Search

PyMAPDL, which stands for Python based Mechanical Ansys Parametric Design Language, provides a Pythonic interface to Ansys for automating tasks and managing multiple simulations. The present study focuses on utilizing PyMAPDL to numerically explore the stress and deformation conditions in the domed section of a cylindrical liner, specifically by analyzing ellipsoidal and torispherical dome shapes through the creation of a parametric model in Python. The output suggests a better selection of the dome geometrical parameters. The study extends to filament-wound composite pressure vessels, emphasizing the challenges in determining winding angles and layer thickness variations along the meridian line in dome parts. Cadfil software is used to obtain the thickness and angle variation of the composite layers, and the output data from Cadfil is imported into Ansys ACP as lookup table to generate the composite layer for linear elastic analysis of single composite layer on the liner. The findings suggest that a lower helical angle enhances strength protection in the dome section. • Utilization of Python via PyMAPDL (Python based Mechanical Ansys Parametric Design Language) for insightful analysis of dome geometrical parameters. • Ellipsoidal domes deform steadily until a radius-to-height ratio of 1.25, then stress and deformation rise. Torispherical domes require careful parameter selection for minimal deformation and stress; higher R k /R (knuckle radius to cylinder radius ratio) and lower R c /R (crown radius to cylinder radius ratio) ratios are preferable. • Cadfil software employed to determine composite layer thickness and angle variations, integrated into Ansys ACP for analysis. • Lower helical angles improve strength protection in dome sections. [ABSTRACT FROM AUTHOR]

In order to reveal the coupling parametric vibration characteristics of stay cables under combined excitation, considering the effects of cable geometric nonlinearity, inclination and the cooperative vibration of adjacent cables and bridge deck beams, a single-beam–double-cable coupled parametric vibration model excited by Gaussian white noise and deterministic harmonic excitation is established, and the coupled motion equations are derived. The Milstein–Platen method is used to directly obtain the coupled random vibration time history of the single-beam–double-cable structure, and an iterative method is proposed to counter the influence of the parametric diffusion coefficient on the numerical format. By comparing with the finite element method and the Monte Carlo numerical simulation method, the accuracy of the Milstein–Platen method in solving the vibration time history of cable–beam coupling parameters under combined excitation is first verified. Then the random displacement, power spectral density and probability density variation of the cable and beam under the combined excitation of different intensities are analyzed from the angle of random orbit. The results show that under the joint action of deterministic harmonics and random excitation, the time–domain, frequency, and probability characteristics of the single-beam–double-cable coupling system are greatly affected by the Gaussian white noise excitation proportion coefficient, and the degree of influence is different. In addition, compared with the single cable model, the vibration analysis results of the coupling model considering multiple stay cables are more reasonable. [ABSTRACT FROM AUTHOR]

The modality is an important topic for modelling. Using parametric models is an efficient way when real data set shows trimodality. In this paper, we propose a new class of trimodal probability distributions, that is, probability distributions that have up to three modes. Trimodality itself is achieved by applying a proper transformation to density function of certain continuous probability distributions. At first, we obtain preliminary results for an arbitrary density function $ g(x) $ g (x) and, next, we focus on the Gaussian case, studying trimodal Gaussian model more deeply. The Gaussian distribution is applied to produce the trimodal form of Gaussian known as normal distribution. The tractability of analytical expression of normal distribution and properties of the trimodal normal distribution are important reasons why we choose normal distribution. Furthermore, the existing distributions should be improved to be capable of modelling efficiently when there exists a trimodal form in a data set. After new density function is proposed, estimating its parameters is important. Since Mathematica 12.0 software has optimization tools and important modelling techniques, computational steps are performed using this software. The bootstrapped form of real data sets are applied to show the modelling ability of the proposed distribution when real data sets show trimodality. [ABSTRACT FROM AUTHOR]

Agriculture is a very important sector in Africa's economic development, particularly in Burkina Faso, as it employs a large proportion of the population. Given the importance of labor in this sector, a good allocation of the different types of labor could help increase agricultural productivity in Burkina Faso. This research contributes to the literature by determining the specific contributions of each type of labor in enhancing cotton productivity. The sample of this research is 477 cotton farms, and a semiparametric stochastic frontier model has been used in the analysis. The results show that the proportion of wage labor has a nonlinear effect and contributes to improving cotton productivity when the number of educated people in the household increases. But family labor decreases cotton productivity when the number of educated people in the household increase. The comparison between the findings of the semiparametric and parametric frontier shows that technical efficiency is 72.44% when education is used as the channel through which production factors affect cotton productivity. However, this technical efficiency is 54.96% when production factors directly affect cotton productivity in the parametric frontier model. Promoting education in rural areas will help to increase the number of people educated and consequently improve cotton productivity. [ABSTRACT FROM AUTHOR]

We present a new extended parametric equation-of-state model for thermodynamic properties and the correlation length for a simple fluid near its liquid–gas critical point. The model involves 16 universal parameters to perfectly match 10 leading universal amplitudes of the asymptotic Ising-like limit of the critical-to-classical crossover functions calculated by Garrabos and Bervillier [Phys. Rev. E 74,021113 (2006)] from the massive renormalization scheme. The universal values of 8 Ising-like amplitude combinations are then matched exactly. The closure of the construction of parameters is determined after a careful analysis of the intrinsic limitation of parametric equations to describe the universal features at the first order of the confluent corrections-to-scaling. In the asymptotic mean-field limit, the crossover master model also reproduces the mean-field amplitude combinations except for the susceptibility case. The new model is compared with the crossover parametric model previously developed by Agayan et. al [Phys. Rev. E 64, 02615 (2001)]. The residuals from comparison with the mean crossover functions of Garrabos and Bervillier are reported to define the application range of the crossover master model to any simple fluid for which the generalized critical coordinates of the liquid–gas critical point are known. [ABSTRACT FROM AUTHOR]

Traditional structural design involves drawing recognition, repeated modeling, parameter tuning, and numerous mechanical analyses by skilled designers, which is time-consuming and inefficient. To address those problems, a two-stage automatic structural design of the steel frame based on expert experiences is proposed. In the first stage, from the computer-aided design plain drawing, semantic features (walls and openings) and geometrical information of architectural elements are extracted by a layer classification method. The segmentation of rooms is conducted by an enclosed region detection method and the connectivity graph is generated using the connected component analysis method. Based on expert experiences considering both structure and architectural function requirements, the structural member configuration and the floor load distribution are automatically established to obtain the parametric structural model. In the second stage, a modified particle swarm optimization (MPSO) based on expert experiences is proposed for single-objective structural optimization according to the design codes. Then based on MPSO, a hierarchical multi-objective optimization method is adopted to obtain more available solutions with different economic benefit and redundant safety. The results show that the proposed two-stage structural design framework is fully automatic and highly efficient. It integrates parametric modeling and structural optimization, and also enables effective transfer of different data items including architectural plan and structural model. It provides a guideline to automatic structural design of steel frames. [ABSTRACT FROM AUTHOR]

