Modelling attitudes to climate change — an order effect and a test between alternatives

© Springer International Publishing Switzerland 2015. Quantum-like models can be fruitfully used to model attitude change in a social context. Next steps require data, and higher dimensional models. Here, we discuss an exploratory study that demonstrates an order effect when three question sets about Climate Beliefs, Political Affiliation and Attitudes Towards Science are presented in different orders within a larger study of n = 533 subjects. A quantum-like model seems possible, and we propose a new experiment which could be used to test between three possible models for this scenario

Chemical and stable isotope composition (18O/16O, 2H/1H) of formation waters from the Carabobo Oilfield, Venezuela

In this short note, we present the first data on stable isotope composition of the oilfield waters from Carabobo area of the Faja Petrolífera del Orinoco “Hugo Chávez” (Orinoco Oil Belt). From a chemical point of view, the formation waters show a main Na-Cl level (TDS up to 30g/l) with a dilution trend toward Na-HCO3 composition (down to 1g/l). Until now, such a clear net chemical compositional trend was ascribed to a meteoric dilution (fresh/ brackish bicarbonate) of the seawater endmember (the saltiest chloride). The isotope results of this study reveal that the seawater mother water was modified during a high-temperature thrusting event (120–125°C), forming 18O-enriched diagenetic water (up to +4‰), which was diluted in recent times by glacial meltwater and presentday meteoric water. The hypothetical presence of flood by a meteoric paleo-water also offers new hints to explain the low API gravity (<10°API biodegraded, extra heavy oil) and composition of the local crude

APPLICATION OF HEATING MICROSCOPY ON SINTERING AND MELTING BEHAVIOUR OF NATURAL SANDS OF ARCHAEOLOGICAL INTEREST

In antiquity, beach sand was one of the main raw materials for glass-making and for the production of other vitreous materials, like Egyptian blue and faience. During the 1st century AD, glass and pigments manufacturing industry was active along the Gulf of Naples, Italy, where we sampled four littoral sands. Samples were analyzed with different techniques: chemical analysis was performed by means of X-Ray Fluorescence (XRF) and mineralogical analyses with X-Ray Powder Diffraction (XRPD) and Raman Spectroscopy. The complete sintering to melting thermal behaviour of the four sands was studied by heating microscopy or hot-stage microscope (HSM) equipped with an high resolution camera capable to collect sample profile during heating. The effect of the grain size on the sintering curves, which were automatically elaborated by specimen profile transformation, was also investigated. Finally, some deductions about the granulometry effect and the presence of alkaline and alkaline-earth oxides on sintering and melting behaviour were drawn. All the four sands were found suitable for highly sintered manufacts rather than glasses, to reach complete amorphous materials the addition of fluxes was necessary

Versatile and non-cytotoxic GelMA-xanthan gum biomaterial ink for extrusion-based 3D bioprinting

Extrusion-based 3D bioprinting allows the 3D printing of bioinks, composed of cells and biomaterials, to mimic the complex 3D hierarchical structure of native tissues. Successful 3D bioprinting requires bioinks with specific properties, such as biocompatibility, printability, and biodegradability according to the desired application. In the present work, we aimed at developing a new versatile blend of gelatin methacryloyl-xanthan gum (GelMA-XG) suitable for extrusion-based 3D bioprinting with a straightforward process. To this end, we first optimized the process of gelatin methacryloyl (GelMA) synthesis by investigating the impact of different buffer solutions on the degree of functionalization, swelling degree, and degradation rate. The addition of xanthan gum (XG) enabled further tuning of biodegradability and an improvement of GelMA printability. Specifically, an optimal concentration of XG was found through rheological characterization and printability tests. The optimized blend showed enhanced printability and improved shape fidelity as well as its degradation products turned out to be non-cytotoxic, thus laying the foundation for cell-based applications. In conclusion, our newly developed biomaterial ink is a promising candidate for extrusion-based 3D bioprinting

Prediction of Displacements in Unstable Areas Using a Neural Model

In pipeline management the accurate prediction of weak displacements is a crucial factor in drawing up a prevention policy since the accumulation of these displacements over a period of several years can lead to situations of high risk. This work addresses the specific problem related to the prediction of displacements induced by rainfall in unstable areas, of known geology, and crossed by underground pipelines. A neural model has been configured which learns of displacements from instrumented sites (where inclinometric measurements are available) and is able to generalise to other sites not equipped with inclinometers

Quantization-Aware NN Layers with High-throughput FPGA Implementation for Edge AI

