Accurate Prediction Of Response Research Articles

Accuracy of MRI Versus PET/CT in the Prediction of Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer.

Background Breast-conserving surgeries have significantly advanced breast cancer treatment, offering favorable oncological outcomes, enhanced cosmetic results, reduced postoperative morbidity, and better psychological acceptance compared to mastectomy. The introduction of neoadjuvant therapy has expanded the applicability of breast conservation surgery to include locally advanced tumors. Tumor response to neoadjuvant chemotherapy is evaluated using imaging modalities such as breast ultrasound, breast magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT). Accurate prediction of therapeutic response facilitates the planning of surgical and adjuvant treatments. This study aims to compare the diagnostic accuracy of MRI and PET/CT in predicting treatment response to neoadjuvant chemotherapy in breast cancer patients. Methods This retrospective study was conducted at a tertiary care center in Bahrain. A total of 138 patients with locally advanced breast cancer or human epidermal growth factor receptor-2 (HER2) positive, hormone receptor-negative cancers who underwent breast-conserving surgeries between June 2018 and December 2022 were included. The inclusion criteria focused on patients achieving a complete pathological responsefollowing neoadjuvant systemic therapy, ensuring a homogenous study population. Patients with hormone receptor-positive early breast cancers or metastatic tumors, ineligible for neoadjuvant chemotherapy, were excluded. Non-responders and partial responders were also excluded from the study. Statistical analysis was performed using IBM SPSS v26.0 (IBM Corp., Armonk, US). Response rates for the imaging modalities and histopathology results were assessed. Agreement between histology and imaging modalities was computed using kappa statistics. Diagnostic performance for predicting "no residual" disease was evaluated using the McNemar Test. All tests were two-tailed, with a p-value <0.05 considered statistically significant. Results The study included 138 patients, of whom 73 (52.9%) had an incomplete response or residual disease, while 65 (47.1%) had a complete response or no residual disease according to histology reports. There was slight agreement between post-neoadjuvant MRI and histology results (Cohen's kappa 0.172, p=0.010), while substantial agreement was observed between post-neoadjuvant PET/CT and histology results (Cohen's kappa 0.614, p=0.000). PET/CT demonstrated a higher sensitivity of 93.8% (p<0.001) and a specificity of 68.5%. Although MRI was more specific, the positive predictive value was comparable for both PET/CT and MRI. Conclusion PET/CT shows higher sensitivity and can serve as an early marker for predicting complete pathological response in post-neoadjuvant breast cancer patients. However, the prediction of residual disease is optimized by combining both MRI and PET/CT as diagnostic modalities.

Research on vibration response distorted similitude of cylindrical shells under modal distribution disorder

Similitude theory enables local-scale models to reflect the dynamic characteristics of full-scale models and is widely employed in engineering experimentation. However, shell structures, constrained by wall thickness, cannot undergo experiments through proportional downsizing. Existing similitude studies with retained wall thickness exhibit only minimal distortion, and accurate prediction of vibration responses is hindered by modal distribution disorder. To address these challenges, a method based on modified frequency response functions of natural frequencies and mode shapes is proposed. Scaling laws for natural frequencies and mode shapes of cylindrical shells with retained wall thickness are established. This method yields a maximum prediction error of 4.29 % for prototype natural frequencies, compared to 50.57 % for existing methods. Predicted prototype vibration acceleration responses correspond well with theoretical calculations in both frequency and amplitude, while existing methods fail entirely. The phenomenon of modal distribution disorder and the effectiveness of the proposed method in mitigating it are verified by modal experiments and response tests. Research reveals that the sensitivity of different modal frequencies to the wall thickness scaling factor varies, serving as the cause of modal distribution disorder. The modal distribution with the same circumferential wave number is unaffected by wall thickness. Modes above the bending frequency, with axial half-wavelength consistent with bending modes, are prone to disorder due to wall thickness distortion. During wall thickness distortion, a mode originally with the lowest frequency is surpassed towards lower frequency by the mode with larger circumferential wave number and the same axial half-wave number, leading to disorder. The proposed method addresses the challenge of minimal distortion in structural vibration response and poor performance, advancing the development of distorted similitude.