This study aimed to determine the accuracy and consistency of established methods of extrapolating mean survival for immuno-oncology (IO) therapies, the extent of any systematic biases in estimating long-term clinical benefit, what influences the magnitude of any bias, and the potential implications for health technology assessment. A targeted literature search was conducted to identify published long-term follow-up from clinical trials of immune-checkpoint inhibitors. Earlier published results were identified and Kaplan-Meier estimates for short- and long-term follow-up were digitized and converted to pseudo–individual patient data using an established algorithm. Six standard parametric, 5 flexible parametric, and 2 mixture-cure models (MCMs) were used to extrapolate long-term survival. Mean and restricted mean survival time (RMST) were estimated and compared between short- and long-term follow-up. Predicted RMST from extrapolation of early data underestimated observed RMST in long-term follow-up for 184 of 271 extrapolations. All models except the MCMs frequently underestimated observed RMST. Mean survival estimates increased with longer follow-up in 196 of 270 extrapolations. The increase exceeded 20% in 122 extrapolations. Log-logistic and log-normal models showed the smallest change with additional follow-up. MCM performance varied substantially with functional form. Standard and flexible parametric models frequently underestimate mean survival for IO treatments. Log-logistic and log-normal models may be the most pragmatic and parsimonious solutions for estimating IO mean survival from immature data. Flexible parametric models may be preferred when the data used in health technology assessment are more mature. MCMs fitted to immature data produce unreliable results and are not recommended. • Treatment with immune-checkpoint inhibitors can result in a proportion of patients achieving durable response, generating a long tail to the survival distribution. Conventional extrapolation methods applied to short-term data may not perform well in estimating mean survival in these circumstances. • Both standard parametric and spline-based flexible parametric models (FPMs) tend to underestimate survival benefit for immuno-oncology therapies. Performance seems to worsen as the proportion of durable responders increases. More flexible models perform better than restrictive models, but with some risk of overfitting to short-term data. Mixture-cure models fitted to early data cuts produce unreliable results and may substantially overestimate mean survival. • Log-logistic and log-normal models may be the most pragmatic and parsimonious solutions for estimating mean survival from short-term data when the proportion of durable responders is expected to be low. FPMs using odds or normal scale may be better options when the data used in health technology assessment are more mature. When the expected proportion of durable responders is high, neither standard nor FPMs are expected to perform well and methods integrating external information should be considered. [ABSTRACT FROM AUTHOR]

In psychophysics and psychometrics, an integral method to the discipline involves charting how a person's response pattern changes according to a continuum of stimuli. For instance, in hearing science, Visual Analog Scaling tasks are experiments in which listeners hear sounds across a speech continuum and give a numeric rating between 0 and 100 conveying whether the sound they heard was more like word "a" or more like word "b" (i.e. each participant is giving a continuous categorization response). By taking all the continuous categorization responses across the speech continuum, a parametric curve model can be fit to the data and used to analyze any individual's response pattern by speech continuum. Standard statistical modeling techniques are not able to accommodate all of the specific requirements needed to analyze these data. Thus, Bayesian hierarchical modeling techniques are employed to accommodate group-level non-linear curves, individual-specific non-linear curves, continuum-level random effects, and a subject-specific variance that is predicted by other model parameters. In this paper, a Bayesian hierarchical model is constructed to model the data from a Visual Analog Scaling task study of mono-lingual and bi-lingual participants. Any nonlinear curve function could be used and we demonstrate the technique using the 4-parameter logistic function. Overall, the model was found to fit particularly well to the data from the study and results suggested that the magnitude of the slope was what most defined the differences in response patterns between continua. [ABSTRACT FROM AUTHOR]

In Bayesian statistics, posterior contraction rates (PCRs) quantify the speed at which the posterior distribution concentrates on arbitrarily small neighborhoods of a true model, in a suitable way, as the sample size goes to infinity. In this paper, we develop a new approach to PCRs, with respect to strong norm distances on parameter spaces of functions. Critical to our approach is the combination of a local Lipschitz-continuity for the posterior distribution with a dynamic formulation of the Wasserstein distance, which allows to set forth an interesting connection between PCRs and some classical problems arising in mathematical analysis, probability and statistics, e.g., Laplace methods for approximating integrals, Sanov's large deviation principles in the Wasserstein distance, rates of convergence of mean Glivenko–Cantelli theorems, and estimates of weighted Poincaré–Wirtinger constants. We first present a theorem on PCRs for a model in the regular infinite-dimensional exponential family, which exploits sufficient statistics of the model, and then extend such a theorem to a general dominated model. These results rely on the development of novel techniques to evaluate Laplace integrals and weighted Poincaré–Wirtinger constants in infinite-dimension, which are of independent interest. The proposed approach is applied to the regular parametric model, the multinomial model, the finite-dimensional and the infinite-dimensional logistic-Gaussian model and the infinite-dimensional linear regression. In general, our approach leads to optimal PCRs in finite-dimensional models, whereas for infinite-dimensional models it is shown explicitly how the prior distribution affect PCRs. [ABSTRACT FROM AUTHOR]

We consider a parametric modelling approach for survival data where covariates are allowed to enter the model through multiple distributional parameters (i.e., scale and shape). This is in contrast with the standard convention of having a single covariate-dependent parameter, typically the scale. Taking what is referred to as a multi-parameter regression (MPR) approach to modelling has been shown to produce flexible and robust models with relatively low model complexity cost. However, it is very common to have clustered data arising from survival analysis studies, and this is something that is under developed in the MPR context. The purpose of this article is to extend MPR models to handle multivariate survival data by introducing random effects in both the scale and the shape regression components. We consider a variety of possible dependence structures for these random effects (independent, shared and correlated), and estimation proceeds using a h-likelihood approach. The performance of our estimation procedure is investigated by a way of an extensive simulation study, and the merits of our modelling approach are illustrated through applications to two real data examples, a lung cancer dataset and a bladder cancer dataset. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) printing applied in architectural engineering is profoundly changing building technologies and construction processes. Indeed, the wide scope of metamaterials and components manufactured with 3D printing provides unprecedented adaptability and customizability in construction. One of the promising applications of such novel technology concerns metamaterial noise barriers that protect buildings from environmental noise. More specifically, sonic crystal noise barriers (SCNBs) are effective solutions with good sound insulation characteristics (with the advantage of letting air/light pass through). Conversely, the limitations of SCNB are related to their design and manufacturing (new complex design approaches are required to maximize the performance of such devices). To overcome such drawbacks, this research proposes a novel methodological approach to design and customize the topology of SCNBs made achievable with additive manufacturing. First, parametric modeling affords design flexibility and adaptability. Second, iterative performance simulations are set to adjust parameters considering target frequencies. Third, 3D printing allows the prototyping of customized barriers. The methodology is applied to the case of train brake noise. A novel SCNB is designed, simulated, and prototyped through 3D-printing technology. Finally, experimental validation is executed according to current international standards. [ABSTRACT FROM AUTHOR]

