Parametric Model Research Articles

Parametric Analytical Modulation Transfer Function Model in Turbid Atmosphere with Application to Image Restoration

To address the issues of image blurring and color distortion in hazy conditions, an image restoration method based on a parametric analytical modulation transfer function model is proposed under turbid atmospheric conditions. A source database is established using a numerical radiative transfer method based on discrete ordinate. Through multivariate nonlinear fitting and linear interpolation, the quantitative relationships among critical spatial frequency, turbid atmospheric MTF, and key atmospheric optical parameters—such as optical thickness, single scattering albedo, and asymmetry factor—are examined. A fast and efficient parametric analytical MTF model for turbid atmospheres is developed and applied to restore images affected by fog. The results demonstrate that, within the applicable range of the model, the model’s maximum mean relative error and the root mean square error are 7.16% and 0.0454, respectively. The computational speed is nearly a thousand times faster than that of the numerical radiative transfer method, achieving high accuracy and ease of application. Images restored using this model exhibit enhanced clarity and quality, effectively compensating for the degradation in image quality caused by turbid atmospheres. This approach represents a novel solution to the challenges of image processing in complex atmospheric environments.

Sex differences in clinical characteristics, phenotypes, management, and long-term outcomes of myocardial injury: an international cohort study

Abstract Background Several studies have demonstrated sex differences in patients presenting to the emergency department (ED) with myocardial infarction, heart failure or syncope. Whether sex-specific differences in chest pain patients with myocardial injury exist is unknown. Purpose To determine whether sex-specific differences in the clinical characteristics, phenotypes, management, and long-term outcomes of patients with myocardial injury exist. Methods Patients presenting to the ED with chest pain suggestive of MI were consecutively enrolled into a prospective multicenter international diagnostic study. Final diagnoses were centrally adjudicated by two independent cardiologists, applying the fourth universal definition of myocardial infarction, using all available medical records, including serial hs-cTn and cardiac imaging. Follow-ups were performed at 3 months, 1-, 2- and 5-years. The primary outcome was 5-year all-cause mortality. Secondary outcomes included 5-year cardiovascular mortality and non-cardiovascular mortality. Multivariable flexible parametric (Royston-Parmar) models assuming proportional hazards were fitted. Models were adjusted for age, history of coronary artery disease, history of malignancy, diabetes, hypertension, smoking status, history of liver disease, and renal function. Results Among 8046 chest pain patients presenting to the ED, 1158 (14.4%) had myocardial injury, of which 358 (31%) were females and 800 (69%) males. Women were older (79 years, vs. 73 years), had fewer cardiovascular risk factors and comparable non-cardiovascular comorbidities except for lung disease which was more commonly found in men. Myocardial injury phenotypes varied between sexes (Figure). The most common phenotype in both sexes was heart failure, yet it was more common in men (37.2%) than in women (22.3%). The second most common phenotype in women was hypertension (15.4%) and arrhythmia (15.4 %), while in men was myocarditis (17.8 %), followed by arrhythmia (15.8%). Women underwent less invasive coronary angiography (19.8% vs 26.9%) or stress testing (12% vs 21.5%). In the angiographic findings, women presented more often with normal findings (30.4% vs. 19.6%) or mild CAD (20.3% vs. 11.2%) whereas men displayed more frequently 3-vessel disease (14.5% vs. 36.4%), Table. After adjusting for known confounders, women were associated with a lower 5-years risk for all-cause mortality (HR 0.69; 95%CI: 0.56–0.83) and cardiovascular mortality (HR 0.61; 95%CI: 0.46–0.79). No statistically significant difference in non-cardiovascular mortality risk was found (HR 0.79; 95%CI: 0.59-1.04). Conclusions Women presenting with chest pain to the ED and having myocardial injury were less likely than men to undergo coronary angiography or non-invasive cardiac imaging. Long-term risk for all-cause or cardiovascular mortality was lower for women compared to men.Myocardial Injury phenotypesPatient management