Information transfer between tip leakage vortex and blade aerodynamic force of a compressor cascade

The compressor blade exhibits intricate flow patterns near stall conditions, with the tip leakage vortex and the main flow shedding vortex from the blade's trailing edge identified as factors influencing the blade's aerodynamic force oscillation. The tip leakage vortex is recognized as the primary contributor to flow structures during non-synchronous vibration (NSV), although existing compressor NSV reduced order models often attribute the fluid oscillation to the passage flow shedding vortex. A tool capable of distinguishing between flow structures and blade aerodynamic force is crucial for resolving this discrepancy. Detached eddy simulations (DES) in a compressor cascade form the basis for gathering data on vortex structures and blade aerodynamic force. This study employs an information thesis method to elucidate the cause-and-effect relationship between the tip leakage vortex and blade aerodynamic force. Through a series of unsteady single passage simulations and experimental validation, the mechanisms leading to variations in blade aerodynamic force are demonstrated. Notably, this research utilizes Proper Orthogonal Decomposition (POD) mode time coefficients in span and axial directions to represent dynamic information on vortex structures. Additionally, transfer entropy is introduced as a metric to evaluate the causal interaction between turbulent flow of the tip leakage and the blade's aerodynamic force, marking a significant advancement. The analysis of information transfer entropy aids in identifying the vortex structure with a stronger relationship to aerodynamic force, a critical finding. By applying this approach, the study examines tip leakage vortex structures in various tip clearance sizes concerning aerodynamic force. The Tip3C and Tip5C tip geometries exhibit tip leakage vortex features with radial vortex characteristics and backflow, akin to structures associated with non-synchronous vibration. These tip vortex structures in the Tip3C and Tip5C tip geometry cascades demonstrate substantial correlations with aerodynamic force oscillation on the blade surface. Conversely, the Tip1C tip geometry cascade displays distinct tip leakage vortex features compared to Tip3C and Tip5C, with weaker ties to blade aerodynamic force oscillation. As simulation models progress, consideration of vortex structures in the passage and leakage vortex at the leading edge is imperative for accurate predictions of aerodynamic force oscillation response.

Eosinophils, Eosinophilic Gastrointestinal Diseases, and Inflammatory Bowel Disease: A Critical Review.

Inflammatory bowel disease (IBD) and eosinophilic gastrointestinal diseases (EGIDs) are complex, multifactorial chronic inflammatory disorders affecting the gastrointestinal tract. Their epidemiology, particularly for eosinophilic esophagitis (EoE), is increasing worldwide, with a rise in the co-diagnosis of IBD and EGIDs. Both disorders share common risk factors, such as early exposure to antibiotics or specific dietary habits. Moreover, from a molecular perspective, eosinophilic infiltration is crucial in the diagnosis of eosinophilic disorders, and it also plays a pivotal role in IBD histological diagnosis. Indeed, recent evidence highlights the significant role of eosinophils in the health of the intestinal mucosal barrier and as mediators between innate and acquired immunity, even indicating a potential role in IBD pathogenesis. This narrative review aims to summarize the current evidence regarding the common clinical and molecular aspects of EGIDs and IBD and the current state of knowledge regarding overlap conditions and their pathogenesis. Pubmed was searched until May 2023 to assess relevant studies describing the epidemiology, pathophysiology, and therapy of EGIDs in IBD. The immune pathways and mechanisms underlying both EGIDs and IBD remain partially known. An improved understanding of the role of eosinophils in overlapping conditions could lead to enhanced diagnostic precision, the development of more effective future therapeutic strategies, and a more accurate prediction of patient response. Consequently, the identification of red flags indicative of an eosinophilic disorder in IBD patients is of paramount importance and must be evaluated on a case-by-case basis.

