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The aim of the present article is to model and predict the financial failure of non-financial companies listed on the Bucharest Stock Exchange. The prediction models are based on the companies’ financial reports. The paper emphasizes the importance of processing outlier data and the significance of categorical independent variables. The paper contributes to bankruptcy and corporate financial failure research by presenting a Romanian situation. Results show that the model that uses 3-year financial data prior to the failure has a better accuracy. Several models have been compared, and it was found that using categorical independent variables as explanatory variables increased the accuracy of the models against those that used numerical regressors.

Recent hardware developments within industrial digitalization make more evident the need of high‐fidelity models to exploit process system engineering potential in biomanufacturing. Hybrid models, which combine first‐principles (white‐box) and data‐driven (black‐box) approaches, aim to leverage the strengths of both methodologies to mitigate their individual limitations to provide high predictive power. This article explores the integration of hybrid modeling techniques in bioprocess engineering, emphasizing their potential to facilitate product development, accelerate experimental design, enhance process monitoring, drive optimization and catalyze the implementation of digitalization strategies to achieve a more robust and sustainable production in the framework of the goals set by the UN 2030 agenda. In this review, we analyze 270 publications from the early 1990s to 2024, extracting application domains, hybrid‐model architectures, implementation software, and nonparametric output structures. Our findings highlight marked improvements in predictive accuracy, and extrapolation capabilities while revealing persistent challenges in integrating mechanistic and empirical components. This review on hybrid modeling implementations shows their potential to enhance accuracy, calibration, and extrapolation capabilities, highly required for bioprocesses. This work underscores the importance of integrating mechanistic and data‐driven approaches to improve the flexibility and interpretability of models in complex engineering systems, offering significant implications for more sustainable and innovative bioprocesses. This review analyzes 270 studies on hybrid semi‐parametric modeling in bioprocess engineering, highlighting dominant architectures, applications, and tools. It emphasizes the potential of hybrid models and process‐systems engineering to improve accuracy, extrapolation, and sustainability, aligning bioprocess development with key UN Sustainable Development Goals.

Permanent-magnet linear motors (PMLMs) are widely used in various fields of industrial production, and the optimization design of the PMLM is increasingly attracting attention in order to improve the comprehensive performance of the motor. The primary problem of PMLM optimization design is the establishment of a motor model, and this paper summarizes the modeling of the PMLM electromagnetic field. First, PMLM parametric modeling methods (model-driven methods) such as the equivalent circuit method, analytical method, and finite element method, are introduced, and then non-parametric modeling methods (data-driven methods) such as the surrogate model and machine learning are introduced. Non-parametric modeling methods have the characteristics of higher accuracy and faster computation, and are the mainstream approach to motor modeling at present. However, surrogate models and traditional machine learning models such as support vector machine (SVM) and extreme learning machine (ELM) approaches have shortcomings in dealing with the high-dimensional data of motors, and some machine learning methods such as random forest (RF) require a large number of samples to obtain better modeling accuracy. Considering the modeling problem in the case of the high-dimensional electromagnetic field of the motor under the condition of a limited number of samples, this paper introduces the generative adversarial network (GAN) model and the application of the GAN in the electromagnetic field modeling of PMLM, and compares it with the mainstream machine learning models. Finally, the development of motor modeling that combines model-driven and data-driven methods is proposed.

This study presents the design and fabrication of a parametric physical model using additive manufacturing to visualize the double orthogonal projection of a line, enhancing engineering education through tangible geometric representation. The model consists of two articulated plates representing the principal projection planes and flexible cylindrical elements depicting a spatial line and its projections. Designed in SolidEdge, all parts were fabricated with FDM technology using a Creality Ender 3 printer and PLA filament. The plates include three articulation points enabling simulation of perpendicular and aligned positions. Flexible cylinders inserted into aligned holes create a kinematic mechanism that demonstrates the transformation from 3D line to 2D projections. A spotlight enhances spatial interpretation by marking projection points. Process parameters: layer height 0.2 mm, nozzle 0.4 mm, temperature 190–210°C, and speed 50–60 mm/s, ensured dimensional accuracy and a ±0.1 mm tolerance. The model proved stable, repeatable, and effective as a dynamic teaching tool in descriptive geometry and STEM (Science, Technology, Engineering, and Mathematics) education.

After the economic crisis and the BASEL agreement, the bankruptcy prediction research has evolved substantially due to its importance in corporate finance. This paper summarizes the short history of bankruptcy prediction from the beginning until quite recently. First, it presents a short summary of bankruptcy prediction evolution pointing to the most used models. Then, it provides a summary of the most cited papers that discuss the evolution of bankruptcy prediction and of those papers that have contributed to bankruptcy prediction. Finally, it summarizes some critiques about bankruptcy prediction that the literature has formulated over time and provides some suggestions for future research on bankruptcy prediction.