Hot spots around Sagittarius A*. Joint fits to astrometry and polarimetry

Observations of Sagittarius A* ( in the near-infrared (NIR) show irregular flaring activity. Flares coincide with the astrometric rotation of the brightness centroid and with looping patterns in fractional linear polarization. These signatures can be explained with a model of a bright hot spot, transiently orbiting the black hole. We extend the capabilities of the existing algorithms to perform parameter estimation and model comparison in the Bayesian framework using NIR observations from the GRAVITY instrument, and simultaneously fitting the astrometric and polarimetric data. Using the numerical radiative transfer code we defined several geometric models describing a hot spot orbiting Sgr A*, threaded with a magnetic field, and emitting synchrotron radiation. We then explored the posterior space of our models with a nested sampling code We used Bayesian evidence to make comparisons between the models. We have used 11 models to sharpen our understanding of the importance of various aspects of the orbital model, such as non-Keplerian motion, hot-spot size, and off-equatorial orbit. All considered models converge to realizations that fit the data well, but the equatorial super-Keplerian model is favoured by the currently available NIR dataset. We have inferred an inclination of $ deg, which corroborates previous estimates, a preferred period of $ minutes, and an orbital radius of $ gravitational radii with the orbital velocity of $ times the Keplerian value. A hot spot with a diameter smaller than 5 gravitational radii is favoured. Black hole spin is not constrained well.

Management and outcomes of acute myocardial infarction in patients with Alzheimer's disease: a study of 2.7 million individuals with myocardial infarction, 2010 - 2020

Abstract Background As a consequence of population ageing, the prevalence of Alzheimer’s Disease (AD) is rising. Evidence-based clinical decision-making for those with AD that present with acute myocardial infarction (MI) is limited by difficulty in identifying which patients will benefit from guideline-directed invasive management versus a more conservative approach. Purpose This study aimed to assess the prevalence of AD in patients presenting with acute MI and characterise its association with patient factors, treatment utilisation and clinical outcomes. Methods A retrospective cohort study was conducted using the United States National Readmission Database (NRD), 2010–2020. International Classification of Disease (ICD; version 9 and 10, Clinical Modification) codes were used to identify patients with a principal diagnosis of MI, pre-existing AD and other comorbidities, in addition to invasive procedures (invasive coronary angiography ± percutaneous coronary intervention [PCI] or coronary artery bypass graft [CABG] surgery). Logistic and flexible parametric survival models (adjusted for age, sex, MI subtype, smoking status, comorbidities, and MI treatments) were used to evaluate the association between AD and in-hospital and 1-year clinical outcomes, respectively. Results Overall, 2,750,798 individuals were admitted with MI during the study period, of which 35,367 had Alzheimer’s disease; 1.3%; Figure 1). Patients with AD were older (median age 85 vs. 67), more likely to be female (57.6 vs. 37.9%), present with non-ST-elevation MI (77.7 vs. 69.5%), and had a greater burden of individual comorbidities (including chronic kidney disease and depression) and multimorbidity (≥2 long-term conditions; 77.4 vs. 71.0%). Patients with AD were less likely to undergo invasive management (coronary angiography ± PCI/CABG; 27.7 vs 77.6%; p < 0.001; adjusted odds ratio [aOR] 0.24, 95% confidence intervals [CI] 0.23–0.25). Patients with AD had greater mortality both in-hospital (11.9 vs. 5.2%; aOR 1.30 (95% CI: 1.26–1.35, p < 0.001) and 1-year (15.9 vs. 7.3%; adjusted hazard ratio [aHR] 1.21; 95% CI 1.18–1.25). AD was associated with a greater risk of major adverse cardiovascular events (MACE; myocardial infarction, stroke or all-cause mortality; 27.8 vs. 17.7%; aHR 1.10, 95% CI 1.08–1.12) at 1-year post-MI (figure 2). However, patients with AD that were selected to undergo invasive management experienced reduced MACE (aHR 0.74, 95% CI 0.70–0.77) and all-cause mortality (aHR 0.53, 95% CI 0.49–0.57) in comparison to those managed conservatively. Conclusions Patients with Alzheimer’s Disease presenting with acute MI face a poor prognosis marked by a high rate of MACE and all-cause mortality. Future research should seek to identify those who benefit most from invasive management. At present, individualised, shared and multidisciplinary decision-making is key to guide the management of patients with AD that present with acute MI.

