In previous work, summarized in this paper, we proposed an operation of parallel composition for rewriting-logic theories, allowing compositional specification of systems and reusability of components. The present paper focuses on compositional verification. We show how the assume/guarantee technique can be transposed to our setting, by giving appropriate definitions of satisfaction based on transition structures and path semantics. We also show that simulation and equational abstraction can be done componentwise. Appropriate concepts of fairness and deadlock for our composition operation are discussed, as they affect satisfaction of temporal formulas. We keep in parallel a distributed and a global view of composed systems. We show that these views are equivalent and interchangeable, which may help our intuition and also has practical uses as, for example, it allows global-style verification of a modularly specified system. Under consideration in Theory and Practice of Logic Programming (TPLP).

Regression models with log-transformed dependent variables are widely used by social scientists to investigate nonlinear relationships between variables. Unfortunately, this transformation complicates the substantive interpretation of estimation results and often leads to incomplete and sometimes even misleading interpretations. We focus on one valuable but underused method, the presentation of quantities of interest such as expected values or first differences on the original scale of the dependent variable. The procedure to derive these quantities differs in seemingly minor but critical aspects from the well-known procedure based on standard linear models. To improve empirical practice, we explain the underlying problem and develop guidelines that help researchers to derive meaningful interpretations from regression results of models with log-transformed dependent variables.

This paper focuses on developing a novel hybrid-haptic (nHH) device with a remote center of rotation with 4 DOFs (degrees of freedom) intendant to be used as a haptic device. The new architecture is composed of two chains handling each one a part of the motions. It has the advantages of a parallel robot as high stiffness and accuracy, and the large workspace of the serial robots. The optimal synthesis of the nHH was performed using real-coded genetic algorithms. The optimization criteria and constraints were established and successively formulated and solved using a mono-objective function. A validation and comparison study were performed between the spherical parallel manipulator and the nHH. The obtained results are promising since the nHH is compared to other similar task devices, such as spherical parallel manipulator, and presents a suitable kinematic performance with a task workspace free singularity inside.

That the world we seem to experience around us might be nothing but a simulation – perhaps generated by a demon or super-computer – is a perennial theme in science fiction movies. Muriel Leuenberger explores a recent example.

Uncertainty quantification (UQ) plays a crucial role in data assimilation (DA) since it impacts both the quality of the reconstruction and near-future forecast. However, traditional UQ approaches are often limited in their ability to handle complex datasets and may have a large computational cost. In this paper, we present a new ensemble-based approach to extend the 4DVarNet framework, an end-to-end deep learning scheme backboned on variational DA used to estimate the mean of the state along a given DA window. We use conditional 4DVarNet simulations compliant with the available observations to estimate the 4DVarNet probability density function. Our approach enables to combine both the efficiency of 4DVarNet in terms of computational cost and validation performance with a fast and memory-saving Monte-Carlo based post-processing of the reconstruction, leading to the so-called En4DVarNet estimation of the state pdf. We demonstrate our approach in a case study involving the sea surface height: 4DVarNet is pretrained on an idealized Observation System Simulation Experiment (OSSE), then used on real-world dataset (OSE). The sampling of independent realizations of the state is made among the catalogue of model-based data used during training. To illustrate our approach, we use a nadir altimeter constellation in January 2017 and show how the uncertainties retrieved by combining 4DVarNet with the statistical properties of the training dataset lead to a relevant information providing in most cases a confidence interval compliant with the Cryosat-2 nadir alongtrack dataset kept for validation.

We develop a novel Monte Carlo algorithm for the vector consisting of the supremum, the time at which the supremum is attained, and the position at a given (constant) time of an exponentially tempered Lévy process. The algorithm, based on the increments of the process without tempering, converges geometrically fast (as a function of the computational cost) for discontinuous and locally Lipschitz functions of the vector. We prove that the corresponding multilevel Monte Carlo estimator has optimal computational complexity (i.e. of order $\varepsilon^{-2}$ if the mean squared error is at most $\varepsilon^2$ ) and provide its central limit theorem (CLT). Using the CLT we construct confidence intervals for barrier option prices and various risk measures based on drawdown under the tempered stable (CGMY) model calibrated/estimated on real-world data. We provide non-asymptotic and asymptotic comparisons of our algorithm with existing approximations, leading to rule-of-thumb principles guiding users to the best method for a given set of parameters. We illustrate the performance of the algorithm with numerical examples.

