On the use of laser-scanning vibrometry for mechanical performance evaluation of 3D printed specimens

In this study, we explored the suitability of laser-scanning vibrometry (LSV) for evaluation of the mechanical behavior of rectangular prisms produced by Fused Filament Fabrication (FFF). Our hypothesis was that LSV would be able to discriminate the mechanical behavior of specimens fabricated with different process parameters combinations. Build orientation, raster angle, nozzle temperature, printing speed and layer thickness were the process parameters of interest. Based on a factorial design of experiment approach, 48 different process parameter combinations were taken into account and 96 polylactic acid (PLA) rectangular prisms were fabricated. The characterization of their dynamical behavior provided frequency data, making possible the computation of an equivalent elastic modulus metric. Statistical analysis of the equivalent elastic modulus dataset confirmed the significant influences of raster angle, build orientation and nozzle temperature. Moreover, multivariate regression models served to rank, not only the significant influences of individual process parameters, but also the significant quadratic and cubic interactions between them. The previous knowledge was then applied to generate an ad hoc model selecting the most important factors (linear and interactions). The predicted equivalent elastic moduli provided by our ad hoc model were used in modal analysis simulations of both 3D printed rectangular prisms and a complex part. The simulated frequencies thus obtained were generally closer to the experimental ones (=11%), as compared to modal analysis simulations based on internal geometry modelling (=33%). The use of LSV appears very promising in the characterization of the mechanical behavior and integrity of 3D printed parts. Other additive manufacturing technologies may benefit from the use of this technique and from the adoption of the presented methodology to test, simulate and optimize the properties of 3D printed products. © 2021 The Author

Analysis of the relationship between the adoption of the OHSAS 18001 and business performance in different organizational contexts

This paper investigates how the characteristics of operational processes—systematic and project-based—affect the impact of adopting the safety management systems on different performance metrics. The proposed approach allows the development of a framework which matches safety problems and risks encompassed by organizational tasks with solutions generated by new safety knowledge linked to the adoption of the OHSAS 18001 standard. Our analysis of the effect over work accidents, as well as operational and economic performance of implementing the OHSAS 18001 in Spanish manufacturing, construction and professional services organizations during 2006–2009 shows that organizations modify existing safety practices to mitigate work accidents, and that safety learning effects widely vary across industry sectors. Organizations whose current knowledge is mostly codified and processes are highly systematic benefit more from safety knowledge and experience, whereas the effects of the OHSAS 18001 dilute in organizations whose knowledge is high in tacitness, and whose processes difficult the visibility of the consequences of work accidents. This study has important implications for managing knowledge acquisition processes. The findings offer valuable insights on how managers can develop communication and coordination actions to cope with the potential incompatibilities between safety management systems, the properties of knowledge and work environmental conditions.Preprin

Territorial efficiency: analysis of the role of public work safety controls

This study analyzes the efficiency of Spanish provinces in a model that incorporates occupational health and safety (OHS) policy controls and work accidents into the analysis. Building on productivity models rooted in nonparametric frontier methods, namely Data Envelopment Analysis, the proposed approach allows the development of a production function that accurately models the joint production of desirable (GDP) and undesirable (work accidents) outputs. The efficiency analysis of the 50 Spanish provinces during 2003–2012 reveals that territories that drastically cut resources dedicated to OHS controls—in our case, safety inspections and economic sanctions for safety violations—show higher inefficiency levels. Nevertheless, the changes in OHS policies introduced by Spanish provinces after the change in the state of the economy in 2008 had a heterogeneous impact on their efficiency level. Effective OHS policy is not necessarily linked to merely implementing more OHS policy controls, but rather to the capacity of territories to efficiently allocate their available OHS resources and monitor business activity. Policy implications and future research avenues are discussed.Peer ReviewedPostprint (author's final draft

Prohibición de despidos y suspensiones en el ámbito privado durante la pandemia. Beneficios y consecuencias

Fil: Abad, Damián Esteban. Universidad Nacional Villa María; Argentina.Fil: Seppey, Rodolfo Héctor. Universidad Nacional Villa María; Argentina.Fil: Abad, María Ayelén. Universidad Nacional Villa María; Argentina

Generative adversarial networks for data-scarce spectral applications

Generative adversarial networks (GANs) are one of the most robust and versatile techniques in the field of generative artificial intelligence. In this work, we report on an application of GANs in the domain of synthetic spectral data generation, offering a solution to the scarcity of data found in various scientific contexts. We demonstrate the proposed approach by applying it to an illustrative problem within the realm of near-field radiative heat transfer involving a multilayered hyperbolic metamaterial. We find that a successful generation of spectral data requires two modifications to conventional GANs: (i) the introduction of Wasserstein GANs (WGANs) to avoid mode collapse, and, (ii) the conditioning of WGANs to obtain accurate labels for the generated data. We show that a simple feed-forward neural network (FFNN), when augmented with data generated by a CWGAN, enhances significantly its performance under conditions of limited data availability, demonstrating the intrinsic value of CWGAN data augmentation beyond simply providing larger datasets. In addition, we show that CWGANs can act as a surrogate model with improved performance in the low-data regime with respect to simple FFNNs. Overall, this work highlights the potential of generative machine learning algorithms in scientific applications beyond image generation and optimization