Computer modeling and simulation are essential for predicting architectural solutions performance. When integrated into the design process; they are known as performance-based design. During the design process, fast analytical cycles are required. However, daylighting simulations are challenging due to computational processing and time requirements. This paper presents a comparative study of fast daylight simulation methods, two-phase, three-phase and path tracing, in a hypothetical commercial building located in Miami, Florida. Five types of static glass and one electrochromic glass were tested with six parametric models of a translucent horizontal shading device. Daysim was used as reference. The two-phase method produced results similar to the reference results with faster processing times for static facades. For dynamic facades, the path tracing method with the tested interface enabled faster simulations and parametric variations. However, the three-phase method yielded a lower average error. [ABSTRACT FROM AUTHOR]

Count data are common in practice. Statistical process control for count data thus has attracted much attention in recent years. Most existing methods on this topic focus on the detection of mean shifts of count data based on parametric modeling. However, their assumed parametric models (e.g., the Poisson probability model) are often invalid in practice due mainly to the potential impact of some latent confounding risk factors, which would lead to unreliable performance of the related control charts. In addition, it is highly desirable and important to monitor the dispersion of count data when the Poisson probability model is invalid. To this end, new nonparametric cumulative sum control charts and their corresponding self‐starting versions are suggested in this paper for monitoring the dispersion of count data based on data categorization and categorical data analysis. Numerical results show that the proposed method can provide more effective and robust monitoring of count data in comparison with some representative existing methods. A real‐data example is used to demonstrate its implementation and application. [ABSTRACT FROM AUTHOR]

In many industries, the reliability of a product is often determined by a quantile of a distribution of a product's characteristics meeting a specified requirement. A typical approach to address this is to assume a parametric model and compute a one‐sided confidence bound on the quantile. However, this can become difficult if the sample size is too small to reliably estimate such a parametric model. Linear interpolation between order statistics is a viable nonparametric alternative if the sample size is sufficiently large. In most cases, linear extrapolation from the extreme order statistics can be used, but can result in inconsistent coverage. In this work, we perform an empirical study to generate robust weights for linear extrapolation that greatly improves the accuracy of the coverage across a feasible range of distribution families with positive support. Our method is applied to two industrial datasets. [ABSTRACT FROM AUTHOR]

Keeping in view the design of short to medium-span bridges in critical locations such as road/river crossings under the action of oppositely moving high-speed loads, this paper presents a hybrid model for the prediction of dynamic displacement employing a theoretical non-dimensional framework aided by an artificial neural network (ANN). The introduction of such a hybrid model reduces parametric space and time. The feed-forward ANN is implemented to train the comprehensive data set obtained from the non-dimensional theoretical scheme. The dimensionless input to ANN comprises the bridge and load parameters, whereas maximum dynamic displacement is the output parameter. The robustness and efficiency of the proposed surrogate ANN framework in terms of computational efficiency are compared with the conventional mode-superposition method formulated in dimensionless form. The training of test and validated data sets results in best-fit models with optimistic statistical metrics and accuracy ≤ 6%. Moreover, the sensitivity of different parameters to the displacement response is highlighted using Pearson's correlation and Quantile–Quantile plots. To incorporate the best-fit models into the design by bypassing the high-fidelity model, a user-friendly interface is developed, confirming the standards of the high-speed rating code (TZY2014-232 2014). Such interfaces have the potential for wide application in the preliminary design of short-to medium-span bridges under opposite moving loads in critical locations such as crossing valleys, rivers, and railroads. [ABSTRACT FROM AUTHOR]

Measurement of human faces is fundamental to many applications from recognition to genetic phenotyping. While anthropometric landmarks provide a conventional set of homologous measurement points, digital scans are increasingly used for facial measurement, despite the difficulties in establishing their homology. We introduce an alternative basis for facial measurement, which 1) provides a richer information density than discrete point measurements, 2) derives its homology from shared facial topography (ridges, folds, etc.), and 3) quantifies local morphological variation following the conventions and practices of anatomical description. A parametric model that permits matching a broad range of facial variation by the adjustment of 71 parameters is demonstrated by modeling a sample of 80 adult human faces. The surface of the parametric model can be adjusted to match each photogrammetric surface mesh generally to within 1 mm, demonstrating a novel and efficient means for facial shape encoding. We examine how well this scheme quantifies facial shape and variation with respect to geographic ancestry and sex. We compare this analysis with a more conventional, landmark-based geometric morphometric (GMM) study with 43 landmarks placed on the same set of scans. Our multivariate statistical analysis using the 71 attribute values separates geographic ancestry groups and sexes with a high degree of reliability, and these results are broadly similar to those from GMM, but with some key differences that we discuss. This approach is compared with conventional, non-parametric methods for the quantification of facial shape, including generality, information density, and the separation of size and shape. Potential uses for phenotypic and dysmorphology studies are also discussed. [ABSTRACT FROM AUTHOR]

Shearer cables, subjected to large deformations and exposed to harsh working conditions during frequent back-and-forth movements, pose difficulties in achieving comprehensive mechanical performance and extended fatigue life. This study addresses parametric modeling challenges related to determining tangency within and between layers, recursively generating spiral curves from the (n-1)-th level to the n-th level, and constructing irregular surfaces for insulation and sheath. Investigating tensile and bending properties, the research explores the impact of varying pitch diameter ratios at different stranding levels, stranding directions, and monofilaments on mechanical performance across scales. The results reveal a nonlinear increase in stress in power and control conductors with growing pitch diameter ratios. The optimal combination is determined as a pitch diameter ratio of 6 for cabling, 5 for the control conductor, and 14 for the power conductor. The predicted fatigue life of the improved cable by Ncode aligns with bending test results, demonstrating functionality up to 15.12e4 cycles. Stress distribution in parallel stranding is lower and more even, tending to scatter. Conversely, counter stranding experiences relatively higher stress, ensuring a more stable structure. While maintaining a constant effective conductor cross-sectional area, finer monofilaments result in higher cross-sectional filling ratios, enhancing tensile and bending performance. [ABSTRACT FROM AUTHOR]