We analyse data from a study involving 173 pregnant women. The data are observed values of the β human chorionic gonadotropin hormone measured during the first 80 days of gestational age, including from one up to six longitudinal responses for each woman. The main objective in this study is to predict normal versus abnormal pregnancy outcomes from data that are available at the early stages of pregnancy. We achieve the desired classification with a semiparametric hierarchical model. Specifically, we consider a Dirichlet process mixture prior for the distribution of the random effects in each group. The unknown random-effects distributions are allowed to vary across groups but are made dependent by using a design vector to select different features of a single underlying random probability measure. The resulting model is an extension of the dependent Dirichlet process model, with an additional probability model for group classification. The model is shown to perform better than an alternative model which is based on independent Dirichlet processes for the groups. Relevant posterior distributions are summarized by using Markov chain Monte Carlo methods.

Building Information Modelling (BIM) is a globally adapted methodology by government organisations and builders who conceive the integration of the organisation, planning, development and the digital construction model into a single project. In the case of a heritage building, the Historic Building Information Modelling (HBIM) approach is able to cover the comprehensive restoration of the building. In contrast to BIM applied to new buildings, HBIM can address different models which represent either periods of historical interpretation, restoration phases or records of heritage assets over time. Great efforts are currently being made to automatically reconstitute the geometry of cultural heritage elements from data acquisition techniques such as Terrestrial Laser Scanning (TLS) or Structure From Motion (SfM) into BIM (Scan-to-BIM). Hence, this work advances on the parametric modelling from remote sensing point cloud data, which is carried out under the Rhino+Grasshopper-ArchiCAD combination. This workflow enables the automatic conversion of TLS and SFM point cloud data into textured 3D meshes and thus BIM objects to be included in the HBIM project. The accuracy assessment of this workflow yields a standard deviation value of 68.28 pixels, which is lower than other author’s precision but suffices for the automatic HBIM of the case study in this research.

Current research applies an energy-based design model to improve performance in existing modern buildings, in Rio de Janeiro, from the 1940’s, improving these buildings’ shading systems. This article proposes a methodology tested through a case study, the Nova Cintra building. The methodology starts by analysing the original shading system performance, regarding insolation, illuminance and air temperature. Using these results, proposes two computacional methods to improve performance: (1) a combinatorial modelling process, recombining the existing shading systems positions in the building’s north façade; and (2) a transformation process, using parametric and algorithmic–parametric modelling, to improve the existing shading systems performance. Both processes use optimization algorithms. The results of these modelling and optimization methods are compared with the results of the original system and suggests an improvement between 111.1% and 590.4% for insolation; between 360.9% and 84.4% for illuminance; and between 2.9% and 3.0% for air temperature, considering winter and summer solstices. This improvement aims at reducing the buildings’ energy consumption and foresees the production of renewable energy from solar harvesting, to mitigate climate change.

A synthetic review of the application of multi-objective optimization models to the design of climate-responsive buildings and neighbourhoods is carried out. The review focused on the software utilized during both simulation and optimization stages, as well as on the objective functions and the design variables. The hereby work aims at identifying knowledge gaps and future trends in the research field of automation in the design of buildings. Around 140 scientific journal articles, published between 2014 and 2021, were selected from Scopus and Web of Science databases. A three-step selection process was applied to refine the search terms and to discard works investigating mechanical, structural, and seismic topics. Meta-analysis of the results highlighted that multi-objective optimization models are widely exploited for (i) enhancing building’s energy efficiency, (ii) improving thermal and (iii) visual comfort, minimizing (iv) life-cycle costs, and (v) emissions. Reviewed workflows demonstrated to be suitable for exploring different design alternatives for building envelope, systems layout, and occupancy patterns. Nonetheless, there are still some aspects that need to be further enhanced to fully enable their potential such as the ability to operate at multiple temporal and spatial scales and the possibility of exploring strategies based on sector coupling to improve a building’s energy efficiency.

Modelling and computation for multivariate longitudinal surveys have proven challenging, particularly when data are not all continuous and Gaussian but contain discrete measurements. In many social science surveys, study participants are selected via complex survey designs such as stratified random sampling, leading to discrepancies between the sample and population, which are further compounded by missing data and loss to follow-up. Survey weights are typically constructed to address these issues, but it is not clear how to include them in models. Motivated by data on sexual development, we propose a novel non-parametric approach for mixed scale longitudinal data in surveys. In the approach proposed, the mixed scale multivariate response is expressed through an underlying continuous variable with dynamic latent factors inducing time varying associations. Bias from the survey design is adjusted for in posterior computation relying on a Markov chain Monte Carlo algorithm. The approach is assessed in simulation studies and applied to the National Longitudinal Study of Adolescent to Adult Health.