Resilience is a widely studied concept that is a key objective in the design and development of sustainable systems. This is especially true for the agricultural systems critical to food production, economic viability, and sustainability of our communities, as farmers seek to meet increasing demand in the face of shocks such as climate change and natural disasters. Although there is a rich body of work examining resilience, there is limited understanding of how the concept of resilience should be tailored for agricultural systems. This study seeks to address this gap by performing a systematic literature review of 50 papers selected from SCOPUS using the PRISMA protocol. A summary of research topics and characteristics by geographical region is presented. The paper also categorizes the types of shocks studied and the corresponding response methods. Results suggest that the focus of resilience research changes by region, which may indicate that design strategies and objectives should also differ by region. Furthermore, the work identifies a need for more simulation-based quantitative research into the impact of resilience.

People living with tetraplegia experience a significant loss of sensory and motor function; with the severity depending on their injury level and completeness. To complete tasks independently, people with tetraplegia often rely on assistive devices. To avoid upper extremity pain, designs should not require applications of force near the limits of the user's physical strength. This paper establishes a 2D biomechanical model using static equilibrium and joint torque limits to predict multidirectional strength patterns in the sagittal plane for people with C5 to C7 tetraplegia in a seated position. The results from the biomechanical model highlight the areas and directions of high strength. The strength patterns observed in this paper provide an opportunity for designers to evaluate strength requirements and take advantage of areas and directions of high strength and ensure that users are not required to apply force near their physical limit. In doing this, designs such as assistive devices can be developed that enable users with a reduction in strength to operate them independently.

Among additive manufacturing processes, wire arc additive manufacturing (WAAM) is one of the most promising methods for manufacturing complex near-net-shape parts, as it allows the layer-by-layer deposition of welded material at a high deposition rate. However, this technology is highly dependent on deposition conditions and thermomechanical phenomena during the process. Therefore, process simulation could be used to analyse the effects of different deposition parameters on the thermomechanical results to optimise the process. However, as the computing time required for this study may become prohibitive, a dedicated strategy is needed to reduce it while maintaining a good level of accuracy. In this study, only the thermal analysis of the process is investigated. An efficient metamodel based on B-spline entities is developed to emulate the thermal response of the WAAM process when building a mild steel four-layer wall structure. Thanks to B-spline entities, the temperature profile at different locations is approximated as a function of a subset of deposition parameters of WAAM process, and the results are compared with the simulated temperature profile resulting from a validation dataset.

Precise sustainability assessment becomes increasingly important in decision-making, marketing, and regulations. Therefore, reliable and comparable LCA becomes mandatory. Currently, primary data is rarely available due to vastly complex value chains. Secondary data from eco-databases provide a remedy to estimate the sustainability impacts of up- and downstream processes. While giving insights and estimations, this data is seldom fitting exactly to the own processes and lacks comparability. Therefore, this paper proposes a method to close the gap between unreliable secondary data and unavailable primary data. This gap is to be closed by the integration of simulated process data. CAM is a tool during the work preparation to assess the design's manufacturability, decrease set-up times and optimize the NC code. However, integrating DES into LCA is still subject to research and will be discussed in this paper. This paper answers the question of the necessary steps to integrate the simulated production process in an LCA to increase the quality and reliability of sustainability indicators. A method is presented, and the implementation of the steps with the help of a developed assistance system on an example is performed.

Over the last 20 years, finite element analysis (FEA) has become a standard analysis tool for metal joining processes. When FEA tools are combined with design of experiments (DOE) methodologies, academic research has shown the potential for virtual DOE to allow for the rapid analysis of manufacturing parameters and their influence on final formed products. However, within the domain of bulk-metal joining, FEA tools are rarely used in industrial applications and limit DOE trails to physical testing which are therefore constrained by financial costs and time.