Information dissemination has a significant impact on social development. This paper considers that there are many stochastic factors in the social system, which will result in the phenomena of information cross-dissemination and variation. The dual-system stochastic susceptible-infectious-mutant-recovered model of information cross-dissemination and variation is derived from this problem. Afterward, the existence of the global positive solution is demonstrated, sufficient conditions for the disappearance of information and its stationary distribution are calculated, and the optimal control strategy for the stochastic model is proposed. The numerical simulation supports the results of the theoretical analysis and is compared to the parameter variation of the deterministic model. The results demonstrate that cross-dissemination of information can result in information variation and diffusion. Meanwhile, white noise has a positive effect on information dissemination, which can be improved by adjusting the perturbation parameters. [ABSTRACT FROM AUTHOR]

This paper discusses regression analysis of interval‐censored failure time data arising from semiparametric transformation models in the presence of missing covariates. Although some methods have been developed for the problem, they either apply only to limited situations or may have some computational issues. Corresponding to these, we propose a new and unified two‐step inference procedure that can be easily implemented using the existing or standard software. The proposed method makes use of a set of working models to extract partial information from incomplete observations and yields a consistent estimator of regression parameters assuming missing at random. An extensive simulation study is conducted and indicates that it performs well in practical situations. Finally, we apply the proposed approach to an Alzheimer's Disease study that motivated this study. [ABSTRACT FROM AUTHOR]

The Mann–Whitney effect is a measure for comparing survival distributions between two groups. The Mann–Whitney effect is interpreted as the probability that a randomly selected subject in a group survives longer than a randomly selected subject in the other group. Under the independence assumption of two groups, the Mann–Whitney effect can be expressed as the traditional integral formula of survival functions. However, when the survival times in two groups are not independent of each other, the traditional formula of the Mann–Whitney effect has to be modified. In this article, we propose a copula-based approach to compute the Mann–Whitney effect with parametric survival models under dependence of two groups, which may arise in the potential outcome framework. In addition, we develop a Shiny web app that can implement the proposed method via simple commands. Through a simulation study, we show the correctness of the proposed calculator. We apply the proposed methods to two real datasets. [ABSTRACT FROM AUTHOR]

This study aims to develop a procedure for the production of 3D-printed forearm prostheses (especially hard outer sockets). The production procedure is designed in the form of a parametric workflow (CAD model), which significantly speeds up the designing process of the prosthesis. This procedure is not fixedly dependent on the software (SW) equipment and is fully transferable into another SW environment. The use of these prostheses will significantly increase the comfort of their patients' lives. It is possible to produce prostheses faster and in larger amounts and variants by the usage of additive technology. The input for the own production of the prosthesis is a model of the internal soft socket of the patient. This soft socket (soft bed) is made by a qualified prosthetist. A 3D-scanned CAD model is obtained afterward using the scanning method by an automatic laser projector. An editable, parametric external socket (modifiable in any CAD format) is generated from the obtained 3D scan using a special algorithmic model. This socket, after the necessary individual modifications, is transferred to 3D printing technology and produced using powder technology Multi Jet Fusion (HP MJF). The result of the designed and tested procedure is a quickly editable 3D-printed outer socket (main part of prosthesis), which is able to fully replace the current long-fiber composite solution. Production of current solutions is relatively time-consuming, and only one piece is produced in a given time. The newly designed technology eliminates this. This study summarized the possibilities of speeding up the production of forearm prostheses (but not only these) by creating a parametric CAD model that is applicable to different patients. [ABSTRACT FROM AUTHOR]

Background: Semiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients' survival analysis. Methods: Training (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic. Results: Although the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis. Conclusions: The Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival. Trial registration: ClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/, date of registration: 30th June 2017. [ABSTRACT FROM AUTHOR]

Entrepreneurship scholars interested in the performance of innovative start-ups mainly focus on the internal factors related to innovation processes. In the present paper, the research perspective used to study the performance of innovative start-ups is broadened by also considering the influence of the context – more specifically, the technological regime, defined as the state of the art of the technological environment resulting from technological changes/upgrades. Thus, the objective of this study is to investigate if and to what extent the technological regime can affect the performance of innovative start-ups. Statistical elaborations for about 11,760 Italian innovative start-ups (data are retrieved from the Italian Ministry of Economic Development website and are current up to January 2021) are carried out through the estimation of a stochastic frontier analysis: a parametric model. This methodology identifies the causes of inefficiency and disentangles random from technical inefficiency of the model. The results reveal that the technological regime affects performance but, unexpectedly, does not boost the impact of internal factors (related to innovation processes) on performance. The main conclusion is that internal innovation-related factors and the technological regime do not benefit each other when innovation processes are managed in innovative start-ups. [ABSTRACT FROM AUTHOR]

Latent class analysis (LCA) refers to techniques for identifying groups in data based on a parametric model. Examples include mixture models, LCA with ordinal indicators, and latent class growth analysis. Despite its popularity, there is limited guidance with respect to decisions that must be made when conducting and reporting LCA. Moreover, there is a lack of user-friendly open-source implementations. Based on contemporary academic discourse, this paper introduces recommendations for LCA which are summarized in the SMART-LCA checklist: Standards for More Accuracy in Reporting of different Types of Latent Class Analysis. The free open-source R-package package tidySEM implements the practices recommended here. It is easy for beginners to adopt thanks to user-friendly wrapper functions, and yet remains relevant for expert users as its models are integrated within the OpenMx structural equation modeling framework and remain fully customizable. The Appendices and tidySEM package vignettes include tutorial examples of common applications of LCA. [ABSTRACT FROM AUTHOR]

This research presents an experimental electric vehicle developed at the Tecnológico Nacional de México Celaya campus. It was decided to use a golf cart-type gasoline vehicle as a starting point. Initially, the body was removed, and the vehicle was electrified, meaning its engine was replaced with an electric one. Subsequently, sensors used to measure the vehicle states were placed, calibrated, and instrumented. Additionally, a mathematical model was developed along with a strategy for the parametric identification of this model. A communication scheme was implemented consisting of four slave devices responsible for controlling the accelerator, brake, steering wheel, and measuring the sensors related to odometry. The master device is responsible for communicating with the slaves, displaying information on a screen, creating a log, and implementing trajectory tracking techniques based on classical, geometric, and predictive control. Finally, the performance of the control algorithms implemented on the experimental prototype was compared in terms of tracking error and control input across three different types of trajectories: lane change, right-angle curve, and U-turn. [ABSTRACT FROM AUTHOR]