Determinants of Long COVID illness duration and service use-an Irish retrospective cohort study

Abstract The Follow-up After Disease Acquisition (FADA) study is a retrospective cohort study of COVID-19 recovery, aimed to characterise the nature and severity of Long COVID (LC) in an Irish community-based sample. Duration analysis is conducted applying Cox’s proportional hazards model to those self-reported recovered from LC (n = 577) and a Gompertz distribution parametric model, determined best fit by AIC, for those still ill with LC (n = 928) at time of survey completion. A negative binomial model is estimated to understand the variation of services accessed for LC. The Hazard in this instance is recovery(<1 = contributing factor to duration). Factors significantly contributing to duration of illness are never married (Hazard Ratio (HR):0.689;p:0.01) higher physical activity levels prior to first C-19 infection (HR:0.964; p:0.06) and number of COVID-19 infections (H:0.926;p:0.07). Those that contract more recent variants are likely to recover quicker from their LC illness (baseline Wildtype. Alpha HR:1.7;p:0.0; Delta HR:3.36;p:0.0; Omicron HR:3.8; p:0.0). In variation of services used for LC, there are some major differences between those self-reporting as recovered from LC at time of survey completion and those still ill. Of those recovered, men, those sickest with LC and sick longest use a wider variation of services. Single people use a significantly narrower range of health services to combat their illness. For those still ill higher education, higher levels of physical activity pre COVID-19, healthcare workers, higher rates of comorbidity, those sick longest and those whose health has been most impacted by LC use a wider variation of services. A poorer health rating negatively impacts variation of services accessed for LC illness. More recent strains of COVID-19 are significantly less likely to lead to LC, this offers hope for the future, albeit more research is required to investigate factors to improve recovery rates for those already experiencing LC. Key messages • Those with Long COVID that recover quickly are fundamentally different to those that do not. • The number of COVID-19 infections is a key indicator of length of Long COVID illness, we need to protect our most vulnerable and put appropriate safeguards in place for healthcare provision.

An analysis of the virtual cardio-oncology research initiative (VICORI) resource: bleeding risk among cancer patients beyond 1-year post-myocardial infarction

Abstract Introduction Cardiovascular disease (CVD) and cancer are common causes of morbidity and mortality. Advancements in treatment strategies for both diseases have resulted in a growing population who live with both conditions. Myocardial infarction (MI) represents approximately 20% of all CVD admissions in cancer patients and 10% of patients who present with an acute MI have cancer. Managing MI patients with cancer require careful balancing of their ischaemic and bleeding risks. While dual antiplatelet therapy (DAPT) increases a patient’s bleeding risk, cancer patients are at further increased risk due to a range of direct and indirect cancer effects. While our recent studies demonstrated an increased bleeding risk in cancer patients in the 1st year after MI (the period of intensive APT), it is currently not known if this risk is sustained beyond this period. VICORI is the world’s first whole-country cardio-oncology research platform, linking data from the National Cancer Registration and Analysis Service, National Institute for Cardiovascular Outcomes Research and Hospital Episode Statistics [1]. We investigated the risk of bleeding among MI patients in the 2nd year post-MI, where a large majority of patients would have completed a course of DAPT, stratified by the presence or absence of cancer. Methods In this retrospective observational study, we investigated the risk of bleeding following an MI between 2006-2019, in patients with and without cancer. Cancer was defined as a diagnosis of cancer in the 1-year preceding MI. Patients who lived past the 1st year after MI were followed-up from 12 to 24 months after their MI. We used inverse probability weighting based on propensity scores to produce a balanced cohort of patients with and without cancer. Cox proportional hazard and flexible parametric modelling were used to investigate cancer as a predictor of bleeding. Subgroup analyses were performed on stent status, MI and cancer type. Results Of 506280 patients presenting with MI, 5666 (1.1%) had cancer while 500614 (98.9%) did not. Prostate (n=1377), colorectal (n=1024), lung (n=490), breast (n=448) and bladder (n=334) cancers were among the most common types of cancers. The rate (hazard) of bleeding in MI was higher in patients with cancer in the 2nd year post-MI (HR:1.42, 95%CI 1.29-1.56) compared to those without. From the flexible parametric survival analysis, the risk of bleeding in cancer patients in the 2nd year post-MI was lower compared to the 1st year but remained statistically significant compared to non-cancer patients. Conclusion In this large real-world study, cancer patients have a persistent, increased bleeding risk beyond the 1-year post-MI period, compared to those without cancer, and this risk differed by type of cancer. Further work is needed to identify specific patient characteristics in each patient sub-group which alters one’s ischaemic or bleeding risk so they can be balanced favourably with personalised strategies.

