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The fundamental concept behind the MDA (Model Driven Architecture) approach is the development of many models, first the Computation Independent Model (CIM), then the Platform Independent Model (PIM), and lastly the Platform Specific Model (PSM) for the concrete implementation of the system. Web applications are just one example of customized software that is now being developed at an increasing rate. Interaction Flow Modeling Language (IFML) was developed to represent the front end of any program that necessitates a powerful interaction with a user through the use of an interface, regardless of the technical details of its implementation. There are various modeling tools for IFML; the Webratio tool is an illustration that facilitates the generation of the entire web application. This article discusses the model transformations in the MDA’s approach, starting from the CIM level up to the PSM level through the PIM level. To begin, we created the Business Process Model and Notation (BPMN) and IFML metamodels in Eclipse tool, we created also the BPMN model, and we get the IFML model by applying the shift rules in Atlas Transformation Language (ATL). Finally, we generated the application using a standard tool that implements IFML Webratio tool. A CRUD (Create, Read, Update, and Delete) features for the after-sales service case study were provided to illustrate the conversion strategy from the CIM level via the PIM level to the PSM level.

There are several models for magnetic hysteresis. Their key purposes are to model magnetization curves with a history dependence to achieve hysteresis cycles without a frequency dependence. There are different approaches to handling history dependence. The two main categories are Duhem-type models and Preisach-type models. Duhem models handle it via a simple directional dependence on the flux rate, without a proper memory. While the Preisach type model handles it via memory of the point where the direction of the flux rate is changed. The most common Duhem model is the phenomenological Jiles–Atherton model, with examples of other models including the Coleman–Hodgdon model and the Tellinen model. Examples of Preisach type models are the classical Preisach model and the Prandtl–Ishlinskii model, although there are also many other models with adoptions of a similar history dependence. Hysteresis is by definition rate-independent, and thereby not dependent on the speed of the alternating flux density. An additional rate dependence is still important and often included in many dynamic hysteresis models. The Chua model is common for modeling non-linear dynamic magnetization curves; however, it does not define classical hysteresis. Other similar adoptions also exist that combine hysteresis modeling with eddy current modeling, similar to how frequency dependence is included in core loss modeling. Most models are made for scalar values of alternating fields, but there are also several models with vector generalizations that also consider three-dimensional directions.

The theory governing the strong nuclear force—quantum chromodynamics—predicts that at sufficiently high energy densities, hadronic nuclear matter undergoes a deconfinement transition to a new phase of quarks and gluons 1 . Although this has been observed in ultrarelativistic heavy-ion collisions 2 , 3 , it is currently an open question whether quark matter exists inside neutron stars 4 . By combining astrophysical observations and theoretical ab initio calculations in a model-independent way, we find that the inferred properties of matter in the cores of neutron stars with mass corresponding to 1.4 solar masses ( M ⊙ ) are compatible with nuclear model calculations. However, the matter in the interior of maximally massive stable neutron stars exhibits characteristics of the deconfined phase, which we interpret as evidence for the presence of quark-matter cores. For the heaviest reliably observed neutron stars 5 , 6 with mass M  ≈ 2 M ⊙ , the presence of quark matter is found to be linked to the behaviour of the speed of sound c s in strongly interacting matter. If the conformal bound c s 2 ≤ 1 / 3 (ref. 7 ) is not strongly violated, massive neutron stars are predicted to have sizable quark-matter cores. This finding has important implications for the phenomenology of neutron stars and affects the dynamics of neutron star mergers with at least one sufficiently massive participant. The cores of neutron stars could be made of hadronic matter or quark matter. By combining first-principles calculations with observational data, evidence for the presence of quark matter in neutron star cores is found.

In this work, the dissolution behaviors of a series of sodium alkylbenzenesulfonates (NaLAS) tablets with different moisture contents and neutralization degrees were investigated in aqueous solution. The ANOVA-based, model-independent and model-dependent methods were employed to perform comparison analyses on dissolution profiles. The measurements of powder X-ray diffraction patterns and mechanical properties elucidate distinct differences in each formula. The results show that ANOVA provides a possibility for finding the source of differences among different variables, and the model-independent methods including the k values and mean dissolution time are easy to interpret and perform comparison analyses. The Hixson–Crowell model gives satisfactory correlation results for the dissolution data and the dissolution kinetics parameters are obtained. The inhibition effects of neutralization degree and moisture content on NaLAS dissolution were examined, which reveals that the increase in lamellar phase proportion leads to the reduction of dissolution rate. The comparison analyses performed in this work form part of a methodology for dissolution profile prediction and comparison.