Evaluation of the capabilities of atmospheric pressure chemical ionization source coupled to tandem mass spectrometry for the determination of dioxin-like polychlorobiphenyls in complex-matrix food samples

The use of the novel atmospheric pressure chemical ionization (APCI) source for gas chromatography (GC) coupled to triple quadrupole using tandem mass spectrometry (MS/MS) and its potential for the simultaneous determination of the 12 dioxin-like polychlorobiphenyls (DL-PCBs) in complex food and feed matrices has been evaluated. In first place, ionization and fragmentation behavior of DL-PCBs on the APCI source under charge transfer conditions has been studied followed by their fragmentation in the collision cell. Linearity, repeatability and sensitivity have been studied obtaining instrumental limits of detection and quantification of 0.0025 and 0.005 pg µL-1 (2.5 and 5 fg on column) respectively for every DL-PCB. Finally, application to real samples has been carried out and DL-PCB congeners (PCB 77, 81, 105, 114, 118, 123, 126, 156, 157, 167, 169, 189) have been detected in the different samples in the range of 0.40 to 10000 pg g-1. GC-(APCI)MS/MS has been proved as a suitable alternative to the traditionally accepted confirmation method based on the use of high resolution mass spectrometry and other triple quadrupole tandem mass spectrometry techniques operating with electron ionization. The development of MS/MS methodologies for the analysis of dioxins and DL-PCBs is nowadays particularly important, since this technique was included as a confirmatory method in the present European Union regulations that establish the requirements for the determination of these compounds in food and feed matrices.The authors acknowledge the financial support of Generalitat Valenciana, (research group of excellence PROMETEO/2009/054 and PROMETEO II 2014/023 and Collaborative Research on Environment and Food-Safety (ISIC/2012/016)

Deep learning for the modeling and inverse design of radiative heat transfer

Deep learning is having a tremendous impact in many areas of computer science and engineering. Motivated by this success, deep neural networks are attracting increasing attention in many other disciplines, including the physical sciences. In this work, we show that artificial neural networks can be successfully used in the theoretical modeling and analysis of a variety of radiative-heat-transfer phenomena and devices. By using a set of custom-designed numerical methods able to efficiently generate the required training data sets, we demonstrate this approach in the context of three very different problems, namely (i) near-field radiative heat transfer between multilayer systems that form hyperbolic metamaterials, (ii) passive radiate cooling in photonic crystal slab structures, and (iii) thermal emission of subwavelength objects. Despite their fundamental differences in nature, in all three cases we show that simple neural-network architectures trained with data sets of moderate size can be used as fast and accurate surrogates for doing numerical simulations, as well as engines for solving inverse design and optimization in the context of radiative heat transfer. Overall, our work shows that deep learning and artificial neural networks provide a valuable and versatile toolkit for advancing the field of thermal radiatio

Tunable Thermal Emission of Subwavelength Silica Ribbons

The thermal properties of individual subwavelength objects, which defy Planck’s law, are attracting significant fundamental and applied interest in different research areas. Special attention has been devoted to anisotropic structures made of polar dielectrics featuring thicknesses smaller than both the thermal wavelength and the skin depth. Recently, a novel experimental technique has enabled the measurement of the thermal emissivity of anisotropic SiO2 nanoribbons (with thicknesses on the order of 100 nm), demonstrating that their emission properties can be largely tuned by adjusting their dimensions. However, despite the great interest aroused by these results, their rigorous theoretical analysis has remained elusive due to the computational challenges arising from the vast difference in the length scales involved in the problem. In this work, we present a systematic theoretical analysis of the thermal emission properties of these dielectric nanoribbons based on simulations within the framework of fluctuational electrodynamics carried out with the boundary element method implemented in the SCUFF-EM code. In agreement with the experiments, we show that the emissivity of these subwavelength structures can be largely tuned and enhanced over the thin-film limit. We elucidate that the peculiar emissivity of these nanoribbons is due to the very anisotropic thermal emission that originates from the phonon polaritons of this material and the properties of the waveguide modes sustained by these dielectric structures. Our work illustrates the rich thermal properties of subwavelength objects, as well as the need for rigorous theoretical methods that are able to unveil the complex thermal emission phenomena emerging in this class of systemsJ.J.G.E. was supported by the Spanish Ministry of Science and Innovation through an FPU grant (FPU19/05281). J.B.A. acknowledges financial support from the Ministerio de Ciencia, Innovacioń y Universidades (RTI2018-098452-B-I00). J.C.C. acknowledges funding from the Spanish Ministry of Science and Innovation (PID2020-114880GB-I00