The Parameterized Oceanic Front-Guided PIX2PIX Model: A Limited Data-Driven Approach to Oceanic Front Sound Speed Reconstruction

In response to the demand for high-precision acoustic support under the condition of limited data, this study utilized high-resolution reanalysis data and in situ observation data to extract the Kuroshio Extension Front (KEF) section through front-line identification methods. By combining the parameterized oceanic front model and the statistical features of big data, the parameterized oceanic front was reconstructed. A proxy dataset was generated using the Latin hypercube sampling method, and the sound speed reconstruction model based on the PIX2PIX model was trained and validated using single sound speed profiles at different positions of the oceanic front, combined with the parameterized oceanic front model. The experimental results show that the proposed sound speed reconstruction model can significantly improve the reconstruction accuracy by introducing the parameterized front model as an additional input, especially in the shallow-water area. The mean absolute error (MAE) of the full-depth sound speed reconstruction for this model is 0.63~0.95 m·s−1, and the structural similarity index (SSIM) is 0.76~0.78. The MAE of the sound speed section within a 1000 m depth is reduced by 6.50~37.62%, reaching 1.95~3.31 m·s−1. In addition, the acoustic support capabilities and generalization of the model were verified through ray tracing models and in situ data. This study contributes to advancing high-precision acoustic support in data-limited oceanic environments, laying a solid groundwork for future innovations in marine acoustics.

A Global-in-Time Neural Network Approach to Dynamic Portfolio Optimization

ABSTRACT We discuss a neural network approach, which does not rely on dynamic programming techniques, to solve dynamic portfolio optimization problems subject to multiple investment constraints. The approach allows for objectives of a very general form encompassing both time-consistent and time-inconsistent objectives, as well as objectives requiring multi-level optimization. The number of parameters of the neural network remains independent of the number of portfolio rebalancing events. Compared to reinforcement learning, this technique avoids the computation of high-dimensional conditional expectations. The approach remains practical when considering large numbers of underlying assets, long investment time horizons or very frequent rebalancing events. We prove convergence of the numerical solution to the theoretical optimal solution of a large class of problems under fairly general conditions, and present ground truth analyses for a number of popular formulations, including mean-variance, mean-semi-variance, and mean-conditional value-at-risk problems. Numerical experiments show that if the investment objective functional is separable in the sense of dynamic programming, the correct time-consistent optimal investment strategy is recovered, otherwise we obtain the correct pre-commitment (time-inconsistent) investment strategy. This method is agnostic as to the underlying data generating assumptions, and results are illustrated using (i) parametric models for underlying asset returns, (ii) stationary block bootstrap resampling of empirical returns, and (iii) generative adversarial network (GAN)-generated synthetic asset returns.