This work mainly investigates the asymptotic properties for the wavelet estimator in a non parametric regression model with martingale difference random errors. The moment consistency, strong consistency, complete consistency, the rate of strong consistency and the asymptotic normality for the wavelet estimator are established under some mild conditions, which were not obtained before. Some numerical analysis is carried out to support the theoretical results. [ABSTRACT FROM AUTHOR]

Designed clinical studies often assess outcomes at pre-planned time points. In most situations, standard statistical models, such as generalized linear mixed models and generalized additive models, are sufficient to depict the temporal trends of the outcome and produce valid inference. Complicating factors, however, do exist in practical data analyses. One complication arises when the outcome and observational processes are interdependent, that is, the observational process is informative; another challenge is patient characteristics may influence the longitudinally observed outcomes in non-additive ways, for example, by multiplicative factors. In this research, we extend the standard longitudinal models to accommodate informative observation through a more flexible modeling structure—one with additive-multiplicative components that do not require explicit specification of the dependency structure between the outcome and observation processes. Along this vein, we provide the essential theory for inference in such models. Simulation studies showed the proposed method performs well for finite-sample scenarios, and the method was applied to analyze a motivating example from an alcohol-associated hepatitis observational study. [ABSTRACT FROM AUTHOR]

Many non-fatal events can be considered recurrent in that they can occur repeatedly over time, and some researchers may be interested in the trajectory and relative risk of non-fatal events. With the competing risk of death, the treatment effect on the mean number of recurrent events is non-identifiable since the observed mean is a function of both the recurrent event and terminal event processes. In this paper, we assume independence between the non-fatal and the terminal event process, conditional on the shared frailty, to fit a parametric model that recovers the trajectory of, and identifies the effect of treatment on, the non-fatal event process in the presence of the competing risk of death. Simulation studies are conducted to verify the reliability of our estimators. We illustrate the method and perform model diagnostics using the Carvedilol Prospective Randomized Cumulative Survival trial which involves heart-failure events. [ABSTRACT FROM AUTHOR]

With the rapid popularization of smart devices, the applications and technologies of the Internet of Things (IoT) are in high demand worldwide, especially in improving development efficiency and ensuring system quality, reliability, and security. Formal methods have been successfully used to specify, verify, and analyze software and hardware systems, effectively alleviating the above problems in these systems. However, most of the existing works mainly focus on the practical applications of IoT, and there is a lack of research on modeling and analyzing IoT systems from the perspective of formal methods. In this paper, we first propose a secure mobile real‐time process calculus for specifying and reasoning about IoT systems, called SMrCaIT, which supports not only value‐passing communication but also name‐passing communication. In addition, this calculus can strictly separate process actions and mobility modeling by providing parametric mobility models. Subsequently, we present the operational semantics of this calculus, in particular, the rules on how to secure channel communication by closing the scope of a channel and handling scope extrusion. Taking vehicle ad hoc network (VANET) as a case, the application details of SMrCaIT and its operational semantics are fully demonstrated. By using the rewrite engine Real‐Time Maude, we further implement SMrCaIT and its operational semantics and verify some properties of the VANET case to indicate the effectiveness of our calculus in real‐world scenes. [ABSTRACT FROM AUTHOR]

The paper investigates the stiffness and strength of log walls with standard and dovetail bonded corners under lateral loads. The finite element model is developed and validated with experimental work at small-scale and wall-scale levels. The validated wall-scale model was modified by conducting a detailed investigation on assessing the wall behaviour with varying crucial parameters, including coefficient of friction, vertical load, geometric irregularities, and the wall's aspect ratio. The study suggested various techniques for improving the wall's lateral load resistance capacities and initial stiffness, employing steel and wood pins. [ABSTRACT FROM AUTHOR]

In topology optimization of the microchannel heat sink, the minimum width of flow channels are constrained by the mesh size. To reduce the constraint of finite element mesh on the optimized structure, this paper proposes a mesh adaptive strategy which consists of finer ground mesh and coarser adaptive mesh for the heat sink design. The nodes of adaptive mesh are selected from ground mesh based on the structure of current iteration. The physical fields are solved on adaptive mesh, and design variables are updated on ground mesh. Besides, hyperparameters are introduced to control the adaptive degree, and their influence on optimized results is analysed in detail. Numerical experiments with simplified two-layer thermal-fluid model and parametric level set method show that the developed method outperforms traditional uniform mesh and is able to save large amounts of computing resources. [ABSTRACT FROM AUTHOR]

Deep learning-based scan-to-building information models (BIMs) approaches have gained popularity in generating as-built BIMs for highway bridges. However, several critical challenges emerge. First, the acquisition of large-scale training data is prohibitively expensive. Second, the complex geometry of bridges, such as variable curvature and cross sections, poses difficulties for modeling. Lastly, these generated models often lack a parametric definition. To address these challenges, this paper introduces an enhanced scan-to-BIM framework that uses low-cost synthetic point clouds and parametric modeling. This approach contains two main components: (1) semantic segmentation using augmented datasets, and (2) a projection-based parametric modeling method. Through rigorous experiments, it is evident that augmenting the training process with synthetic data significantly improves model performance, yielding up to a 12.2% segmentation improvement in this work. In terms of modeling, the reconstructed model showed a marginal mean difference of 0.06 m against the ground truth. Notably, when applied to real-world bridges, the framework demonstrated a comparable accuracy level, with deviations primarily stemming from occlusions in the bridge abutments. In conclusion, this study highlights the effectiveness of the proposed framework in creating as-built BIM models for highway bridges. Additionally, it emphasizes the significance of combining synthetic and real data for optimal accuracy and proves its potential in modern highway infrastructure applications. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) has emerged as the most favorable source for recognizing brain disorders like epileptic seizure (ES) using deep learning (DL) methods. This study investigated the well-performed EEG-based ES detection method by decomposing EEG signals. Specifically, empirical mode decomposition (EMD) decomposes EEG signals into six intrinsic mode functions (IMFs). Three distinct features, namely, fluctuation index, variance, and ellipse area of the second order difference plot (SODP), were extracted from each of the IMFs. The feature values from all EEG channels were arranged in two composite feature forms: a 1D (i.e., unidimensional) form and a 2D image-like form. For ES recognition, the convolutional neural network (CNN), the most prominent DL model for 2D input, was considered for the 2D feature form, and a 1D version of CNN was employed for the 1D feature form. The experiment was conducted on a benchmark CHB-MIT dataset as well as a dataset prepared from the EEG signals of ES patients from Prince Hospital Khulna (PHK), Bangladesh. The 2D feature-based CNN model outperformed the other 1D feature-based models, showing an accuracy of 99.78% for CHB-MIT and 95.26% for PHK. Furthermore, the cross-dataset evaluations also showed favorable outcomes. Therefore, the proposed method with 2D composite feature form can be a promising ES detection method. [ABSTRACT FROM AUTHOR]

