squares solution is derived. Through this model, a sub-resolution solution can be obtained without requiring interpolation. By employing all the estimated polynomial coefficients, the non-stationary motion of the target can be fully compensated, yielding the acquisition of a finely focused image. Finally, the experimental findings validate the superiority and robustness of the proposed method in comparison to state-of-the-art approaches.

Carbon-mechanic dual-control design decision model of prefabricated structural components

Prefabricated enclosure structures (PES) have become the key technology for low-carbon construction in the engineering field due to their low labor demand, fast construction speed, and low environmental pollution. In the context of low-carbon development, efficiently designing PES that not only possess high-strength but also demonstrate significant greenhouse gas (GHG) reduction benefits has become a critical challenge. The objective of this study is to address the challenges of automated design and scientific decision-making methods for PES components. First, the Carbon-mechanic dual-control assessment model (CDAM) is proposed, providing a novel evaluation dimension for the design of prefabricated components. Subsequently, based on the CDAM, an automated design decision-making process, and model for components are established using an improved genetic algorithm and parameterized method. Finally, under the constraints of actual engineering conditions, adaptive solutions were automatically generated for specific cases. These solutions were comprehensively evaluated and optimized using CDAM, resulting in design guidelines and optimal solutions for PES that balance environmental performance and load-bearing capacity. The case analysis results show that the optimal scheme based on the carbon-mechanic dual-control (CM) index is: the sectional dimension is 830 mm, the reinforcement ratio is 0.9 % and the cavity ratio is 45 %, which shows the ideal characteristics of low-carbon and high-strength. Compared with the single index decision scheme, this scheme achieve a maximum of 42 % GHG emissions reduction. The high comprehensive performance scheme is characterized by small sectional dimension, high reinforcement ratio, and high cavity ratio. Cavity ratio is a key parameter to achieve high performance design. The research findings provide an important theoretical basis and technical support for the low-carbon transformation in the engineering field.

Monitoring Eastern White Pine Health by Using Field-Measured Foliar Traits and Hyperspectral Data.

Canopy foliar traits serve as crucial indicators of plant health and productivity, forming a vital link between plant conditions and ecosystem dynamics. In this study, the use of hyperspectral data and foliar traits for white pine needle damage (WPND) detection was investigated for the first time. Eastern White Pine (Pinus strobus L., EWP), a species of ecological and economic significance in the Northeastern USA, faces a growing threat from WPND. We used field-measured leaf traits and hyperspectral remote sensing data using parametric and non-parametric methods for WPND detection in the green stage. Results indicated that the random forest (RF) model based solely on remotely sensed spectral vegetation indices (SVIs) demonstrated the highest accuracy of nearly 87% and Kappa coefficient (K) of 0.68 for disease classification into asymptomatic and symptomatic classes. The combination of field-measured traits and remote sensing data indicated an overall accuracy of 77% with a Kappa coefficient (K) of 0.46. These findings contribute valuable insights and highlight the potential of both field-derived foliar and remote sensing data for WPND detection in EWP. With an exponential rise in forest pests and pathogens in recent years, remote sensing techniques can prove beneficial for the timely and accurate detection of disease and improved forest management practices.

A body-fitted adaptive mesh and Helmholtz-type filter based parameterized level-set method for structural topology optimization

A body-fitted adaptive mesh and Helmholtz-type filter based parameterized level-set method for structural topology optimization

Modelling Future Land Surface Temperature: A Comparative Analysis between Parametric and Non-Parametric Methods

Modelling Future Land Surface Temperature: A Comparative Analysis between Parametric and Non-Parametric Methods