This research explores the suitability of an FEA-based DOE to predict the complex behaviour during bulk-metal joining processes through a case study on the staking of spherical bearings. For the two DOE outputs of pushout strength and post-stake torque, the FEA-based DOE error did not exceed ±1.2% and ± 1.5 Nm respectively which far surpasses what was previously capable from analytically derived closed-form solutions. The outcomes of this case study demonstration the potential for FEA-based DOE to provide an inexpensive, methodical, and scalable solution for modelling bulk-metal joining process

The tolerancing of products for manufacturing is usually performed at the end of the design process and the responsibility of the designer. Although components are commonly tolerated to ensure functionality, time-based influences, like wear, that occur during operation, are often neglected. This could result in small amounts of scrap after production, but high quantities of failure during operation. To overcome this issue, this paper presents an approach to perform a multi-objective optimization considering tolerances based on a wear simulation. Thereby, mean shifts serve as optimization variables, while the aim of the optimization is to generate an optimal ratio of scrap to failure. In addition, the optimization results are interpreted and further options for the designer are presented. Moreover, the approach is exemplary applied to a use case.

Remote and virtual centers have been studied for the past twenty years as an alternative to the traditional air traffic control tower environment. Designing such complex sociotechnical systems requires a systems engineering approach that appropriately integrates the human element as well as the technological and organizational components. In this paper, we identify the challenges of implementing this human-systems integration in the design of complex systems. We present the feedback we obtained from a series of semi-structured interviews with people involved in the development of military air traffic solutions. The participants' responses helped us establish methodological guidelines for designing and building a disruptive remote and virtual air traffic control center. We discuss how virtualized human- in-the-loop simulations in particular should help designers analyze user activity and be more flexible in system acquisition.

Finite element (FE) simulations can be used both in the early product development phase to evaluate the performance of developed components as well as in later stages to verify the reliability of functions and components that would otherwise require a large number of physical prototype tests. This requires calibrated material cards that are capable of realistically representing the specific material behavior. The necessary material parameter identification process is usually time-consuming and resource-intensive, which is why the direct inverse method based on machine learning has recently become increasingly popular. Within the neural network (NN) the generated domain knowledge can be stored and retrieved within milliseconds, which is why this method is time and resource-efficient. This research paper describes advantages and potentials of the direct inverse method in the context of the product development process (PDP). Additionally, arising transformation opportunities of the PDP are discussed and an application scenario of the method is presented followed by possible linkage potentials with existing development methods such as shape optimization.

Simulation is fundamental to many engineering design processes and powers the field of computational design. Simulation inherently consumes energy resulting in CO2 emissions that impact our environment. While one can source energy from renewable sources and use energy efficient hardware, efforts need to also be made in how we can use simulation in a sustainable manner.

This paper presents a sustainable simulation framework that borrows concepts from web services. The framework makes it easy for engineering firms to adopt and embed sustainable simulation practices thereby removing the burden from the designer tin thinking about how to design sustainably. An illustrative example reveals a 25% reduction in computational effort can be achieved by adopting the framework.

Approximately 10% of hospitalized patients develops decubitus ulcers that quickly degenerates into chronic illness that reduces the quality of life and requires expensive clinical management. The use of an anti-decubitus active mattress, that automatically redistributes the pressure loads, reduces the occurrence of new lesions and promotes the healing of the pre-existing ones.

The aim of this work is to design and develop two tools to support the design of an anti-decubitus active mattress. Almost all the systems found in literature are based on the classification of pressure maps through machine learning and are difficultly usable in the design context.

This work proposes a pressure map Classifier and an Interactive Simulator of the mattress, based on a simpler logic, by integrating image processing techniques and functioning simulations. The Classifier can recognize the patient's pressure maps and classify them according to six reference sleep postures. The Interactive Simulator allows to understand the operating mechanisms of the mattress and to test the controller and the various control logics in the absence of a physical prototype.

Additive manufacture (AM) enables a greatly increased design freedom owing to its ability to manufacture otherwise difficult or impossible geometries. However, design creativity can often present itself as a barrier to realising the advantages that AM could offer. In this study the use of AM, bioresorbable materials and lattice design is considered as a method of satisfying contradicting design requirements during fracture healing. Often, immediately after a fracture high stiffness fixation is required; contradictingly during the remodelling phase high stiffness can inhibit bone healing. This study proposes the use of a bioresorbable body centred cubic (BCC) or face centred cubic (FCC) lattice structure to meet the need for tailored variation in implant stiffness over time. To reduce computational expense of lattice modelling a method is outlined, including the use of homogenisation. Results show homogenised representations perform within 5.2% and 1.4% for BCC and FCC unit cells respectively, with a 95% reduction in computational expense. Using resorption rates from the literature, time-dependent change in unit cell geometry was also modelled, showing the way in which a decrease in stiffness over time could be achieved.