Seismic response prediction and fragility assessment of high-speed railway bridges using machine learning technology

Nowadays, reliable and accurate prediction of the seismic response of high-speed railway bridges and then rapid assessment of their seismic fragility is crucial for the risk assessment of regional high-speed railway bridges. However, the traditional seismic fragility assessment based on nonlinear time history analysis (NTHA) has a characteristic of high computational cost. Therefore, this study explores different machine learning (ML) methods to develop a reliable rapid seismic fragility assessment framework for high-speed railway bridges. Taking a typical high-speed railway continuous (HRC) bridge as a case, a large number of numerical model and ground motion pairs considering various structural and ground motion features are established. Then, the two types of models including six popular ML methods are investigated, in which Lasso regression, artificial neural network (ANN) and support vector machine (SVM) are single models, and random forest (RF), extreme gradient boosting (XGBoost) and light gradient boosting machine (Light GBM) are ensemble models. A database containing a large number of seismic responses of different components is established through the NTHA to develop prediction models, and the optimal hyperparameters of different ML models are determined by ten-fold cross-validation and grid search. The study results show that the ensemble model can reliably predict the seismic response of the HRC bridge compared with the single model, and the XGBoost model is recommended for seismic fragility assessment. On this basis, the optimal ML model obtained by the performance evaluation is applied to develop seismic fragility curves of the HRC bridge, which are compared with the fragility curves based on NTHA. The results indicate that the XGBoost model can effectively replace the traditional NTHA for rapid seismic fragility assessment of the HRC bridge, which can achieve a sufficient assessment level while saving calculation costs.

Predicting emergent animal biodiversity patterns across multiple scales.

Restoring biodiversity-based resilience and ecosystem multi-functionality needs to be informed by more accurate predictions of animal biodiversity responses to environmental change. Ecological models make a substantial contribution to this understanding, especially when they encode the biological mechanisms and processes that give rise to emergent patterns (population, community, ecosystem properties and dynamics). Here, a distinction between 'mechanistic' and 'process-based' ecological models is established to review existing approaches. Mechanistic and process-based ecological models have made key advances to understanding the structure, function and dynamics of animal biodiversity, but are typically designed to account for specific levels of biological organisation and spatiotemporal scales. Cross-scale ecological models, which predict emergent co-occurring biodiversity patterns at interacting scales of space, time and biological organisation, is a critical next step in predictive ecology. A way forward is to first capitalise on existing models to systematically evaluate the ability of scale-explicit mechanisms and processes to predict emergent patterns at alternative scales. Such model intercomparisons will reveal mechanism to process transitions across fine to broad scales, overcome approach-specific barriers to model realism or tractability and identify gaps which necessitate the development of new fundamental principles. Key challenges surrounding model complexity and uncertainty would need to be addressed, and while opportunities from big data can streamline the integration of multiple scale-explicit biodiversity patterns, ambitious cross-scale field studies are also needed. Crucially, overcoming cross-scale ecological modelling challenges would unite disparate fields of ecology with the common goal of improving the evidence-base to safeguard biodiversity and ecosystems under novel environmental change.

Taller trees exhibit greater hydraulic vulnerability in southern Amazonian forests

Potential increases in drought frequency and vapour pressure deficit pose a risk to the future function of tropical trees. Previous studies have found that taller tropical trees show a stronger increase in mortality than shorter trees in response to dry anomalies, but the mechanisms behind this are unclear. Here we investigate whether canopy branches of taller tropical trees have different hydraulic traits compared to shorter conspecifics.We determined xylem resistance to embolism (P50), hydraulic safety margin (HSM), xylem functional traits and xylem theoretical hydraulic conductivity for canopy branches of four tree species across a range of tree heights (sapling to maximum tree height) in an ecotonal forest near the Amazonia-Cerrado transition.We found that canopy branches of taller trees within each species have lower HSM, suggesting that they are more susceptible to hydraulic failure under drought than smaller conspecifics. Height-related trends in HSM were driven by variation in P50 with height and not by variation in leaf water potential which did not vary with height. We find that canopy branches with greater xylem vessel diameters are generally more vulnerable to embolism, suggesting a potential role for a diameter-safety linkage in explaining observed patterns of decreasing HSM with height. However, we find no evidence of a branch-level trade-off between theoretical hydraulic conductivity and hydraulic vulnerability.The greater hydraulic vulnerability of larger trees provides a potential explanation for the higher drought-induced mortality observed in taller tropical trees. The consistency of the height-P50 relationship across species opens the door to a more accurate prediction of southern Amazon forest responses to future droughts. Whether the findings for forests in southern Amazonia can be generalized to other Amazonian regions remains an open question.