In the context of digitalizing cultural heritage, HBIM is garnering increasing attention from heritage conservationists for its advantages. In conservation practices for cultural heritage, expressing regional characteristics and documenting in HBIM has become increasingly crucial, and these needs are inextricably linked to the need for models with a high level of detail (LOD). This paper presents a combination of Terrestrial Laser Scanning surveys, UAV photogrammetry, and handheld laser scanning to scan cultural heritage. The scan data obtained from multiple digital surveys serves as the foundation for modelling HBIM. For the regional characteristics, the HBIM model was built with detailed geometric shapes, configurations, materials, and colors of each element, as well as complex patterns and damage using the parametric models and mesh models. The information of the building and its components stored in the HBIM model. Consequently, obtained a highly detailed HBIM that not only captures the intricate details of the ideal state and real-world damage and degradation with minimal error but also serves as a repository for a multitude of information types. It has been concluded that a high LOD is essential to representing the regional geometric feature of a building and recording the damage of cultural heritage. [ABSTRACT FROM AUTHOR]

The asymptotic properties of nonparametric kernel estimators of diffusion process and integrated diffusion process were studied by scholars through using the theories of local time, giving the properties of consistency and asymptotic normality for nonparametric kernel estimators under appropriate conditions, but not property of strong consistency for integrated diffusion process. Instead of using the local time method, the paper applies the moment inequality of the ρ-mixing sequence to prove the strong consistency of the nonparametric kernel estimators in the integrated diffusion process. Our theorem conditions are mild and canonical, and some of them improve on the existing corresponding conditions. In numerical simulations and analysis of data from real applications, the nonparametric kernel estimators can capture well the variation characteristics of drift coefficient and diffusion coefficient, and that it is possible to fit parametric models with such characteristics, so that the economic interpretation of the models can be obtained. [ABSTRACT FROM AUTHOR]

Researchers who use measurement models for evaluation purposes often select models with stringent requirements, such as Rasch models, which are parametric. Mokken Scale Analysis (MSA) offers a theory-driven nonparametric modeling approach that may be more appropriate for some measurement applications. Researchers have discussed using MSA as a preliminary procedure with which to evaluate data quality before applying a parametric model. However, the literature includes only a few examples in which researchers have integrated MSA techniques with parametric models throughout the analytic procedure. We consider a systematic approach for integrating results from nonparametric MSA techniques with parametric measurement models to evaluate measurement quality and construct scales with useful measurement properties. We use real-data illustrations and a simulation study to demonstrate and systematically explore our approach. We discuss implications for research and practice. [ABSTRACT FROM AUTHOR]

In this paper we propose a new nonparametric regression technique under multiplicative distortion measurement errors settings. The unobservable variables are both distorted in a multiplicative fashion by an observed confounding variable. A bias reduction is proposed by choosing a function that is the projection of the unknown regression function onto the parametric family in a certain metric. We find that this new technique leads to substantial improvement in the performance of regression estimators in comparison with the direct one-step estimation, irrespective of the choice of a parametric model. We obtain asymptotic normality results for the estimated nonparametric kernel smoothers, and further discuss their estimation efficiency. We conduct Monte Carlo simulation experiments to examine the performance of the proposed estimators. [ABSTRACT FROM AUTHOR]

Enhancing the through-flow capability of the turbine facilitates the potential to decrease the external dimension of the engine, leading to a reduction in weight and an increase in the thrust-to-weight ratio. The optimization of blade profile in terms of aerodynamic design is an essential strategy to improve the overall performance of the high through-flow design turbine. This study aims to develop an automated optimization technique, utilizing the response surface method (RSM), to enable reliable prediction and rapid optimization of aerodynamic performance. A parametric modeling method is devised to generate blade profiles with continuous curvature throughout the automated optimization process. The Box–Behnken experimental design, in combination with Reynolds-averaged Navier–Stokes numerical calculation, is employed to construct a second-order polynomial RSM approximation model. The optimization process comprises two levels: improving the through-flow capability and reducing blade profile loss. The optimized profile, Opt1, achieves a substantial 13.19% improvement in through-flow capability at the cost of a 12.27% increase in blade profile loss. Thus, further optimization is performed to minimize blade profile loss based on the Opt1 scheme. Geometric constraints are applied to the most influential parameters affecting through-flow capability to mitigate their impacts. Compared to the Opt1, the final optimized profile, Opt2, achieves a substantial 31.83% decrease in blade profile loss with a negligible sacrifice of 0.23% in through-flow capability. [ABSTRACT FROM AUTHOR]

This article discusses the problem of generating internal waves during explosive eruptions of underwater volcanoes. The importance and novelty of such studies are associated with the emerging data on the observation of internal waves during a volcanic eruption in the Tonga Archipelago in 2022 reported in Zhang and Li ["Oceanic internal waves generated by the Tongan volcano eruption," Acta Oceanol. Sin. 41(8), 1–4 (2022)]. Using Le Méhaute's parametric model originally suggested to calculate surface waves, internal waves in the ocean with a constant buoyancy frequency are calculated. Internal waves are trains of different modes, the parameters of which were found for different ratios between the radius of the equivalent source and the depth of the basin. Even at relatively small distances from the source (tens of km), wave amplitudes changeover time periods consisting of several days, which confirms the possibility of delineating the tsunami source by internal waves. [ABSTRACT FROM AUTHOR]

We consider an exchangeably weighted bootstrap for function-valued estimators defined as a zero point of a function-valued random criterion function. A large number of bootstrap resampling schemes emerge as special cases of our settings. The main ingredient is the use of a differential identity that applies when the random criterion function is linear in terms of the empirical measure. Our results are general and do not require linearity of the statistical model in terms of the unknown parameter. We also consider the semiparametric models extending Zhan's work to a more delicate framework. The theoretical results established in this paper are (or will be) key tools for further developments in the parametric and semiparametric models. [ABSTRACT FROM AUTHOR]

