Effects of finite strains in fully coupled 3D geomechanical simulations

Numerical modeling of geomechanical phenomena and geo-engineering problems often involves complex issues related to several variables and corresponding coupling effects. Under certain circumstances, both soil and rock may experience a nonlinear material response caused by, for example, plastic, viscous, or damage behavior or even a nonlinear geometric response due to large deformations or displacements of the solid. Furthermore, the presence of one or more fluids (water, oil, gas, etc.) within the skeleton must be taken into account when evaluating the interaction between the different phases of the continuum body. A multiphase three-dimensional (3D) coupled model of finite strains, suitable for dealing with solid-displacement and fluid-diffusion problems, is described for assumed elastoplastic behavior of the solid phase. Particularly, a 3D mixed finite element was implemented to fulfill stability requirements of the adopted formulation, and a permeability tensor dependent on deformation is introduced. A consolidation scenario induced by silo filling was investigated, and the effects of the adoption of finite strains are discusse

Absorptive capacity and relationship learning mechanisms as complementary drivers of green innovation performance

This paper aims to explore in depth how internal and external knowledge-based drivers actually affect the firms\u2019 green innovation performance. Subsequently, this study analyzes the relationships between absorptive capacity (internal knowledge-based driver), relationship learning (external knowledge-based driver) and green innovation performance. This study relies on a sample of 112 firms belonging to the Spanish automotive components manufacturing sector (ACMS) and uses partial least squares path modeling to test the hypotheses proposed. The empirical results show that both absorptive capacity and relationship learning exert a significant positive effect on the dependent variable and that relationship learning moderates the link between absorptive capacity and green innovation performance. This paper presents some limitations with respect to the particular sector (i.e. the ACMS) and geographical context (Spain). For this reason, researchers must be thoughtful while generalizing these results to distinct scenarios. Managers should devote more time and resources to reinforce their absorptive capacity as an important strategic tool to generate new knowledge and hence foster green innovation performance in manufacturing industries. The paper shows the importance of encouraging decision-makers to cultivate and rely on relationship learning mechanisms with their main stakeholders and to acquire the necessary information and knowledge that might be valuable in the maturity of green innovations. This study proposes that relationship learning plays a moderating role in the relationship between absorptive capacity and green innovation performance

Cadenas globales de valor y sistemas locales: las dos caras de una misma moneda

En un mundo cada vez m\ue1s complejo, en el que la competencia es cada vez mayor y m\ue1s global, los cl\ufasteres locales podr\uedan seguir desempe\uf1ando un papel muy relevante. El desarrollo de estrategias ancladas en un lugar, caracterizado por din\ue1micas innovadoras, es clave para poder beneficiarse de la integraci\uf3n en las cadenas globales de valor (CGV) y capturar parte del valor generado en ellas. A trav\ue9s del estudio de la coevoluci\uf3n de los cl\ufasteres y las CGV, en este art\uedculo se identifican aquellos elementos que son cr\uedticos para el desarrollo de las regiones y los cl\ufasteres en el \ue1mbito mundial, pudiendo as\ued explorarse con mayor grado de detalle la complejidad que en la pr\uf3xima d\ue9cada pueda generar la globalizaci\uf3n

Formazione per il futuro: spunti di riflessione per il settore edile-artigianoA

Nota derivante dal lavoro svolto per il Progetto di ricerca Monitoraggio della formazione in materia di sicurezza e rilevazione delle competenze richieste, promosso da Edilcassa Veneto e svolto da Ires Veneto, consistente in focus group compiuto con le parti sociali

Milk Fatty Acids Predicted by Mid-infrared Spectroscopy in Mixed Dairy Herds

Over the last years, healthy food has gained interest among consumers, especially with regard to the fat content of livestock products which has been associated to the risk of cardiovascular diseases. Individual milk samples (n = 12,624) of 2,977 Holstein-Friesian (HF), Brown Swiss (BS) and Simmental (SI) cows from 39 multibreed herds were analyzed for fat content, protein content, casein content and somatic cell count using mid-infrared spectroscopy (MIRS). Daily milk yield was also recorded. Groups of fatty acids (FA), expressed as percentage of milk fat, were predicted by MIRS: they were saturated (SFA), unsaturated (UFA), monounsaturated (MUFA) and polyunsaturated (PUFA) FA. Data were analyzed with a linear mixed model including the fixed effects of month of sampling, parity, days in milk (DIM), herd, breed, and interactions between parity and breed, and DIM and breed. The random effects were cow nested within breed and residual. Milk of HF cows exhibited the lowest percentage of SFA (70.45%) and the highest of UFA (31.20%), and milk of SI cows was intermediate between that of HF and BS breeds for all groups of FA. The values of groups of FA across DIM were similar for the different breeds. Results from this study indicate that, under similar environmental and management conditions, milk of HF exhibits better FA profile than milk of BS and SI

Milk Fatty Acids Predicted by Mid-infrared Spectroscopy in Mixed Dairy Herds

Over the last years, healthy food has gained interest among consumers, especially with regard to the fat content of livestock products which has been associated to the risk of cardiovascular diseases. Individual milk samples (n = 12,624) of 2,977 Holstein-Friesian (HF), Brown Swiss (BS) and Simmental (SI) cows from 39 multibreed herds were analyzed for fat content, protein content, casein content and somatic cell count using mid-infrared spectroscopy (MIRS). Daily milk yield was also recorded. Groups of fatty acids (FA), expressed as percentage of milk fat, were predicted by MIRS: they were saturated (SFA), unsaturated (UFA), monounsaturated (MUFA) and polyunsaturated (PUFA) FA. Data were analyzed with a linear mixed model including the fixed effects of month of sampling, parity, days in milk (DIM), herd, breed, and interactions between parity and breed, and DIM and breed. The random effects were cow nested within breed and residual. Milk of HF cows exhibited the lowest percentage of SFA (70.45%) and the highest of UFA (31.20%), and milk of SI cows was intermediate between that of HF and BS breeds for all groups of FA. The values of groups of FA across DIM were similar for the different breeds. Results from this study indicate that, under similar environmental and management conditions, milk of HF exhibits better FA profile than milk of BS and SI

A Sensor Network with Embedded Data Processing and Data-to-Cloud Capabilities for Vibration-Based Real-Time SHM

This work describes a network of low power/low-cost microelectromechanical- (MEMS-) based three-axial acceleration sensors with local data processing and data-to-cloud capabilities. In particular, the developed sensor nodes are capable to acquire acceleration time series and extract their frequency spectrum peaks, which are autonomously sent through an ad hoc developed gateway device to an online database using a dedicated transfer protocol. The developed network minimizes the power consumption to monitor remotely and in real time the acceleration spectra peaks at each sensor node. An experimental setup in which a network of 5 sensor nodes is used to monitor a simply supported steel beam in free vibration conditions is considered to test the performance of the implemented circuitry. The total weight and energy consumption of the entire network are, respectively, less than 50 g and 300 mW in continuous monitoring conditions. Results show a very good agreement between the measured natural vibration frequencies of the beam and the theoretical values estimated according to the classical closed formula. As such, the proposed monitoring network can be considered ideal for the SHM of civil structures like long-span bridges