New model to predict survival in advanced pancreatic ductal adenocarcinoma patients by measuring GGT and LDH levels and monocyte count.

Introduction: Pancreatic ductal adenocarcinoma (PDAC) is a lethal cancer with a poor survival outcome. Predicting patient survival allows physicians to tailor treatments to specific individuals. Thus, a simple and cost-effective prognosis model is sorely needed.

Wine corks decontamination using plasma activated water.

Cork taint provides off-odors and changes negatively wine composition. In fact, it is one of the most important causes of discarding bottled wine. 2,4,6-Trichloroanisole (TCA) is the most known molecule responsible of that problem.

Confounders contributing to explain the association between sex and disease impact in patients with recent-onset psoriatic arthritis.

Objectives: To evaluate the effect of potential confounders on the association between sex and disease impact in recent-onset psoriatic arthritis.

Methods: We performed a multicentre observational prospective study (2-year follow-up, regular annual visits). The study population comprised patients aged ≥18 years who fulfilled the CASPAR criteria and less than 2 years since the onset of symptoms.

Quantum cyber-physical systems.

This paper aims to promote a quantum framework that analyzes Industry 4.0 cyber-physical systems more efficiently than traditional simulations used to represent integrated systems. The paper proposes a novel configuration of distributed quantum circuits in multilayered complex networks that enable the evaluation of industrial value creation chains.

Development and characterization of anti-biofilm coatings applied by Non-Equilibrium Atmospheric Plasma on stainless steel.

Biofilm-mediated microbial persistence of pathogenic and spoilage bacteria is a serious problem in food industries. Due to the difficulty of removing mature biofilms, great efforts are being made to find new strategies to prevent bacterial adherence to surfaces, the first step for biofilm development. In this study, coatings of (3-aminopropyl)triethoxysilane (APTES), tetraethyl orthosilicate (TEOS) and acrylic acid (AA) were applied by Non-Equilibrium Atmospheric Plasma on stainless steel (SS) AISI 316, the SS most commonly used in food industry equipment.

Improvement of Quantum Approximate Optimization Algorithm for Max-Cut Problems.

The objective of this short letter is to study the optimal partitioning of value stream networks into two classes so that the number of connections between them is maximized. Such kind of problems are frequently found in the design of different systems such as communication network configuration, and industrial applications in which certain topological characteristics enhance value-stream network resilience. The main interest is to improve the Max-Cut algorithm proposed in the quantum approximate optimization approach (QAOA), looking to promote a more efficient implementation than those already published.

Transcending Conventional Biometry Frontiers: Diffusive Dynamics PPG Biometry.

This paper presents the first photoplethysmographic (PPG) signal dynamic-based biometric authentication system with a Siamese convolutional neural network (CNN). Our method extracts the PPG signal's biometric characteristics from its diffusive dynamics, characterized by geometric patterns in the (p,q)-planes specific to the 0-1 test. PPG signal diffusive dynamics are strongly dependent on the vascular bed's biostructure, unique to each individual.

Industry 4.0 Quantum Strategic Organizational Design Configurations. The Case of 3 Qubits: One Reports to Two.

In this work we explore how the relationship between one subordinate reporting to two leaders influences the alignment of the latter with the company's strategic objectives in an Industry 4.0 environment. We do this through the implementation of quantum circuits that represent decision networks.

Antibiofilm coatings through atmospheric pressure plasma for 3D printed surgical instruments.

Recently, medical applications for 3D printing are expanding rapidly and are expected to revolutionize health care, specifically, manufacturing surgical guides and protective face mask against coronavirus (COVID-19). These instruments come in contact with the human tissues, being necessary 3D printed materials free of pathogenic microbes or other contaminants. Therefore, they must be sterilized to avoid that bacteria can attach to the surface and produce biofilm.

Numerical Modeling for Simulation of Compaction of Refractory Materials for Secondary Steelmaking.

The purpose of this work is to simulate the powder compaction of refractory materials, using the discrete element method (DEM). The capability of two cohesive contact models, implemented in different DEM packages, to simulate the compaction of a mixture of two refractory materials (dead burnt magnesia (MgO) and calcined alumina (AlO)) was analyzed, and the simulation results were compared with experimental data. The maximum force applied by the punch and the porosity and final shape quality of the compact were examined.

Comparison of Cohesive Models in EDEM and LIGGGHTS for Simulating Powder Compaction.

The purpose of this work was to analyse the compaction of a cohesive material using different Discrete Element Method (DEM) simulators to determine the equivalent contact models and to identify how some simulation parameters affect the compaction results (maximum force and compact appearance) and computational costs. For this purpose, three cohesion contact models were tested: linear cohesion in EDEM, and simplified Johnson-Kendall-Roberts (SJKR) and modified SJKR (SJKR2) in LIGGGHTS. The influence of the particle size distribution (PSD) on the results was also investigated.

Project ES3: attempting to quantify and measure the level of stress.

Introduction: The WHO has qualified stress as a 'world epidemic' due to its increasingly greater incidence on health. The work described in this paper represents an attempt to objectively quantify the level of stress.

Aim: The aim of the method developed here is to measure how close or how far a subject is from a situation that can be considered 'normal' in medical and social terms.

TAO-robust backpropagation learning algorithm.

In several fields, as industrial modelling, multilayer feedforward neural networks are often used as universal function approximations. These supervised neural networks are commonly trained by a traditional backpropagation learning format, which minimises the mean squared error (mse) of the training data. However, in the presence of corrupted data (outliers) this training scheme may produce wrong models.