In this paper, a non-intrusive reduced-order model (ROM) for parametric reactor kinetics simulations is presented. Time-dependent ROMs are notoriously data intensive and difficult to implement when nonlinear multiphysics phenomena are considered. These challenges are exacerbated when parametric dependencies are included. The proper orthogonal decomposition mode coefficient interpolation (POD-MCI) ROM presented in this work can be constructed directly from lower-dimensional full-order model (FOM) outputs and is independent of the underlying model. This greatly alleviates the data requirement of many existing ROMs and can be used without modification on arbitrarily complex models or experimental data. The POD-MCI ROM is demonstrated on a number of examples and yields accurate characterizations of the parametric behaviors of both FOM outputs and derived quantities of interest within the selected parameter spaces, at extremely attractive computational speedup factors relative to FOMs. [ABSTRACT FROM AUTHOR]

An optimisation-based calibration technique, using the area metric, is applied to determine the input parameters of a stochastic earthquake-waveform simulation method. The calibration algorithm updates a model prior, specifically an estimate of a region's seismological (source, path and site) parameters, typically developed using waveform data, or existing models, from a wide range of sources. In the absence of calibration, this can result in overestimates of a target region's ground motion variability, and in some cases, introduce biases. The proposed method simultaneously attains optimum estimates of median, range and distribution (uncertainty) of these seismological parameters, and resultant ground motions, for a specific target region, through calibration of physically constrained parametric models to local ground motion data. We apply the method to Italy, a region of moderate seismicity, using response spectra recorded in the European Strong Motion (ESM) dataset. As a prior, we utilise independent seismological models developed using strong motion data across a wider European context. The calibration obtains values of each seismological parameter considered (such as, but not limited to, quality factor, geometrical spreading and stress drop), to develop a suite of optimal parameters for locally adjusted stochastic ground motion simulation. We consider both the epistemic and aleatory variability associated with the calibration process. We were able to reduce the area metric (misfit) value by up to 88% for the simulations using updated parameters, compared to the initial prior. This framework for the calibration and updating of seismological models can help achieve robust and transparent regionally adjusted estimates of ground motion, and to consider epistemic uncertainty through correlated parameters. [ABSTRACT FROM AUTHOR]

Knowledge of recruitment to a fishery is particularly important in short-lived species, like most cephalopods, in which there is a complete turnover of individual every 1 or 2 years. In this study, age, body length, statolith length and growth rates of loliginid paralarvae (Alloteuthis media, A. subulata and Loligo vulgaris) were determined to evaluate the form of the growth curve and differences between species and hatching seasons. A total of 222 paralarvae collected in zooplankton samples along the Galician coast (NW Spain) were used to determine relationships between dorsal mantle length (DML), statolith length (SL) and the number of increments (NI)) deposited in the statolith. The paralarvae of L. vulgaris were between 1 and 35 days old, while both Alloteuthis species ranged in age from 1 to 54 days. Generalised additive models (GAMs) revealed non-linear growth in DML, with L. vulgaris exhibiting higher DML-at-age than Alloteuthis species. The best parametric model fit for these data was obtained using exponential regressions. The SL–NI relationship also differed between species, with L. vulgaris having larger statolith length at age. The paralarvae of L. vulgaris and A. media had two well-differentiated hatching peaks, one in late spring-early summer (beginning of the upwelling season) and another peak in late autumn (end of the upwelling season). Alloteuthis subulata had an earlier hatching peak in spring than in the other two loliginid species—and the second peak in autumn was barely visible, possibly because this species occurs further north in areas not sampled in autumn. [ABSTRACT FROM AUTHOR]

The primary cause of embankment dam failure is the overtopping of the crest, making dam safety a crucial consideration due to the potential for catastrophic consequences. Dam safety regulations and guidelines are increasingly demanding the enhancement of rockfill dams' resistance to overtopping and leakages. Assessing the breaching of embankments is essential for conducting risk evaluations and hazard studies. Typically, this assessment is carried out using parametric breach models, which are statistically derived equations based on historical dam failure cases. However, these models often overlook important parameters such as material properties, leading to uncertainties in the results. This research focuses on quantifying the accuracy of breach parameters (breach width, failure time, peak outflow) predicted by various parametric breach models found in the literature. Three prototype embankment dams (homogeneous earthfill dam, homogeneous rockfill dam, and zoned rockfill dam), along with five historical failure cases, are considered to evaluate the predictive capability of the parametric breach models. The study compares the measured and estimated breaching parameter values, aiming to identify the most suitable parametric breach model based on the dam design. The results indicate that the performance of the different equations in estimating breach parameters significantly depends on the characteristics of the dam and reservoir. Additionally, a simplified physical model (DLBreach) is employed to estimate breach parameters for the three prototype dams, and a comparison with results from parametric breach models is presented. In conclusion, accurately predicting breach parameters is vital for improving dam safety. This research highlights the influence of dam and reservoir characteristics on the performance of different parametric breach models. By identifying the most suitable breach models for specific dam designs, this study contributes to the understanding and management of embankment dam failures. [ABSTRACT FROM AUTHOR]

Purpose: This paper delves into the aerodynamic optimization of a single-stage axial turbine employed in aero-engines. Design/methodology/approach: An efficient integrated design optimization approach tailored for turbine blade profiles is proposed. The approach combines a novel hierarchical dynamic switching PSO (HDSPSO) algorithm with a parametric modeling technique of turbine blades and high-fidelity Computational Fluid Dynamics (CFD) simulation analysis. The proposed HDSPSO algorithm introduces significant enhancements to the original PSO in three pivotal aspects: adaptive acceleration coefficients, distance-based dynamic neighborhood, and a switchable learning mechanism. The core idea behind these improvements is to incorporate the evolutionary state, strengthen interactions within the swarm, enrich update strategies for particles, and effectively prevent premature convergence while enhancing global search capability. Findings: Mathematical experiments are conducted to compare the performance of HDSPSO with three other representative PSO variants. The results demonstrate that HDSPSO is a competitive intelligent algorithm with significant global search capabilities and rapid convergence speed. Subsequently, the HDSPSO-based integrated design optimization approach is applied to optimize the turbine blade profiles. The optimized turbine blades have a more uniform thickness distribution, an enhanced loading distribution, and a better flow condition. Importantly, these optimizations lead to a remarkable improvement in aerodynamic performance under both design and non-design working conditions. Originality/value: These findings highlight the effectiveness and advancement of the HDSPSO-based integrated design optimization approach for turbine blade profiles in enhancing the overall aerodynamic performance. Furthermore, it confirms the great prospects of the innovative HDSPSO algorithm in tackling challenging tasks in practical engineering applications. [ABSTRACT FROM AUTHOR]

