In order to predict the effective properties of heterogeneous materials using the finite element approach, a boundary value problem (BVP) may be defined on a representative volume element (RVE) with appropriate boundary conditions, among which periodic boundary condition is the most efficient in terms of convergence rate. The classical method to impose the periodic boundary condition requires the identical meshes on opposite RVE boundaries. This condition is not always easy to satisfy for arbitrary meshes. This work develops a new method based on polynomial interpolation that avoids the need of matching mesh condition on opposite RVE boundaries.
An artificial Neural Network (NNW) is designed to serve as a surrogate model of micro-scale simulations in the context of multi-scale analyzes in solid mechanics. The design and training methodologies of the NNW are developed in order to allow accounting for history-dependent material behaviors. On the one hand, a Recurrent Neural Network (RNN) using a Gated Recurrent Unit (GRU) is constructed, which allows mimicking the internal variables required to account for history-dependent behaviors since the RNN is self-equipped with hidden variables that have the ability of tracking loading history. On the other hand, in order to achieve accuracy under multi-dimensional non-proportional loading conditions, training of the RNN is achieved using sequential data. In particular the sequential training data are collected from finite element simulations on an elasto-plastic composite RVE subjected to random loading paths. The random loading paths are generated in a way similar to a random walking in stochastic process and allows generating data for a wide range of strain-stress states and state evolution. The accuracy and efficiency of the RNN-based surrogate model is tested on the structural analysis of an open-hole sample subjected to several loading/unloading cycles. It is shown that a similar accuracy as with a FE 2 multiscale simulation can be reached with the RNN-based surrogate model as long as the local strain state remains in the training range, while the computational time is reduced by four orders of magnitude.
The recent spread of African swine fever (ASF) in the People's Republic of China and neighbouring countries in Asia has had significant economic consequences with an estimated direct cost of $55-$130 billion. This pandemic, originally detected in Republic of Georgia in 2007, has devastated the swine industry in large geographical areas of Southeast Asia with 14 countries reporting ASF outbreaks since the first documented case was confirmed in the city of Shenyang, Liaoning Province, China, on 3 August 2018. In the absence of any available vaccines, the control of ASF relies on the detection and culling of infected animals. The United States Department of Agriculture recently developed a recombinant experimental vaccine candidate, ASFV-G-ΔI177L, by deleting the I177L gene from the genome of the highly virulent pandemic ASFV strain Georgia, which efficaciouly protects pigs from the parental virus. Here, the initial studies were extended demonstrating that ASFV-G-ΔI177L is able to protect pigs against the virulent ASFV isolate currently circulating and producing disease in Vietnam with similar efficacy as reported against the Georgia strain. Comparative studies performed using a large number of pigs of European and Vietnamese origin demonstrated that a minimum protective dose of 10 2 HAD 50 of ASFV-G-ΔI177L equally protects animals of both breeds. In concurrence with those results, the onset of immunity in these animal breed showed appearance of protection in approximately one-third of the animals by the second week post vaccination, with full protection achieved by the fourth week post vaccination. Therefore, results presented here demonstrated that ASFV-G-ΔI177L is able to induce protection against virulent Vietnameese ASFV field strains and is effective in protecting local breeds of pigs as efficiently as previously shown for European cross-bred pigs. To our knowledge, this is the first report showing the efficacy of a Georgia 2007 based vaccine candidate in Asian breed of pigs or challenged with an Asian ASFV strain.
A large strain hyperelastic phenomenological constitutive model is proposed to model the highly nonlinear, rate-dependent mechanical behavior of amorphous glassy polymers under isothermal conditions. A corotational formulation is used through the total Lagrange formalism. At small strains, the viscoelastic behavior is captured using the generalized Maxwell model. At large strains beyond a viscoelastic limit characterized by a pressure-sensitive yield function, which is extended from the Drucker-Prager one, a viscoplastic region follows. The viscoplastic flow is governed by a non-associated Perzyna-type flow rule incorporating this pressure-sensitive yield function and a quadratic flow potential in order to capture the volumetric deformation during the plastic process. The stress reduction phenomena arising from the post-peak plateau and during the failure stage are considered in the context of a continuum damage mechanics approach.The post-peak softening is modeled by an internal scalar, so-called softening variable, whose evolution is governed by a saturation law. When the softening variable is saturated, the rehardening stage is naturally obtained since the isotropic and kinematic hardening phenomena are still developing. Beyond the onset of failure characterized by a pressure-sensitive failure criterion, the damage process leading to the total failure is controlled by a second internal scalar, so-called failure variable. The final failure occurs when the failure variable reaches its critical value. To avoid the loss of solution uniqueness when dealing with the continuum damage mechanics formalism, a non-local implicit gradient formulation is used for both the softening and failure variables, leading to a multi-mechanism non-local damage continuum. The pressure sensitivity considered in both the yield and failure conditions allows for the distinction under compression and tension loading conditions. It is shown through experimental comparisons that the proposed constitutive model has the ability to capture the complex behavior of amorphous glassy polymers, including their failure.
Background Streptococcus suis infection, an emerging zoonosis, is an increasing public health problem across South East Asia and the most common cause of acute bacterial meningitis in adults in Vietnam. Little is known of the risk factors underlying the disease.Methods and FindingsA case-control study with appropriate hospital and matched community controls for each patient was conducted between May 2006 and June 2009. Potential risk factors were assessed using a standardized questionnaire and investigation of throat and rectal S. suis carriage in cases, controls and their pigs, using real-time PCR and culture of swab samples. We recruited 101 cases of S. suis meningitis, 303 hospital controls and 300 community controls. By multivariate analysis, risk factors identified for S. suis infection as compared to either control group included eating “high risk” dishes, including such dishes as undercooked pig blood and pig intestine (OR1 = 2.22; 95%CI = [1.15–4.28] and OR2 = 4.44; 95%CI = [2.15–9.15]), occupations related to pigs (OR1 = 3.84; 95%CI = [1.32–11.11] and OR2 = 5.52; 95%CI = [1.49–20.39]), and exposures to pigs or pork in the presence of skin injuries (OR1 = 7.48; 95%CI = [1.97–28.44] and OR2 = 15.96; 95%CI = [2.97–85.72]). S. suis specific DNA was detected in rectal and throat swabs of 6 patients and was cultured from 2 rectal samples, but was not detected in such samples of 1522 healthy individuals or patients without S. suis infection.ConclusionsThis case control study, the largest prospective epidemiological assessment of this disease, has identified the most important risk factors associated with S. suis bacterial meningitis to be eating ‘high risk’ dishes popular in parts of Asia, occupational exposure to pigs and pig products, and preparation of pork in the presence of skin lesions. These risk factors can be addressed in public health campaigns aimed at preventing S. suis infection.
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