Distributed parameter model of dynamic contrast-enhanced MRI in the identification of IDH mutation, 1p19q codeletion, and tumor cell proliferation in glioma patients

ObjectivesTo investigate the clinical value of hemodynamic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) in predicting glioma genotypes including isocitrate dehydrogenase (IDH) mutation, 1p/19q codeletion status and the tumor proliferation index (Ki-67) noninvasively. And to compare the diagnostic performance of parameters of distributed parameter (DP)model and extended Tofts (Ex-Tofts) model.Materials and methodsDynamic contrast-enhanced MRI (DCE-MRI) data of patients with glioma were prospectively enrolled from April 2021 to May 2023. The imaging data were analyzed using DP and Ex-Tofts model for evaluating the perfusion and permeability characteristics of glioma. Comparisons were performed according to IDH genotype in all glioma patients and 1p/19q codeletion in IDH mutation glioma patients. Receiver operating characteristic (ROC) curves were generated for DCE-MRI parameters. The Spearman rank correlation coefficients were calculated between DCE MRI parameters and Ki-67 index.ResultsIn IDH-mutation gliomas, a higher blood flow (F) was found in 1p/19q codeletion gliomas than in 1p/19q intact gliomas. No parameter derived from Ex-Tofts model showed significant differences in predicting 1p/19q status. Fractional volume of interstitial space (Ve) derived from both the DP and Ex-Tofts models exhibited optimal performance in predicting IDH genotype (AUC = 0.818, 0.828, respectively). Ve also showed the highest correlations with Ki-67 LI within their respective models in all gliomas (ρ = 0.62, 0.61), indicating comparable moderate positive associations. Ki-67ConclusionDP model showed a clear advantage in predicting 1p/19q status compared to Ex-Tofts model. The DP and Ex-Tofts models performed similarly in predicting IDH mutation and Ki-67 index.

An Integrated Urban Building Energy Benchmarking Workflow to Support Urban Energy Evaluation: A Case Study of Sheffield UK

Understanding energy demand and supply flow at a large urban scale is an essential step for urban designers, planners and policymakers in investigating how buildings within an existing urban context could be designed as a whole to support the future sustainable built environment. The contemporary approach is to model energy use activities at various building and urban scales. This, albeit a practical approach, poses significant challenges in acquiring good quality data concerning buildings and their interactions at an urban scale at an affordable price. This paper presents a streamlined benchmarking methodology with a parametric modelling workflow to complement the mainstream urban building energy modelling (UBEM) approach. The proposed building energy benchmarking workflow integrates multiple databases concerning building energy consumption, energy generation and underlying grid infrastructure. Parametric modelling serves as a tool for integrating databases through the underlying sortable geometric characteristics. This is envisaged to afford stockholders, such as policymakers or urban planners, greater flexibility to investigate energy demand and supply scenarios at an urban neighbourhood scale and further explore potential applications. Using the proposed workflow, we look at renewable solar energy to experiment with offsetting urban building energy consumption through reconfiguring existing electricity microgrids in the Sheffield city centre. The result of this study demonstrates how the presented urban building energy benchmarking (UBEB) workflow would afford capabilities and flexibility to support stakeholders, e.g., urban planners, policymakers, and end-users, to better understand existing barriers and explore actionable opportunities via re-configurable electricity microgrids.

Model uncertainty quantification of a degradation model of miter gates using normalizing flow-based likelihood-free inference

Degradation modeling is essential for failure prognostics of miter gates and subsequent risk-informed maintenance or operational decision-making. Typically, degradation models are formulated based on certain assumptions and simplifications and may not fully capture the complexity of underlying degradation mechanisms. Even minor errors in such models may lead to significant discrepancies in failure prognostics due to error accumulation over time. Aiming to improve the accuracy of the degradation model and ensure reliable failure prognostics, this article proposes a degradation model updating framework for miter gates using normalizing flow-based likelihood-free inference, accounting for both model parameter uncertainty and model form uncertainty (i.e., model bias). The proposed work consists of two phases: the offline training phase and the online updating phase. During the offline training phase, the unknown model bias term is modeled as a random noise with uncertain standard deviation. Synthetic data are then generated using a physical model based on prior knowledge of the uncertain model parameters and model bias. The synthetic data are utilized to train an inference model, which has a summary network for data compression and a conditional invertible neural network for parameter estimation. In the online updating phase, the trained inference model uses strain observations to obtain posterior samples. The Gaussian mixture model and dual particle filter techniques are utilized to recursively update posterior samples, thereby improving the estimation accuracy. Subsequently, model bias is inversely estimated using the degradation model, which uses the updated model parameters and the gap length from the inference model. Following that, a regression model is constructed to correct the biases inherent in the simplified degradation model. This process is implemented iteratively over time to perform continuous model updating and failure prognostics. Results of a case study demonstrate the efficacy of the proposed framework in reducing both model parameter uncertainty and recovering model biases and thereby improving the accuracy of failure prognostics of miter gates.