Ultrasound (US) is widely used for evaluating various orbital conditions. However, accurately diagnosing malignant orbital masses using US remains challenging. We aimed to develop an ultrasonic feature-based model to predict the presence of malignant tumors in the orbit. A total of 510 patients with orbital masses were enrolled between January 2017 and April 2023. They were divided into a development cohort and a validation cohort. In the development cohort (n = 408), the ultrasonic and clinical features with differential values were identified. Based on these features, a predictive model and nomogram were constructed. The diagnostic performance of the model was compared with that of MRI or observers, and further validated in the validation cohort (n = 102). The involvement of more than two quadrants, irregular shape, extremely low echo of the solid part, presence of echogenic foci, cast-like appearance, and two demographic characteristics (age and sex) were identified as independent features related to malignant tumors of the orbit. The predictive model constructed based on these features exhibited better performance in identifying malignant tumors compared to MRI (AUC = 0.78 [95% CI: 0.73, 0.82] vs. 0.69 [95% CI: 0.64, 0.74], p = 0.03) and observers (AUC = 0.93 [95% CI: 0.90, 0.95] vs. Observer 1, AUC = 0.80 [95% CI: 0.76, 0.84], p < 0.01; vs. Observer 2, AUC = 0.71 [95% CI: 0.66, 0.76], p < 0.01). In the validation cohort, the predictive model achieved an AUC of 0.88 (95% CI: 0.81, 0.94). The ultrasonic-clinical feature-based predictive model can accurately identify malignant orbital tumors, offering a convenient approach in clinical practice. [ABSTRACT FROM AUTHOR]

• Numerically investigate folding pattern and rigid-flexible coupled dynamics of satellite-sphere system. • Achieve a larger volume folding rate by pattern of inflatable sphere with orderly deployment. • Inflating process has great influence on satellite based on rigid-flexible coupled dynamic model. • Reduce influence by heavier satellite, small inflation rate and appropriate control torque. Inflatable spheres can be applied to the aerodynamic drag deorbiting device for satellites. It may bring about a complicated dynamic problem under the disturbance from the lightweight large-volume inflatable sphere to the satellite. In order to explore this problem, rigid-flexible coupled dynamics behaviors of satellite-sphere system during inflating process are numerically studied in this paper. The folding pattern of inflatable sphere is designed to obtain the parametric model in the folded state. The dynamic analysis model of this system is established to analyze dynamic behaviors, including the dynamics of the satellite-sphere system, the gas state of the airbag and the disturbance to the satellite, during the inflating process. Parameter effects on the attitude disturbance are studied. The results show that the satellite mass, inflating mass rate and control torque have a great impact on the attitude disturbance. These works provide theoretical bases and technical supports for the engineering development of inflatable sphere deorbiting devices. [ABSTRACT FROM AUTHOR]

Introduction: Living in poverty, especially in low-income countries, are more affected by cardiovascular disease. Unlike the developed countries, it remains a significant cause of preventable heart disease in the Sub-Saharan region, including Ethiopia. According to the Ethiopian Ministry of Health statement, around 40,000 cardiac patients have been waiting for surgery in Ethiopia since September 2020. There is insufficient information about long-term cardiac patients' post-survival after cardiac surgery in Ethiopia. Therefore, the main objective of the current study was to determine the long-term post-cardiac surgery patients' survival status in Ethiopia. Methods: All patients attended from 2012 to 2023 throughout the country were included in the current study. The total number of participants was 1520 heart disease patients. The data collection procedure was conducted from February 2022- January 2023. Machine learning algorithms were applied. Gompertz regression was used also for the multivariable analysis report. Results: From possible machine learning models, random survival forest were preferred. It emphasizes, the most important variable for clinical prediction was SPO2, Age, time to surgery waiting time, and creatinine value and it accounts, 42.55%, 25.17%,11.82%, and 12.19% respectively. From the Gompertz regression, lower saturated oxygen, higher age, lower ejection fraction, short period of cardiac center stays after surgery, prolonged waiting time to surgery, and creating value were statistically significant predictors of death outcome for post-cardiac surgery patients' survival in Ethiopia. Conclusion: Some of the risk factors for the death of post-cardiac surgery patients are identified in the current investigation. Particular attention should be given to patients with prolonged waiting times and aged patients. Since there were only two fully active cardiac centers in Ethiopia it is far from an adequate number of centers for more than 120 million population, therefore, the study highly recommended to increase the number of cardiac centers that serve as cardiac surgery in Ethiopia. [ABSTRACT FROM AUTHOR]

Fascinating 3D shapes arise when a thin planar sheet is folded without stretching, tearing or cutting. The elegance amplifies when the fold/crease is changed from a straight line to a curve, due to the association of plastic deformation via folding and elastic deformation via bending. This results in the curved crease working as a hinge support providing deployability to the surface which is of significant interest in industrial engineering and architectural design. Consequently, finding a stable form of curved crease becomes pivotal in the development of deployable structures. This work proposes a novel way to evaluate such curves by taking inspiration from biomimicry. For this purpose, growth mechanism in plants was observed and an analogous model was developed to create a discrete curve of fold. A parametric model was developed for digital construction of the folded models. Test cases were formulated to compare the behavior of different folded models under various loading conditions. A simplified way to visualize the obtained results is proposed using visual programming tools. The models were further translated into physical prototypes with the aid of 3D printing, hybrid and cured-composite systems, where different mechanisms were adopted to achieve the folds. The prototypes were further tested under constrained boundary and compressive loading conditions, with results validating the analytical model. [ABSTRACT FROM AUTHOR]

Regression modeling is the workhorse of statistics and there is a vast literature on estimation of the regression function. It has been realized in recent years that in regression analysis the ultimate aim may be the estimation of a level set of the regression function, ie, the set of covariate values for which the regression function exceeds a predefined level, instead of the estimation of the regression function itself. The published work on estimation of the level set has thus far focused mainly on nonparametric regression, especially on point estimation. In this article, the construction of confidence sets for the level set of linear regression is considered. In particular, 1−α$$ 1-\alpha $$ level upper, lower and two‐sided confidence sets are constructed for the normal‐error linear regression. It is shown that these confidence sets can be easily constructed from the corresponding 1−α$$ 1-\alpha $$ level simultaneous confidence bands. It is also pointed out that the construction method is readily applicable to other parametric regression models where the mean response depends on a linear predictor through a monotonic link function, which include generalized linear models, linear mixed models and generalized linear mixed models. Therefore, the method proposed in this article is widely applicable. Simulation studies with both linear and generalized linear models are conducted to assess the method and real examples are used to illustrate the method. [ABSTRACT FROM AUTHOR]