Predicting emergent animal biodiversity patterns across multiple scales.

Restoring biodiversity-based resilience and ecosystem multi-functionality needs to be informed by more accurate predictions of animal biodiversity responses to environmental change. Ecological models make a substantial contribution to this understanding, especially when they encode the biological mechanisms and processes that give rise to emergent patterns (population, community, ecosystem properties and dynamics). Here, a distinction between 'mechanistic' and 'process-based' ecological models is established to review existing approaches. Mechanistic and process-based ecological models have made key advances to understanding the structure, function and dynamics of animal biodiversity, but are typically designed to account for specific levels of biological organisation and spatiotemporal scales. Cross-scale ecological models, which predict emergent co-occurring biodiversity patterns at interacting scales of space, time and biological organisation, is a critical next step in predictive ecology. A way forward is to first capitalise on existing models to systematically evaluate the ability of scale-explicit mechanisms and processes to predict emergent patterns at alternative scales. Such model intercomparisons will reveal mechanism to process transitions across fine to broad scales, overcome approach-specific barriers to model realism or tractability and identify gaps which necessitate the development of new fundamental principles. Key challenges surrounding model complexity and uncertainty would need to be addressed, and while opportunities from big data can streamline the integration of multiple scale-explicit biodiversity patterns, ambitious cross-scale field studies are also needed. Crucially, overcoming cross-scale ecological modelling challenges would unite disparate fields of ecology with the common goal of improving the evidence-base to safeguard biodiversity and ecosystems under novel environmental change.

Towards autonomous inland shipping: a manoeuvring model in confined waterways

ABSTRACT Autonomous inland water vessels are essential for promoting intelligent and sustainable waterborne transport. An accurate ship manoeuvring model ensures reliable control strategies and enhances navigation safety. Although ship manoeuvrability models have existed for decades, few studies address shallow and restricted waters. This study introduces a manoeuvring model for inland water vessels, accounting for confinement effects on ship motion. The Manoeuvring Modelling Group (MMG) model in open water serves as the baseline, incorporating empirical methods for shallow water and bank effects. This approach aims to provide a fast and accurate prediction of vessel motion response. The model was validated with free-running experimental data of a pusher-barge model from turning tests at three water depths. Additional case studies highlight shallow water impact compared to infinite water performance under bank effects. Finally, course-keeping case studies are presented, integrating a Proportional-Derivative controller with combined river current and bank-induced forces..

Integrating Tumor and Nodal Radiomics to Predict the Response to Neoadjuvant Chemotherapy and Recurrence Risk for Locally Advanced Gastric Cancer.

To develop and evaluate machine learning models using tumor and nodal radiomics features for predicting the response to neoadjuvant chemotherapy (NAC) and recurrence risk in locally advanced gastric cancer (LAGC). Early and accurate response prediction is vital to stratify LAGC patients and select proper candidates for NAC. A total of 218 patients with LAGC undergoing NAC followed by gastrectomy were enrolled in our study and were randomly divided into a training cohort (n = 153) and a validation cohort (n = 65). We extracted 1316 radiomics features from the volume of interest of the primary lesion and maximal lymph node on venous phase CT images. We built 3 radiomics signatures for distinguishing good responders and poor responders based on tumor radiomics (TR), nodal radiomics (NR), and a combination of the two (TNR), respectively. A nomogram was then developed by integrating the radiomics signature and clinical factors. Kaplan- Meier survival curves were used to evaluate the prognostic value of the nomogram. The TNR signature achieved improved predictive value, with AUCs of 0.755 and 0.744 in the training and validation cohorts. Our proposed nomogram model (TNRN) showed a good performance for GR prediction in the prediction efficacy, calibration ability, and clinical benefit, with AUCs of 0.779 and 0.732 in the training and validation cohorts, superior to the clinical model. Moreover, the TNRN could accurately classify the patients into high-risk and low-risk groups in both training and validation cohorts with regard to postoperative recurrence and metastasis. The TNRN performed well in identifying good responders and provided valuable information for predicting progression-free survival time (PFS) in patients with LAGC who underwent NAC.

