Solid particle erosion and viscoelastic properties of thermoplastic polyurethanes

The wear resistance of several thermoplastic polyurethanes (TPUs) having different chemical nature and micronscale arrangement of the hard and soft segments has been investigated by means of erosion and abrasion tests. The goal was correlating the erosion performances of the materials to their macroscopic mechanical properties. Unlike conventional tests, such as hardness and tensile measurements, viscoelastic analysis proved to be a valuable tool to study the erosion resistance of TPUs. In particular, a strict correlation was found between the erosion rate and the high-frequency (~10^7 Hz) loss modulus. The latter reflects the actual ability of TPU to dissipate the impact energy of the erodent particles

Antiferromagnetic phase of the gapless semiconductor V3Al

Discovering new antiferromagnetic compounds is at the forefront of developing future spintronic devices without fringing magnetic fields. The antiferromagnetic gapless semiconducting D03 phase of V3Al was successfully synthesized via arc-melting and annealing. The antiferromagnetic properties were established through synchrotron measurements of the atom-specific magnetic moments, where the magnetic dichroism reveals large and oppositely-oriented moments on individual V atoms. Density functional theory calculations confirmed the stability of a type G antiferromagnetism involving only two-third of the V atoms, while the remaining V atoms are nonmagnetic. Magnetization, x-ray diffraction and transport measurements also support the antiferromagnetism. This archetypal gapless semiconductor may be considered as a cornerstone for future spintronic devices containing antiferromagnetic elements.Comment: Accepted to Physics Review B on 02/23/1

ImageJ2: ImageJ for the next generation of scientific image data

ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software's ability to handle the requirements of modern science. Due to these new and emerging challenges in scientific imaging, ImageJ is at a critical development crossroads. We present ImageJ2, a total redesign of ImageJ offering a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. ImageJ2 provides a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs

A conceptual framework for machine learning algorithm selection for predictive maintenance

The Industry 4.0 paradigm enables advanced data-driven decision-making processes leading many manufacturers to a digital transformation. Within this context, Predictive Maintenance (PdM) - i.e. a maintenance strategy that predicts failures in advance - based on Machine Learning (ML) - i.e. a set of algorithms to analyze data for pattern recognition - emerged as one of the most prominent data-driven analytical approaches for maximizing availability and efficiency of industrial systems. Indeed, there exists a considerable body of literature dealing with ML-based PdM where a wide set of ML algorithms has been applied to a broad range of industrial settings. Whilst this results in extensive knowledge on the topic, the need to choose the right algorithm for a specific task arises as a challenging issue since it is considered an essential stage in the development and implementation of an ML-oriented approach. To respond to such a necessity, this work proposes a conceptual framework to guide practitioners as well as non-expert users in ML algorithm selection for PdM issues. The aim is to provide a set of guidelines and recommendations for the identification of which ML techniques are likely to achieve valuable performance for specific tasks or datasets. First, the most commonly applied ML algorithms in PdM are analyzed together with their core characteristics, advantages, and disadvantages. Then, several decision variables depending on dataset and ML characteristics, learning objectives, accuracy and interpretability are considered. Finally, illustrative case studies are presented to demonstrate how the proposed framework can be adopted in real industrial applications

Bidimensional ray tracing model for the underwater noise propagation prediction

An increasing attention has recently been paid to the effect of the underwater noise field generated by ship activities on the marine environment. Although this problem is widely discussed in international treaties and conventions, it has not yet found a consolidated technical-scientific treatment capable of quantifying the level of underwater noise emissions produced by naval systems. As part of a national research collaboration, a novel code has been developed to predict noise propagation according to the Ray Tracing approach. Such optical geometry-based technique allows for calculating the Transmission Loss (TL) trend in its respective contributions: geometrical loss (due to the distance between the source and receiver), dissipation loss (due to the characteristics of the propagation environment), and reflection loss (due to the surfaces that delimit the field). The simulation requires as input parameters the source info as spatial position, frequency, and sound pressure level (SPL) as well as the sea properties like seabed depth, the speed of sound profile, the layers thickness the water column is divided into, the sea salinity, temperature, and pH. The simulation code provides the SPL spatial distribution useful as a fast industrial tool in the future studies addressed to identify the emission limits for the protection of marine wildlife

Weakly Coupled Motion of Individual Layers in Ferromagnetic Resonance

We demonstrate a layer- and time-resolved measurement of ferromagnetic resonance (FMR) in a Ni81Fe19 / Cu / Co93Zr7 trilayer structure. Time-resolved x-ray magnetic circular dichroism has been developed in transmission, with resonant field excitation at a FMR frequency of 2.3 GHz. Small-angle (to 0.2 degree), time-domain magnetization precession could be observed directly, and resolved to individual layers through elemental contrast at Ni, Fe, and Co edges. The phase sensitivity allowed direct measurement of relative phase lags in the precession oscillations of individual elements and layers. A weak ferromagnetic coupling, difficult to ascertain in conventional FMR measurements, is revealed in the phase and amplitude response of individual layers across resonance.Comment: 22 pages, 6 figures submitted to Physical Review

