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The purpose of the paper is to obtain insight into, and provide practical advice for, event‐based conceptual modeling.

The paper analyzes a set of event concepts and uses the results to formulate a conceptual event model that is used to identify guidelines for creation of dynamic process models and static information models.

The paper characterizes events as short‐duration processes that have participants, consequences, and properties, and that may be modeled in terms of information structures. The conceptual event model is used to characterize a variety of event concepts and it is used to illustrate how events can be used to integrate dynamic modeling of processes and static modeling of information structures.

The results are unique in the sense that no other general event concept has been used to unify a similar broad variety of seemingly incompatible event concepts. The general event concept can be used to improve dynamic and static modeling.

The purpose of this paper is to present research in the area of the modeling of complex systems using feed‐forward neural network.

Applications of multilayer neural networks with supervisor learning on the own simulator program wrote in Borland^® Pascal Language. Series‐parallel identification method is applied. Tapped delay lines (TDL) in static neural networks for modeling of dynamic plants are used. Gradient and heuristic learning algorithms are applied. Three kinds of calibration of learning and testing data are used.

This paper illustrates that feed‐forward multilayer neural networks can model complex systems. Feed‐forward multilayer neural networks with TDL can be used to build global dynamic models of complex systems. It is possible to compare the quality both models.

The learning and testing data from real systems to tune neuronal models require use of calibrating these data to range 0‐1.

The models quality depends on kind of calibration learning data from real system and depends on kind of learning algorithms.

The method and the learning algorithms discussed in the paper can be used to create global models of complex systems. The multilayer neural network with TDL can be used to model complex dynamic systems with low dynamics.

Farmer exchange rate (FER) is the ratio between a farmer's income and expenditure and is also an indicator of farmers’ welfare. There is little research regarding its use in risk modeling in crop insurance. This study seeks to propose a design for a household margin insurance scheme of the agricultural sector based on FER.

This research employs various risk modeling concepts, i.e. value at risk, loss models and premium calculation, to construct the proposed model. The standard linear, static and time-varying copula models are used to identify the dependency between variables involved in calculating FER.

First, FER can be considered as the primary variable for risk modeling in agricultural household margin insurance because it demonstrates farmers’ financial ability. Second, temporal dependence estimated using the time-varying copula can minimize errors, reduce the premium rate and result in a tighter guarantee's level of security.

This research extends the previous similar studies related to the use of index ratio in margin insurance loss modeling. Its authenticity is in the use of FER, which represents the farmers' trading capability. FER determines farmers’ losses by considering two aspects: the farmers’ income rate and their ability to fulfill their life and farming needs. Also, originality exists in the use of the time-varying copulas in identifying the dependence of the indices involved in calculating FER.

This study aims to analyze Kickstarter data along with social media data from a data mining perspective. Kickstarter is a crowdfunding financing plataform and is a form of fundraising and is increasingly being adopted as a source for achieving the viability of projects. Despite its importance and adoption growth, the success rate of crowdfunding campaigns was 47% in 2017, and it has decreased over the years. A way of increasing the chances of success of campaigns would be to predict, by using machine learning techniques, if a campaign would be successful. By applying classification models, it is possible to estimate if whether or not a campaign will achieve success, and by applying regression models, the authors can forecast the amount of money to be funded.

The authors propose a solution in two phases, namely, launching and campaigning. As a result, models better suited for each point in time of a campaign life cycle.

The authors produced a static predictor capable of classifying the campaigns with an accuracy of 71%. The regression method for phase one achieved a 6.45 of root mean squared error. The dynamic classifier was able to achieve 85% of accuracy before 10% of campaign duration, the equivalent of 3 days, given a campaign with 30 days of length. At this same period time, it was able to achieve a forecasting performance of 2.5 of root mean squared error.

The authors carry out this research presenting the results with a set of real data from a crowdfunding platform. The results are discussed according to the existing literature. This provides a comprehensive review, detailing important research instructions for advancing this field of literature.

Continuum robots modeling, be it from a hard or soft class, is giving rise to several challenges compared with rigid robots. These challenges are mainly due to kinematic redundancy, dynamic nonlinearity and high flexibility. This paper aims initially at designing a hard class of continuum robots, namely, cable-driven continuum robot (CDCR) and equally at developing their kinematic and dynamic models.

First, the CDCR prototype is constructed, and its description is made. Second, kinematic models are established based on the constant curvature assumption and inextensible bending section. Third, by using the Lagrange method, the dynamic model is derived under some simplifications and based on the kinematic equations, in which the flexible backbone’s elasticity modulus was identified experimentally. Finally, the static model of the CDCR is also derived based on the dynamic model.

Numerical examples are carried out using Matlab software to verify the static and dynamic models. Moreover, the static model is validated by comparing the simulation’s results to the real measurements that have been provided with satisfactory results.

To reduce the complexity of the dynamic model’s expressions and avoid the numerical singularity when the bending angle is close to zero, some simplifications have been taken, especially for the kinetic energy terms, by using the nonlinear functions approximation. Hence, the main advantage of this analytical-approximate solution is that it can be applied in the bending angle that ranges up to 2p with reasonable errors, unlike the previously proposed techniques. Furthermore, the resulting dynamic model has, to some extent, the proprieties of simplicity, accuracy and fast computation time. Ultimately, the obtained results from the simulations and real measurements demonstrate that the considered CDCR’s static and dynamic models are feasible.

The European Union’s Digital Markets Act (DMA) calls for far-reaching changes to the way economic activity will occur in EU digital markets. Before its remedies are imposed, it is critical to assess their impacts on individual markets, the digital sector, and the overall European economy. The European Commission (EC) released an Impact Assessment in support of the DMA that purports to evaluate it using cost/benefit analysis.