Vibration Suppression of Graphene Reinforced Laminates Using Shunted Piezoelectric Systems and Machine Learning

The implementation of a machine learning approach to predict vibration suppression, as derived from nanocomposite laminates with piezoelectric shunted systems, is studied in this article. Datasets providing the vibration response and vibration attenuation are developed using parametric finite element simulations. A graphene/fibre-reinforced laminate cantilever beam is used in those simulations. Parameters, including the graphene and fibre reinforcements content, as well as the fibre angles, are among the inputs. Output is the vibration suppression achieved by the piezoelectric shunted system. Artificial Neural Networks are trained and tested using the derived datasets. The proposed methodology can be used for a fast and accurate prediction of the vibration response of nanocomposite laminates.

Deep learning-based prediction of structural responses of RC slabs subjected to blast loading

Considering the risk of explosion, studying the dynamic structural behavior of reinforced concrete (RC) slabs subjected to blast loads is crucial for enhancing their resistance. Conventional approaches, including costly experimental tests, highly hypothetical analytical methods, and time-consuming numerical simulations, have their limitations. This study presents two deep learning-based models for rapid and accurate prediction of explosion-induced responses in RC slabs. Available literature data and supplemented numerical simulation data are used for training and testing. First, a multi-layer perceptron (MLP) model is established to predict the maximum displacement of RC slabs subjected to explosions. The input features include 10 parameters. The training results show that the MLP model exhibits good prediction performance. Second, a one-dimensional convolutional neural network (1D-CNN) model is constructed to predict the failure modes of RC slabs under explosive loads. The input features of the model are consistent with those of the MLP model. The 1D-CNN model outperforms the other five conventional machine learning models in classification. Furthermore, a permutation feature importance analysis is conducted to determine the effects of the 10 input features on the predicted outcomes. This analysis makes the predictions interpretable, thereby bolstering the credibility of the models. Results show that the proposed deep learning models offer an efficient and reliable method to predict the structural response of RC slabs under explosion loads.

Advanced Computational Analysis of Cobalt-Based Superalloys through Crystal Plasticity.

This study introduces an advanced computational method aimed at accelerating continuum-scale processes using crystal plasticity approaches to predict mechanical responses in cobalt-based superalloys. The framework integrates two levels, namely, sub-grain and homogenized, at the meso-scale through crystal plasticity finite element (CPFE) platforms. The model is applicable across a temperature range from room temperature up to 900 °C, accommodating various dislocation mechanisms in the microstructure. The sub-grain level explicitly incorporates precipitates and employs a dislocation density-based constitutive model that is size-dependent. In contrast, the homogenized level utilizes an activation energy-based constitutive model, implicitly representing the γ' phase for efficiency in computations. This level considers the effects of composition and morphology on mechanical properties, demonstrating the potential for cobalt-based superalloys to rival nickel-based superalloys. The study aims to investigate the impacts of elements including tungsten, tantalum, titanium, and chromium through the homogenized constitutive model. The model accounts for the locking mechanism to address the cross-slip of screw dislocations at lower temperatures as well as the glide and climb mechanism to simulate diffusions at higher temperatures. The model's validity is established across diverse compositions and morphologies, as well as various temperatures, through comparison with experimental data. This advanced computational framework not only enables accurate predictions of mechanical responses in cobalt-based superalloys across a wide temperature range, but also provides valuable insights into the design and optimization of these materials for high-temperature applications.