Electric fields imbue enzyme reactivity by aligning active site fragment orbitals

It is broadly recognized that intramolecular electric fields, produced by the protein scaffold and acting on the active site, facilitate enzymatic catalysis. This field effect can be described by several theoretical models, each of which is intuitive to varying degrees. In this contribution, we show that a fundamental effect of electric fields is to generate electrostatic potentials that facilitate the energetic alignment of reactant frontier orbitals. We apply this model to demystify the impact of electric fields on high-valent iron-oxo heme proteins: catalases, peroxidases, and peroxygenases/monooxygenases. Specifically, we show that this model easily accounts for the observed field-induced changes to the spin distribution within peroxidase active sites and explains the transition between epoxidation and hydroxylation pathways seen in Cytochrome P450 active site models. Thus, for the intuitive interpretation of the chemical effect of the field, the strategy involves analyzing the response of the orbitals of active site fragments, and their energetic alignment. We note that the energy difference between fragment orbitals involved in charge redistribution acts as a measure for the chemical hardness/softness of the reactive complex. This measure, and its sensitivity to electric fields, offers a single parameter model from which to quantitatively assess the effects of electric fields on reactivity and selectivity. Thus, the model provides an additional perspective to describe electrostatic preorganization and offers ways for its manipulation.

Differentiation of pathological subtypes and Ki-67 and TTF-1 expression by dual-energy CT (DECT) volumetric quantitative analysis in non-small cell lung cancer

BackgroundTo explore the value of dual-energy computed tomography (DECT) in differentiating pathological subtypes and the expression of immunohistochemical markers Ki-67 and thyroid transcription factor 1 (TTF-1) in patients with non-small cell lung cancer (NSCLC).MethodsBetween July 2022 and May 2024, patients suspected of lung cancer who underwent two-phase contrast-enhanced DECT were prospectively recruited. Whole-tumor volumetric and conventional spectral analysis were utilized to measure DECT parameters in the arterial and venous phase. The DECT parameters model, clinical-CT radiological features model, and combined prediction model were developed to discriminate pathological subtypes and predict Ki-67 or TTF-1 expression. Multivariate logistic regression analysis was used to identify independent predictors. The diagnostic efficacy was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong’s test.ResultsThis study included 119 patients (92 males and 27 females; mean age, 63.0 ± 9.4 years) who was diagnosed with NSCLC. When applying the DECT parameters model to differentiate between adenocarcinoma and squamous cell carcinoma, ROC curve analysis indicated superior diagnostic performance for conventional spectral analysis over volumetric spectral analysis (AUC, 0.801 vs. 0.709). Volumetric spectral analysis exhibited higher diagnostic efficacy in predicting immunohistochemical markers compared to conventional spectral analysis (both P < 0.05). For Ki-67 and TTF-1 expression, the combined prediction model demonstrated optimal diagnostic performance with AUC of 0.943 and 0.967, respectively.ConclusionsThe combined predictive model based on volumetric quantitative analysis in DECT offers valuable information to discriminate immunohistochemical expression status, facilitating clinical decision-making for patients with NSCLC.