Oxidative fermentation of glucose and ethanol in designed media and cooked grape must by acetic acid bacteria

In this study, acetic acid bacteria strains were investigated for their ability to oxidize different carbon sources producing the corresponding oxidative products. Bacterial strains were cultivated in seven designed media and their acetification ability was assessed. The most performing strains were further tested to evaluate gluconic acid production in cooked grape must. Organic acids, sugars, and ethanol concentrations were assayed by high-pressure liquid chromatography. Overall, the findings showed high variability amongst strains of the same species, especially amongst Gluconobacter oxydans strains. However, strains ATCC 621H and DSM 3503T resulted to be the highest gluconic acid producers in all tested conditions. This study shows that grape must can be further valorized by selective fermentations for the production of gluconic based products

Direct dynamics of 2D cable-driven parallel robots including cables mass effect and its influence in the control performance

Cable-driven parallel robots are a type of parallel manipulators where rigid links are replaced by actuated cables. Although in many cases dynamic models that neglect the cables mass and elasticity are employed to simulate the robot behavior and test the control approach to be used, there are several situations in which their effect cannot be disregarded, especially when large span cables are used, and the cable mass density generates important cable sagging. This work proposes a dynamic model for planar cable-driven parallel robots with 3 degrees-of-freedom considering cables mass and elasticity. Furthermore, the effect of using control approaches based on massless inelastic cables dynamic models on robots with non-negligible cable mass and elasticity is finally assessed

Niveles de monóxido de carbono en el aire urbano de San Miguel de Tucumán, Argentina

Carbon monoxide, a highly toxic gas, is one of the most common and widely distributed air pollutants; in San Miguel de Tucumán, Argentina, the main sources of emission are the vehicle engines and the fire produced by the burning of cane fields. The objective was to evaluate the local levels of carbon monoxide (CO) concentration in the urban air of San Miguel de Tucumán during a period of 13 years. Four monitoring stations with high population concentration were selected. Testo 625 measuring instruments with specific probe for CO were used. The measure-ments were made during the months of September, October and November during the period 2003-2015. The concentrations of CO were compared using Variance analysis of Kruskal & Wal-lis and Conover post-test at 5 % significance. Minimum, maximum and Percentiles values (P25, P50 and P75) were considered. Results: No significant differences were observed in annual CO concentrations (p = 0.7177), with min = 24ppm, P25 = 28 ppm, P50 =28.9 ppm, P75 = 32 ppm and maximum = 38 ppm. With significant differences in CO concentrations (p <0.001) according to month and sampling season, higher values were detected in the month of November in Station 4 with min=27.0ppm, P25=30.7 ppm, P50=31.0 ppm, P75=33.2 ppm and maximum=38.0 ppm. Conclusion: The City of San Miguel de Tucumán is located in a mountainous area with little air movement, which means it has a notable accumulation of atmospheric pollutants, with high concentrations of CO in the microcenter of this city.El monóxido de carbono (CO), gas altamente tóxico, es uno de los contaminantes atmosféricos más comunes y ampliamente distribuidos. En San Miguel de Tucumán, Argentina, las principa-les fuentes de emisión son los motores de vehículos y los incendios producidos por la quema de cañaverales. El objetivo de la presente investigación fue evaluar los niveles locales de concen-tración de CO en el aire urbano de San Miguel de Tucumán durante un período de trece años. Se seleccionaron cuatro estaciones de monitoreo con alta concentración poblacional. Se emplearon instrumentos de medición Testo 625, con sonda específica para CO. Las mediciones se realizaron en los meses de septiembre, octubre y noviembre durante el período 2003-2015. Las concen-traciones de CO se compararon empleando análisis de la varianza de Kruskal-Wallis y postest de Conover al 5 % de significación. Se consideraron valores mínimos, máximos y percentiles P25, P50 y P75. Resultados: No se observaron diferencias significativas en las concentraciones anuales de CO (p = 0,7177), con min = 24 ppm, P25 = 28 ppm, P50 = 28,9 ppm, P75 = 32 ppm y máximo = 38 ppm. Con diferencias significativas en las concentraciones de CO (p < 0,001), se-gún mes y estación de muestreo. Los mayores valores fueron detectados en el mes de noviembre en Estación 4, con min = 27,0 ppm, P25 = 30,7 ppm, P50 = 31,0 ppm, P75 = 33,2 ppm y máximo = 38,0 ppm. La ciudad de San Miguel de Tucumán se encuentra en zona montañosa, con escaso movimiento de aire, lo que determina una marcada acumulación de contaminantes atmosféricos, con concentraciones de CO elevadas en el microcentro de esta ciudad.Biotecnologí