Development of a Multi-Criteria Design Optimization Methodology for Automotive Plastics Parts

The plastic industry is undergoing drastic changes, due to the customer sustainability perception of plastics, and the eruption of new processes (such 3D printing) and materials (such as renewably sourced resins). To enable a fast transition to high-quality, sustainable plastic applications, a specific methodology could be a key competitive advantage. This novel methodology is focused on improving the objectivity and efficiency of plastic production and the design review process. It is applicable to discrete optimization events in any product lifecycle milestone, from concept design to serial production stages. The methodology includes a natural way to capture plastic-related knowledge and trends, oriented towards building a dynamic “interaction matrix”, with a list of potential optimizations and their positive or negative impacts in a comprehensive set of multi-criteria evaluations. With an innovative approach, the matrix allows the possibility to incorporate a business strategy, which could be different at every lifecycle stage. The business strategy is translated from the common “verbal” definition into a quantitative set of “Target and Restrictions”, making it possible to detect and prioritize the best potential design optimization changes according to the strategy. This methodology helps to model and compare design alternatives, verify impacts in every evaluation criteria, and make robust and objective information-based decisions. The application of the methodology in real cases of plastic material design optimization in the automotive industry has provided remarkable results, accelerating the detection of improvement methods aligned with the strategy and maximizing the improvement in product competitiveness and sustainability. In comparison with the simultaneous application of existing mono-criteria optimization methodologies (such as “Design to Cost” or “Eco Design”) and subjective expert-based reviews, the novel methodology has a reduced workload and risks, confirming its potential for future application and further development in other polymer-based products, such as consumer goods or packaging.

Enhanced Collaborative Filtering for Personalized E-Government Recommendation

The problems with the information overload of e-government websites have been a big obstacle for users to make decisions. One promising approach to solve this problem is to deploy an intelligent recommendation system on e-government platforms. Collaborative filtering (CF) has shown its superiority by characterizing both items and users by the latent features inferred from the user–item interaction matrix. A fundamental challenge is to enhance the expression of the user or/and item embedding latent features from the implicit feedback. This problem negatively affected the performance of the recommendation system in e-government. In this paper, we firstly propose to learn positive items’ latent features by leveraging both the negative item information and the original embedding features. We present the negative items mixed collaborative filtering (NMCF) method to enhance the CF-based recommender system. Such mixing information is beneficial for extending the expressiveness of the latent features. Comprehensive experimentation on a real-world e-government dataset showed that our approach improved the performance significantly compared with the state-of-the-art baseline algorithms.

Do metal infrastructure effects cancel out in time-lapse electromagnetic measurements?

Controlled-source electromagnetic (CSEM) methods have the potential to be used in reservoir monitoring problems, due to their sensitivity to subsurface resistivity distribution. For example, time-lapse electromagnetic (EM) measurements can help to determine reservoir changes during enhanced oil recovery (EOR) processes such as water/steam injection or CO2 sequestration. Although metal infrastructure such as pipelines and casings can strongly influence EM data and mask the underlying geological response, one may presume that these effects cancel out during time-lapse surveys. In this paper, we analyze the effects of well casings on time-lapse surface-to-surface EM measurements. First, using a synthetic example of an onshore 1D hydrocarbon reservoir we quantify the effect of single and multiple casings at several source and receiver locations. We show that time-lapse responses are distorted significantly when a source or receiver is located near a casing. Next, we study a more realistic scenario where we approximate the hydrocarbon reservoir as a thin bounded resistive sheet. We present a Method of Moments (MoM) algorithm to calculate the secondary currents and charges on a well casing and resistive sheet combination and validate the electric fields these secondary sources generate against finite element modeling. Finally, we calculate and explicitly demonstrate time-lapse amplitude changes in the well casing-thin sheet interaction matrix, secondary currents, charges, and surface electric fields. Our 3D modeling results show that the conductive casing reduces the ability of the resistive sheet to impede the current flow and distorts time-lapse responses. Therefore, one cannot fully eliminate casing effects by subtraction of time-lapse data and must fully incorporate such infrastructure into forward models for time-lapse EM inversion.