Observations of galaxies and primordial radiation suggest that the Universe is made mostly of non-luminous dark matter 1 , 2 . Several new types of fundamental particle have been proposed as candidates for dark matter 3 , such as weakly interacting massive particles (WIMPs) 4 , 5 . These particles would be expected to interact with nuclei in suitable detector materials on Earth, for example, causing them to recoil. However, no definitive signal from such dark-matter interactions has been detected despite concerted efforts by many collaborations 6 . One exception is the much-debated claim by the DAMA collaboration of a statistically significant (more than nine standard deviations) annual modulation in the rate of nuclear interaction events. Annual modulation is expected because of the variation in Earth’s velocity relative to the Galaxy’s dark-matter halo that arises from Earth’s orbital motion around the Sun. DAMA observed a modulation in the rate of interaction events in their detector 7 – 9 with a period and phase consistent with that expected for WIMPs 10 – 12 . Several groups have been working to develop experiments with the aim of reproducing DAMA’s results using the same target medium (sodium iodide) 13 – 17 . To determine whether there is evidence for an excess of events above the expected background in sodium iodide and to look for evidence of an annual modulation, the COSINE-100 experiment uses sodium iodide as the target medium to carry out a model-independent test of DAMA’s claim. Here we report results from the initial operation of the COSINE-100 experiment related to the first task 18 , 19 . We observe no excess of signal-like events above the expected background in the first 59.5 days of data from COSINE-100. Assuming the so-called standard dark-matter halo model, this result rules out spin-independent WIMP–nucleon interactions as the cause of the annual modulation observed by the DAMA collaboration 20 – 23 . The exclusion limit on the WIMP–sodium interaction cross-section is 1.14 × 10 −40 cm 2 for 10-GeV c −2 WIMPs at a 90% confidence level. The COSINE-100 experiment will continue to collect data for two more years, enabling a model-independent test of the annual modulation observed by the DAMA collaboration. Early results from the COSINE-100 experiment—designed to test a much-debated claim of the detection of a dark-matter signal—show no indications of dark matter, providing evidence against the previous claim.

In the human–robot collaboration system, the high-precision distortion correction of the camera as an important sensor is a crucial prerequisite for accomplishing the task. The traditional correction process is to calculate the lens distortion with the camera model parameters or separately from the camera model. However, in the optimization process calculate with the camera model parameters, the mutual compensation between the parameters may lead to numerical instability, and the existing distortion correction methods separated from the camera model are difficult to ensure the accuracy of the correction. To address this problem, this study proposes a model-independent lens distortion correction method based on the image center area from the perspective of the actual camera lens distortion principle. The proposed method is based on the idea that the structured image preserves its ratios through perspective transformation, and uses the local image information in the central area of the image to correct the overall image. The experiments are verified from two cases of low distortion and high distortion under simulation and actual experiments. The experimental results show that the accuracy and stability of this method are better than other methods in training and testing results.

This paper presents a computational strategy that combines a novel rate-independent phenomenological model with an explicit time integration method to efficiently perform nonlinear dynamic analyses of non-stiffening hysteretic mechanical systems. The novel rate-independent model, developed by specializing a general class of uniaxial phenomenological models, has an algebraic nature, is based on a set of only three parameters having a clear mechanical significance, and can be easily implemented in a computer program. The adopted explicit structure-dependent time integration method, belonging to the Chang’s family of explicit methods, is unconditionally stable for all non-stiffening hysteretic mechanical systems, has a second-order accuracy, does not suffer from numerical damping, and displays a small relative period error for small time step. Furthermore, it does not require iterative procedures and, consequently, does not suffer from convergence issues. Numerical accuracy and computational efficiency of the proposed procedure are assessed by performing several nonlinear time history analyses on hysteretic mechanical systems and comparing the results with those obtained by employing a conventional strategy based on the celebrated Bouc–Wen model, or its modified version, and the widely used Newmark’s constant average acceleration method.

This article presents a theoretical investigation of the differential, integrated, elastic, inelastic, total, momentum-transfer, and viscosity cross-sections, along with the total ionization cross-section, for elastically scattered electrons and positrons from a carbon dioxide (CO2) molecule in the incident energy range of 1 eV ≤Ei≤ 1 MeV. In addition, for the first time, we report the spin polarization of e±−CO2 scattering systems. The independent atom model (IAM) with screening correction (IAMS) using a complex optical potential was employed to solve the Dirac relativistic equation in partial-wave analysis. The comparison of our results with the available experimental data and other theoretical predictions shows a reasonable agreement in the intermediate- and high-energy regions.

Small sample learning aims to learn information about object categories from a single or a few training samples. This learning style is crucial for deep learning methods based on large amounts of data. The deep learning method can solve small sample learning through the idea of meta-learning “how to learn by using previous experience.” Therefore, this paper takes image classification as the research object to study how meta-learning quickly learns from a small number of sample images. The main contents are as follows: After considering the distribution difference of data sets on the generalization performance of measurement learning and the advantages of optimizing the initial characterization method, this paper adds the model-independent meta-learning algorithm and designs a multi-scale meta-relational network. First, the idea of META-SGD is adopted, and the inner learning rate is taken as the learning vector and model parameter to learn together. Secondly, in the meta-training process, the model-independent meta-learning algorithm is used to find the optimal parameters of the model. The inner gradient iteration is canceled in the process of meta-validation and meta-test. The experimental results show that the multi-scale meta-relational network makes the learned measurement have stronger generalization ability, which further improves the classification accuracy on the benchmark set and avoids the need for fine-tuning of the model-independent meta-learning algorithm.

Robot manipulators should be able to quickly detect collisions to limit damage due to physical contact. Traditional model-based detection methods in robotics are mainly concentrated on the difference between the estimated and actual applied torque. In this paper, a model independent collision detection method is presented, based on the vibration features generated by collisions. Firstly, the natural frequencies and vibration modal features of the manipulator under collisions are extracted with illustrative examples. Then, a peak frequency based method is developed for the estimation of the vibration modal along the manipulator structure. The vibration modal features are utilized for the construction and training of the artificial neural network for the collision detection task. Furthermore, the proposed networks also generate the location and direction information about contact. The experimental results show the validity of the collision detection and identification scheme, and that it can achieve considerable accuracy.