Description of moments of inertia of superdeformed bands using two and three parameter models in A ≈ 60-90 mass region

Description of moments of inertia of superdeformed bands using two and three parameter models in A ≈ 60-90 mass region

Solar Species: Energy Optimization of Urban Form Through an Evolutionary Design Process

This paper proposes design guidelines to enhance energy efficiency and energy generation potential in active solar buildings. Additionally, it presents a variety of optimized urban forms characterized by attributes such as shape, layout, and number of buildings on the plot. These urban configurations are classified into solar species, each associated with a distinct range of high passive and active solar potential. These results were achieved by developing and applying a simulation-driven, multi-objective optimization technique for the early-stage design of a residential building cluster in a temperate climate. This method leverages both passive and active energy indicators, employing a genetic algorithm to identify optimal forms that maximize active solar potential while also minimizing operational energy demand. The approach utilizes a parametric modelling routine that relies on vertical cores and horizontal connections to produce design iterations featuring irregular geometry, while ensuring structural continuity and means of egress. The findings reveal a significant variability in onsite energy generation, with optimized solutions differing by a factor of 2.5 solely based on shape, underscoring the critical role of active solar potential. Taken together, these results hint at the descriptive and predictive capabilities of these solar species, making them a promising heuristic model for characterizing urban form in relation to energy performance.

A Fisher Information Theory of Aesthetic Preference for Complexity

When evaluating sensory stimuli, people tend to prefer those with not too little or not too much complexity. A recent theoretical proposal for this phenomenon is that preference has a direct link to the Observed Fisher Information that a stimulus carries about the environment. To make this theory complete, one must specify the model that the brain has about complexities in the world. Here, we develop this model by first obtaining the distributions of three indices of complexity measured as normalized Shannon Entropy in real-world images from seven environments. We then search for a parametric model that accounts for these distributions. Finally, we measure the Observed Fisher Information that each image has about the parameters of this model. The results show that with few exceptions, the distributions of image complexities are unimodal, have negative skewness, and are leptokurtotic. Moreover, the sign and magnitude of the skewness varies systematically with the location of the mode. After investigating tens of models for these distributions, we show that the Logit-Losev function, a generalization of the hyperbolic-secant distribution, fits them well. The Observed Fisher Information for this model shows the inverted-U-shape behavior of complexity preference. Finally, we discuss ways to test our Fisher-Information theory.

Parametric structure optimization of a main rotor blade as an application for FSI analysis in main rotor optimization process

Purpose Modern warfare and modern battlefield are very demanding. The recent conflicts showed that the usage of the helicopters is very limited and only the best constructions are able to provide support for the operations. The purpose of this research is to show the possibilities of new design tool for main rotor aerostructural optimization. It is a next chapter of the research that is aimed at finding new solutions for rotorcraft constructions. Design/methodology/approach This work presents a method of preliminary structure optimization of the main rotor blade using parametric modeling. It is the next step in the main rotor optimization studies. It is the next step after preparing the parametric model for the external shape CFD analysis. As a basis for parametric blade structure calculations, the analytical model is provided in this paper. The equations of rigid blade loads and, as a consequence of the strength elements, stresses are shown. The parametric blade modeling is conducted using the Graphic Integrated Programming language. The parametric design method is shown to be used for various blade planform models and different section airfoils. The structure of a blade is generated automatically after the user enters the parameters. The code-inbuilt analysis systems provide a quick inertia examination of the generated geometry, which is the basis for further optimization. The program calculates the blade loads and verifies them with the given material conditions and proposed safety factors. In the analysis, composite materials for the strength elements were proposed. Findings The results of this research showed the application of parametrization into the main rotor blade design loop. It was presented that the main rotor blade structure can be enhanced using fluid-structure interaction (FSI) methods. The time saving with the implementation the process into design loop is shown. Practical implications This work can be practically used in the main rotor blade design process. It provides the possibilities to check various blade aerodynamic configuration in a structure strength aspect. Originality/value To the best of the authors’ knowledge, there were no published research that combines the main rotor FSI analysis. The method, which is presented in the work, provides a new approach to a rotorcraft design. The application of the parametrization and combining it with the FSI method gives a novel solution for helicopters construction enhancement.