Artificial selection of communities drives the emergence of structured interactions

Species-rich communities, such as the microbiota or environmental microbial assemblages, provide key functions for human health and ecological resilience. Increasing effort is being dedicated to design experimental protocols for selecting community-level functions of interest. These experiments typically involve selection acting on populations of communities, each of which is composed of multiple species. Numerical explorations allowed to link the evolutionary dynamics to the multiple parameters involved in this complex, multi-scale evolutionary process. However, a comprehensive theoretical understanding of artificial selection of communities is still lacking. Here, we propose a general model for the evolutionary dynamics of species-rich communities, each described by disordered generalized Lotka-Volterra equations, that we study analytically and by numerical simulations. Our results reveal that a generic response to selection for larger total community abundance is the emergence of an isolated eigenvalue of the interaction matrix that can be understood as an effective cross-feeding term. In this way, selection imprints a structure on the community, which results in a global increase of both the level of mutualism and the diversity of interactions. Our approach moreover allows to disentangle the role of intraspecific competition, interspecific interactions symmetry and number of selected communities in the evolutionary process, and can thus be used as a guidance in optimizing artificial selection protocols.

Experiments and Crystal Plasticity Simulations on Plastic Anisotropy of Naturally Aged and Annealed Al–Mg–Si Alloy Sheets

The influence of the heat treatment on the plastic anisotropy of an Al–Mg–Si sheet was investigated by experiments and crystal plasticity simulations. Uniaxial tension tests were conducted for the naturally aged (T4 temper) and annealed (O temper) Al–Mg–Si sheets. Solute atoms Mg and Si form clusters in the T4 temper sheet, while they bind to form precipitates in the O temper sheet. It is found that the in-plane variation of the R value, texture, and grain size are almost identical for both sheets. By contrast, the anisotropy of the flow stress is clearly dissimilar; the flow stress is the highest in the diagonal direction for the O temper sheet, whereas the flow stress in that direction is nearly lowest for the T4 temper sheet. Thus, the heat treatment alters the anisotropy of the flow stress. The plastic behaviors of the specimens were simulated using the dislocation density-based crystal plasticity model. The influence of the dislocation interaction matrix on the plastic anisotropy was examined. The orientation dependence of the flow stress is found to be sensitive to the interaction matrix. The flow stresses predicted by the interaction matrix determined based on the dislocation dynamic simulation agree with the experimental results for the O temper sheet. Whereas this interaction matrix does not reproduce the flow stress anisotropy for the T4 temper sheet. When the interactions among the dislocations are set to equivalent—i.e., the interaction matrix is filled with unity—the crystal plasticity simulation results in the flow stress anisotropy that is similar to the experimental trend of the T4 temper sheet. In contrast to the flow stress, the R value is insensitive to the interaction matrix, and the predicted R values agree with the experimental results for both specimens.

Multi-species host range of staphylococcal phages isolated from wastewater

The host range of bacteriophages defines their impact on bacterial communities and genome diversity. Here, we characterize 94 novel staphylococcal phages from wastewater and establish their host range on a diversified panel of 117 staphylococci from 29 species. Using this high-resolution phage-bacteria interaction matrix, we unveil a multi-species host range as a dominant trait of the isolated staphylococcal phages. Phage genome sequencing shows this pattern to prevail irrespective of taxonomy. Network analysis between phage-infected bacteria reveals that hosts from multiple species, ecosystems, and drug-resistance phenotypes share numerous phages. Lastly, we show that phages throughout this network can package foreign genetic material enclosing an antibiotic resistance marker at various frequencies. Our findings indicate a weak host specialism of the tested phages, and therefore their potential to promote horizontal gene transfer in this environment.