Coupling a nonlinear finite element mooring model with an overset CFD solver for dynamic analysis of floating structures in waves

An accurate prediction of motion responses of a moored floating structure requires high-fidelity coupled analysis between the structure and its mooring system in waves, which involves both mooring line dynamics and fluid dynamics. In this paper, we develop a nonlinear finite element dynamic mooring analysis module that allows large axial stretching of mooring lines under the open source CFD framework OpenFOAM, and couple it with an Overset CFD solver to establish a fully coupled finite element mooring-CFD analysis model for floating structures. The coupled model is advantageous over existing models in that it is able to deal with large axial mooring stretching and complex mesh motions induced by significant structure responses under severe operating conditions. The developed dynamic mooring analysis method is firstly validated against publicly available data. In order to validate the coupled model, a series of experimental tests were carried out for a semi-submersible platform moored by eight catenary lines under different operating conditions, including free decay, regular and irregular wave tests. The dynamic responses of the floating structure and mooring tension are analyzed and compared with experimental data. Results show that numerically predicted structure responses and mooring tension are generally in good agreement with experimental measurements. Meanwhile, the strongly nonlinear large-amplitude resonant motions generated by the interaction of irregular waves with the moored floating structure are also investigated. Comparisons reveal that the coupled model can accurately predict the resonant spectral peaks of the structure responses, indicating that the it is capable of simulating large-amplitude motions of moored floating structures. The coupled model developed in this paper can be further applied to study the survivability of moored floating structures under harsh sea conditions.

ScPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism.

With the rapid advancement of single-cell sequencing technology, it becomes gradually possible to delve into the cellular responses to various external perturbations at the gene expression level. However, obtaining perturbed samples in certain scenarios may be considerably challenging, and the substantial costs associated with sequencing also curtail the feasibility of large-scale experimentation. A repertoire of methodologies has been employed for forecasting perturbative responses in single-cell gene expression. However, existing methods primarily focus on the average response of a specific cell type to perturbation, overlooking the single-cell specificity of perturbation responses and a more comprehensive prediction of the entire perturbation response distribution. Here, we present scPRAM, a method for predicting perturbation responses in single-cell gene expression based on attention mechanisms. Leveraging variational autoencoders and optimal transport, scPRAM aligns cell states before and after perturbation, followed by accurate prediction of gene expression responses to perturbations for unseen cell types through attention mechanisms. Experiments on multiple real perturbation datasets involving drug treatments and bacterial infections demonstrate that scPRAM attains heightened accuracy in perturbation prediction across cell types, species, and individuals, surpassing existing methodologies. Furthermore, scPRAM demonstrates outstanding capability in identifying differentially expressed genes under perturbation, capturing heterogeneity in perturbation responses across species, and maintaining stability in the presence of data noise and sample size variations. https://github.com/jiang-q19/scPRAM and https://doi.org/10.5281/zenodo.10935038.

Experimental lap-shear tests on friction dampers with single and double slotted holes

Several experimental lap shear tests have been performed to assess the initial values of the slip resistance and their evolution during a cyclic loading history. Both friction dampers with parallel slotted holes and double slotted holes have been investigated. Friction devices with shims made of a previously tested material, called M4, have been tested and the tribological behaviour, the degradation of the friction coefficient, the influence of the bolts’ preload and the effect of Belleville washers have also been investigated. Moreover, a phenomenological model for the force-displacement response has been calibrated for the modelling of the cyclic response of the friction device. This is particularly important for the development of non-linear dynamic analyses of seismic resistant frames equipped with friction joints aiming at an accurate prediction of the structural response during a seismic event. Finally, a quality control of the friction properties of different batches of friction shims has been performed by means of the Fisher-Berens and Snedecor tests.

The drivers of intraspecific trait variation and their implications for future tree productivity and survival.

Forests are facing unprecedented levels of stress from pest and disease outbreaks, disturbance, fragmentation, development, and a changing climate. These selective agents act to alter forest composition from regional to cellular levels. Thus, a central challenge for understanding how forests will be impacted by future change is how to integrate across scales of biology. Phenotype, or an observable trait, is the product of an individual's genes (G) and the environment in which an organism lives (E). To date, researchers have detailed how environment drives variation in tree phenotypes over long time periods (e.g., long-term ecological research sites [LTERs]) and across large spatial scales (e.g., flux network). In parallel, researchers have discovered the genes and pathways that govern phenotypes, finding high degrees of genetic control and signatures of local adaptation in many plant traits. However, the research in these two areas remain largely independent of each other, hindering our ability to generate accurate predictions of plant response to environment, an increasingly urgent need given threats to forest systems. I present the importance of both genes and environment in determining tree responses to climate stress. I highlight why the difference between G versus E in driving variation is critical for our understanding of climate responses, then propose means of accelerating research that examines G and E simultaneously by leveraging existing long-term, large-scale phenotypic data sets from ecological networks and adding newly affordable sequence (-omics) data to both drill down to find the genes and alleles influencing phenotypes and scale up to find how patterns of demography and local adaptation may influence future response to change.