Over the past few years, several applications have been extensively exploiting the advantages of deep learning, in particular when using convolutional neural networks (CNNs). The intrinsic flexibility of such models makes them widely adopted in a variety of practical applications, from medical to industrial. In this latter scenario, however, using consumer Personal Computer (PC) hardware is not always suitable for the potential harsh conditions of the working environment and the strict timing that industrial applications typically have. Therefore, the design of custom FPGA (Field Programmable Gate Array) solutions for network inference is gaining massive attention from researchers and companies as well. In this paper, we propose a family of network architectures composed of three kinds of custom layers working with integer arithmetic with a customizable precision (down to just two bits). Such layers are designed to be effectively trained on classical GPUs (Graphics Processing Units) and then synthesized to FPGA hardware for real-time inference. The idea is to provide a trainable quantization layer, called Requantizer, acting both as a non-linear activation for neurons and a value rescaler to match the desired bit precision. This way, the training is not only quantization-aware, but also capable of estimating the optimal scaling coefficients to accommodate both the non-linear nature of the activations and the constraints imposed by the limited precision. In the experimental section, we test the performance of this kind of model while working both on classical PC hardware and a case-study implementation of a signal peak detection device running on a real FPGA. We employ TensorFlow Lite for training and comparison, and use Xilinx FPGAs and Vivado for synthesis and implementation. The results show an accuracy of the quantized networks close to the floating point version, without the need for representative data for calibration as in other approaches, and performance that is better than dedicated peak detection algorithms. The FPGA implementation is able to run in real time at a rate of four gigapixels per second with moderate hardware resources, while achieving a sustained efficiency of 0.5 TOPS/W (tera operations per second per watt), in line with custom integrated hardware accelerators

Understanding the origin and mixing of deep fluids in shallow aquifers and possible implications for crustal deformation studies. San Vittorino plain, Central Apennines

Expanding knowledge about the origin and mixing of deep fluids and the water–rock–gas interactions in aquifer systems can represent an improvement in the comprehension of crustal deformation processes. An analysis of the deep and meteoric fluid contributions to a regional groundwater circulation model in an active seismic area has been carried out. We performed two hydrogeochemical screenings of 15 springs in the San Vittorino Plain (central Italy). Furthermore, we updated the San Vittorino Plain structural setting with a new geological map and cross-sections, highlighting how and where the aquifers are intersected by faults. The application of Na-Li geothermometers, coupled with trace element and gas analyses, agrees in attributing the highest temperatures (>150◦C), the greatest enrichments in Li (124.3 ppb) and Cs (>5 ppb), and traces of mantle-derived He (1–2%) to springs located in correspondence with high-angle faults (i.e., S5, S11, S13, and S15). This evidence points out the role of faults acting as vehicles for deep fluids into regional carbonate aquifers. These results highlight the criteria for identifying the most suitable sites for monitoring variations in groundwater geochemistry due to the uprising of deep fluids modulated by fault activity to be further correlated with crustal deformation and possibly with seismicity

Whey protein supplement adulteration with rice flour quantification: A simple method using ATR-FT-MIR and iPLS

In this work, a method using ATR-FT-MIR and iPLS was developed to quantify whey protein supplement adulteration with rice flour. The original vanilla flavor commercial whey protein samples were adulterated with commercial rice flour with concentrations between 11.49% to 29.14% (w/w). After the adulteration, the ATR-FT-MIR spectra were obtained with no additional preparation procedure. The iPLS model analysis was performed using RStudio software with the mdatools package. The RMSEC was 1.26, the R2= 0.954 and the cross-validation error (RMSECV) was 3.31. The prediction error (RMSEP) for the validation set was equal 3.48 and the validation R2 was 0.610. These parameters, associated with the fact that the method does not require sample preparation, demonstrate the procedure viability as a tool to quantify adulterations of whey protein with rice flour

Effects of extracellular fiber architecture on cell membrane shear stress in a 3D fibrous matrix

Interstitial fluid flow has been shown to affect the organization and behavior of cells in 3D environments in vivo and in vitro, yet the forces driving such responses are not clear. Due to the complex architecture of the extracellular matrix (ECM) and the difficulty of measuring fluid flow near cells embedded in it, the levels of shear stress experienced by cells in this environment are typically estimated using bulk-averaged matrix parameters such as hydraulic permeability. While this is useful for estimating average stresses, it cannot yield insight into how local matrix fiber architecture-which is cell-controlled in the immediate pericellular environment-affects the local stresses imposed on the cell surface. To address this, we used computational fluid dynamics to study flow through an idealized mesh constructed of a cubic lattice of fibers simulating a typical in vitro collagen gel. We found that, in such high porosity matrices, the fibers strongly affect the flow fields near the cell, with peak shear stresses up to five times higher than those predicted by the Brinkman equation. We also found that minor remodeling of the fibers near the cell surface had major effects on the shear stress profile on the cell. These findings demonstrate the importance of fiber architecture to the fluid forces on a cell embedded in a 3D matrix, and also show how small modifications in the local ECM can lead to large changes in the mechanical environment of the cell