Gene network simulations provide testable predictions for the molecular domestication syndrome

The domestication of plant species lead to repeatable morphological evolution, often referred to as the phenotypic domestication syndrome. Domestication is also associated with important genomic changes, such as the loss of genetic diversity compared to adequately large wild populations, and modifications of gene expression patterns. Here, we explored theoretically the effect of a domestication-like scenario on the evolution of gene regulatory networks. We ran population genetics simulations in which individuals were featured by their genotype (an interaction matrix encoding a gene regulatory network) and their gene expressions, representing the phenotypic level. Our domestication scenario included a population bottleneck and a selection switch mimicking human-mediated directional and canalizing selection, i.e., change in the optimal gene expression level and selection towards more stable expression across environments. We showed that domestication profoundly alters genetic architectures. Based on four examples of plant domestication scenarios, our simulations predict (i) a drop in neutral allelic diversity, (ii) a change in gene expression variance that depends upon the domestication scenario, (iii) transient maladaptive plasticity, (iv) a deep rewiring of the gene regulatory networks, with a trend towards gain of regulatory interactions, and (v) a global increase in the genetic correlations among gene expressions, with a loss of modularity in the resulting coexpression patterns and in the underlying networks. We provide empirically testable predictions on the differences of genetic architectures between wild and domesticated forms. The characterization of such systematic evolutionary changes in the genetic architecture of traits contributes to define a molecular domestication syndrome.

Real Time Estimator to Perform Targeted Biopsies With a Free-Wrist Robot Despite Large Deformations of the Insertion Orifice

In the context of keyhole surgery, and more particularly of uterine biopsy, the fine automatic movements of a surgical instrument held by a robot with 3 active DOF’s require an exact knowledge of the point of rotation of the instrument. However, this center of rotation is not fixed and moves during an examination. This paper deals with a new method of detecting and updating the interaction matrix linking the movements of the robot with the surgical instrument. This is based on the method of updating the Jacobian matrix which is named the “Broyden method”. It is able to take into account body tissue deformations in real time in order to improve the pointing task for automatic movements of a surgical instrument in an unknown environment.

Modelling the Heterogeneity of the Mutual Influence between Russian Regions in the Manufacturing Industry

As factors affecting interregional interactions play an important role in regional economic development. Thus, developing a methodology for assessing these interactions is becoming urgent. The article proposes a methodological approach to analyse the factors influencing possible interactions between Sverdlovsk oblast and other constituent entities of the Russian Federation in the manufacturing industry. It is hypothesised that the elements of an interregional interaction matrix are proxy variables characterising the degree of this interaction. An economic analysis of relations and production chains between Sverdlovsk oblast and other constituent entitles confirmed this hypothesis. First, based on the spatial distribution of manufacturing output in the examined regions, values of an indicator showing the strength of their mutual influence were determined. Second, the impact of economic, infrastructural and institutional factors on the obtained indicator, characterising the inter action between Sverdlovsk oblast and other regions, was assessed using quantile regression. In this case, such a technique was chosen instead of the classical ordinary least squares (OLS) regression that incorrectly estimates the dependencies between the studied variables. This is expressed in the fact that the regression coefficients de pend on q-quantile of the dependent variable. We have revealed that price levels of the examined regions do not affect their possible interactions with Sverdlovsk oblast. Simultaneously, the dissemination of knowledge acts a driver of interaction between the considered regional manufacturing industries. The research findings can be used to prepare strategies, programmes and schemes for the placement and development of industries, considering the potential of Sverdlovsk oblast and other Russian regions.

Exploring the Issues in Policy Deployment Using SAP-LAP and eIRP Approaches: An Empirical Case Study

Policy deployment refers to the formulation of policies, their implementations, and reviews. Every organization irrespective of its size and sector involves the process of policy deployment. There exist many factors that affect the process of policy deployment. These factors pose flexibilities that can alter the outcome of a policy. This paper explores the flexibilities in the choice of the factors governing a policy decision. An Indian auto-spare part industry with certain policy issues is selected for theory building. A framework model for solving the policy deployment issues is formulated using an interpretive comparative technique known as situation-actor-process-learnings-actions-performance (SAP-LAP). It is based on the comparative study of contextual relationships within and among its various elements. The contextual relationship within a particular element is presented in the form of a self-interaction matrix and the contextual relationships among the elements are depicted using the cross-interaction matrix. Further, the actors and actions of the framework are ranked using an efficient interpretive ranking process (eIRP) for easy selection and prioritization. The ranks obtained through the efficient IRP model will facilitate the policymakers in the formulation of better decisions and implementation of key policies.