Interrelationships of CO2, CH4, and N2O fluxes in snow-covered temperate soils, Northern Japan

This study focuses on assessing the concentrations, fluxes, and production rates of greenhouse gases (CO2, CH4, and N2O) in a cold temperate grassland soil underlying snowpack during the winter of 1996/7 in northern Japan. Results included mean ± standard deviation (range) correlation coefficients (R2) for CO2–CH4 concentrations and CO2–N2O concentrations of 0.93 ± 0.07 (0.81–0.99) and 0.96 ± 0.06 (0.83–0.99) for winter, and 0.74 ± 0.17 (0.55–0.92) and 0.96 ± 0.05 (0.88–0.99) for summer, respectively. This suggests close relationships between the mechanisms of CO2 and N2O production and the oxidation of CH4, which are influenced by factors such as oxygen availability, temperature, and moisture in the soil. Furthermore, the study found that winter fluxes of CO2–CH4 and CO2–N2O through the snowpack showed positive linear correlations. Winter CO2 emissions accounted for 96 % of the variability in CH4 oxidation and 77 % of the variability in N2O emissions. This demonstrates that winter CO2 emissions were affected to the magnitude of CH4 oxidations and N2O emissions in the soil. These findings have implications for the modification of terrestrial ecosystem models in temperate regions, particularly in assessing contributions from winter greenhouse gas fluxes to overall annual emissions. Understanding the interrelationships and dynamics of greenhouse gases throughout the year is crucial for accurate modeling and predictions of ecosystem responses to climate change.

Intentional Blade Mistuning to Emulate the Dynamic Influence of Split Ring Damper

Abstract A novel methodology to predict responses of bladed disks incorporated with a split ring damper is proposed. To account for the dynamic characteristics of a split ring damper, intentional blade stiffness mistuning is presented. It is assumed that the mistuning is proportional to the blade stiffness matrix and uniformly distributed to all blades. Therefore, responses of bladed disks integrated with a split ring damper are captured by the detuned bladed disk integrated with a continuous ring damper. The proposed method is demonstrated through a lumped parameter model and validated using a complete finite element model. A key advantage of the proposed approach is that the dynamic characteristics of a split ring damper are emulated by a continuous ring damper in the detuned bladed disk. Moreover, the approach holds the potential to facilitate reduced-order modeling (ROM) by employing sector-level calculations. This results in substantial reductions in computational efforts while maintaining accurate predictions of forced responses.

Linearized Models of the Coupled Rotorcraft Flight Dynamics and Acoustics for Real-Time Noise Prediction

This article demonstrates the linearization of the coupled rotorcraft flight dynamics and aeroacoustics to provide realtime acoustic predictions in generalized maneuvering flight. To demonstrate the methodology, the study makes use of a nonlinear simulation model of a generic utility helicopter (PSUHeloSim) that is coupled with an aeroacoustic solver based on a marching cubes algorithm. A periodic equilibrium of the coupled rotorcraft flight dynamics and acoustics is first found at a desired flight condition using a modified harmonic balance trim solution method. Next, the nonlinear time-periodic dynamics are linearized about that periodic equilibrium and transformed into an equivalent higher order linear time-invariant system in harmonic decomposition form. Composite aeroacoustic measures are included as an output of this system. To speed up runtime and make control design tractable, the order of these harmonic decomposition models is reduced via residualization to an 8-state model where the states are representative of the rigid-body dynamics of the aircraft. This 8-state model is shown to provide accurate acoustic response predictions for small-amplitude pilot inputs and to abate runtime by a factor of approximately 104, thus enabling acoustic predictions in generalized maneuvering flight that are significantly faster than real time. The 8-state model is subsequently used to demonstrate the use of linear system tools for the dynamic analysis of the coupled rotorcraft flight dynamics and acoustics.