Development of a quantitative prediction algorithm for human cord blood-derived CD34+ hematopoietic stem-progenitor cells using parametric and non-parametric machine learning models

The transplantation of CD34+ hematopoietic stem-progenitor cells (HSPCs) derived from cord blood serves as the standard treatment for selected hematological, oncological, metabolic, and immunodeficiency disorders, of which the dose is pivotal to the clinical outcome. Based on numerous maternal and neonatal parameters, we evaluated the predictive power of mathematical pipelines to the proportion of CD34+ cells in the final cryopreserved cord blood product adopting both parametric and non-parametric algorithms. Twenty-four predictor variables associated with the cord blood processing of 802 processed cord blood units randomly sampled in 2020–2022 were retrieved and analyzed. Prediction models were developed by adopting the parametric (multivariate linear regression) and non-parametric (random forest and back propagation neural network) statistical models to investigate the data patterns for determining the single outcome (i.e., the proportion of CD34+ cells). The multivariate linear regression model produced the lowest root-mean-square deviation (0.0982). However, the model created by the back propagation neural network produced the highest median absolute deviation (0.0689) and predictive power (56.99%) in comparison to the random forest and multivariate linear regression. The predictive model depending on a combination of continuous and discrete maternal with neonatal parameters associated with cord blood processing can predict the CD34+ dose in the final product for clinical utilization. The back propagation neural network algorithm produces a model with the highest predictive power which can be widely applied to assisting cell banks for optimal cord blood unit selection to ensure the highest chance of transplantation success.

Association of Medicaid Expansion With Timely Receipt of Treatment and Survival Among Patients With HR-Negative, HER2-Positive Breast Cancer.

Hormone receptor (HR)-negative, HER2-positive (also called HER2-enriched) breast cancer has no worse prognosis than other breast cancers if it is treated with HER2-targeted therapy. Medicaid expansion under the Affordable Care Act (ACA) has been shown to be associated with improved access to care and outcomes for many cancers, but its association with receipt of care for HR-negative, HER2-positive breast cancer is unknown. We examined the association of Medicaid expansion with receipt of guideline-concordant treatment, time to treatment initiation, and survival among nonelderly women newly diagnosed with HR-negative, HER2-positive breast cancer. Women aged 18 to 62 years newly diagnosed with HR-negative, HER2-positive breast cancer between 2010 and 2018 were identified from the National Cancer Database. Outcomes included receipt of stage-based guideline-concordant treatment, timely initiation of treatment (<30 days, <60 days, <90 days from diagnosis), and stage-specific 2-year overall survival. A difference-in-differences (DID) analytic approach compared outcome changes following Medicaid expansion in expansion versus nonexpansion states. Multivariable linear probability models were used to estimate treatment outcomes, and flexible parametric survival models were used to evaluate survival, adjusting for sociodemographic and clinical confounders. A total of 31,401 patients were included. Medicaid expansion was associated with an increase of 0.58 percentage points (ppt; 95% CI, 0.01-1.16) in receipt of guideline-concordant treatment overall, a 2.43-ppt (95% CI, 0.68-4.18) increase in initiating guideline-concordant treatment <60 days after diagnosis, and a 1.17-ppt (95% CI, 0.02-2.32) increase in 2-year survival rate. The increase in 2-year survival associated with Medicaid expansion was most prominent for patients with stage III disease (DID, 3.81; 95% CI, 0.82-6.80). Medicaid expansion was associated with improved care and survival for patients with HR-negative, HER2-positive breast cancer, an aggressive cancer type for which prognosis largely depends on access to effective treatment.

Effect of hot deformation parameters on microstructure evolution mechanism and workability in a wear-resistance steel of machinery equipment

Hot compression experiments were conducted to study the flow behavior and hot workability of NM400 wear-resistance steel. A new Zener-Hollomon parameter ( Z) model is developed for coupling strain into the conventional Z model. The NM400 exhibits excellent resistance to instability owing to its low sensitivity to unsafe regions. Conversely, the peak efficiency of the power dissipation (EPD) region expands to the direction of low temperature with increasing of strain. The dynamic recrystallization (DRX) is facilitated at elevated temperatures and lower strain rates. DRX becomes the dominant softening mechanism with the increasing of temperature. Finally, 1050–1175 °C and 0.11–1.82 s−1 are recommended to be the optimal window. The optimal ln Z values for different strains are provided by the new Z model.