An economic evaluation of the DMA should consider its full impacts on dynamic competition. The Impact Assessment neither assesses the DMA's impact on dynamic competition in the digital economy nor evaluates the impacts of specific DMA prohibitions and obligations. Instead, it considers benefits in general and largely ignores costs. We study its benefit assessments and find they are based on highly inappropriate methodologies and assumptions. A cost/benefit study using inappropriate methodologies and largely ignoring costs cannot provide a sound policy assessment.

Instead of promoting dynamic competition between platforms, the DMA will likely reinforce existing market structures, ossify market boundaries, and stunt European innovation. The DMA is likely to chill R&D by encouraging free riding on the investments of others, which discourages making those investments. Avoiding harm to innovation is critical because innovation delivers large, positive spillover benefits, driving increases in productivity, employment, wages, and prosperity.

The DMA prioritizes static over dynamic competition, with the potential to harm the European economy. Given this, the Impact Assessment does not demonstrate that the DMA will be beneficial overall, and its implementation must be carefully tailored to alleviate or lessen its potential to harm Europe’s economic performance.

The purpose of this paper is to present a methodology for the evaluation of transport aircraft fuselages constructed in a semi-monocoque design.

A fuselage barrel was computed statically and dynamically using finite element methods. Static analysis was conducted using a global/local approach in which the section loads of the global model were used as load introduction in the local model. Subsequently, a crash analysis was performed, and the results from both disciplines were evaluated by either an optimization or parameter variation algorithm.

The presented process chain has been developed for use in preliminary design stages to assess aircraft configurations with regard to statics and dynamics. Parameter variation and optimization were conducted, proving functionality of the methodology.

In this early stage of methodology development only one exemplary static load case is considered and the fuselage design is limited to a constant section.

The presented process chain shows an approach to couple different disciplines to reduce the analysis time in aircraft preliminary design phase.

This methodology couples static design and crashworthiness aspects at an early design stage to avoid time- and cost-intensive redesign in subsequent detailed design stages. The process chain introduced in this paper uses a parameterized approach, making this methodology applicable for each fuselage in semi-monocoque design.

This paper aims to examine how the regional variable in country conflict modeling affects forecast accuracy and identifies a methodology to further improve the predictions.

This paper uses statistical learning methods to both evaluate the quantity of data for clustering countries along with quantifying accuracy according to the number of clusters used.

This study demonstrates that increasing the number of clusters for modeling improves the ability to predict conflict as long as the models are robust.

This study investigates the quantity of clusters used in conflict modeling, while previous research assumes a specific quantity before modeling.

The purpose of this paper is to introduce a novel method for developing static aeroelastic models based on rapid prototyping for wind tunnel testing.

A metal frame and resin covers are applied to a static aeroelastic wind tunnel model, which uses the difference of metal and resin to achieve desired stiffness distribution by the stiffness similarity principle. The metal frame is made by traditional machining, and resin covers are formed by stereolithgraphy. As demonstrated by wind tunnel testing and stiffness measurement, the novel method of design and fabrication of the static aeroelastic model based on stereolithgraphy is practical and feasible, and, compared with that of the traditional static elastic model, is prospective due to its lower costs and shorter period for its design and production, as well as avoiding additional stiffness caused by outer filler.

This method for developing static aeroelastic wind tunnel model with a metal frame and resin covers is feasible, especially for aeroelastic wind tunnel models with complex external aerodynamic shape, which could be accurately constructed based on rapid prototypes in a shorter time with a much lower cost. The developed static aeroelastic aircraft model with a high aspect ratio shows its stiffness distribution in agreement with the design goals, and it is kept in a good condition through the wind tunnel testing at a Mach number ranging from 0.4 to 0.65.

The contact stiffness between the metal frame and resin covers is difficult to calculate accurately even by using finite element analysis; in addition, the manufacturing errors have some effects on the stiffness distribution of aeroelastic models, especially for small-size models.

The design, fabrication and ground testing of aircraft static aeroelastic models presented here provide accurate stiffness and shape stimulation in a cheaper and sooner way compared with that of traditional aeroelastic models. The ground stiffness measurement uses the photogrammetry, which can provide quick, and precise, evaluation of the actual stiffness distribution of a static aeroelastic model. This study, therefore, expands the applications of rapid prototyping on wind tunnel model fabrication, especially for the practical static aeroelastic wind tunnel tests.

The purpose of this paper is to enhance the predictive power of bankruptcy prediction models by taking the past values of firms’ financial ratios as benchmark. For this purpose, the paper proposes an indicator variable expressing the time trends of financial ratios.

The proposed measure uses the minimum and the maximum of financial ratios from the previous period as benchmarks in order to give a more complete picture about the present financial performance of firms. The most popular classification methods of bankruptcy prediction were employed: discriminant analysis, logistic regression, decision trees. Sample specific results and conclusions were avoided by applying tenfold stratified cross-validation.

The empirical results suggest that the proposed measure can increase the predictive performance of bankruptcy prediction models compared to models based solely on static financial ratios. The results gave evidence for the fact that the firms’ past financial performance is a useful benchmark for evaluating the risk of future insolvency.

The proposed concept is completely new to the literature and practice of bankruptcy prediction. Similar concept has not been published to date. The suggested dynamization approach has three important advantages. It is easy to compute from time series of financial ratios. It is applicable within any classifier irrespective of its mathematical background. The performance of models can be enhanced without the necessity of giving up the interpretability of bankruptcy models, so the proposed measure may play very important role in the practice of credit scoring modeling as well.