Increasing the production of high-quality graphene nanosheet powder: The impact of electromagnetic shielding of the reaction chamber on the TIAGO torch plasma approach

Microwave-induced plasmas (MIPs), and specifically the TIAGO device (Torche à Injection Axiale sur Guide d’Ondes), offer a streamlined, cost-effective, and environmentally friendly technique for producing high-quality graphene powder in a reaction chamber by a single-step process through ethanol decomposition. To optimize graphene synthesis process, a pivotal move involves minimizing energy dissipation through radiation to maximize the available microwave energy input. Including a metallic shielding around the reaction chamber, essentially creating a Faraday cage, is proposed. The shielding strategy prevents radiation losses and results in a remarkable increase in solid material formation up to 22.8 %. This value, along with the emitted gases proportions and plasma volume increase, shows a correlation with conditions associated with higher input power. Crucially, the shielding of the reaction chamber does not modify graphene growth kinetics in the plasma, as confirmed by Optical Emission Spectroscopy. The synthesized material undergoes a thorough examination, employing diverse techniques like Raman spectroscopy, electron microscopy, X-ray photoelectron spectroscopy, thermogravimetric analysis, and Brunauer-Emmett-Teller (BET) analysis. These analyses underscore a consistent quality of graphene, unaffected by the shielding implementation. Therefore, electromagnetic shielding of the TIAGO torch discharge not only leads to a remarkable increase in solid material formation, thus energy yield, but does so without compromising the intrinsic properties and quality of the synthesized graphene

Clinical and epidemiological approach to delirium in an acute care unit: a cross-sectional study

During hospital admissions, the union of various factors, those related to acute pathology, dependency conditions, cognitive impairment, change of habitual environment, and others, can cause delirium. Acute delirium in the elderly (ADE) occurs in around a third of patients over 70 years of age. The syndrome generates serious complications that increase hospital morbidity and mortality and a high cost for the health administration. This study aimed to determine the clinical and epidemiological profile of ADE in an internal medicine unit. A descriptive cross-sectional study was carried out using a convenience test. A total of 356 patients participated between September and November 2021. Sociodemographic variables, predisposing and precipitating factors of ADE, methods of action against ADE, and the impact on functional and cognitive deterioration were analyzed. A total of 35.1% of the patients developed ADE, mostly of the hyperactive type and of nocturnal appearance. ADE was mainly treated with psychoactive drugs and 22% required mechanical restraint, with non-pharmacological preventive strategies, support, and caregiver training being the main tools for controlling ADE during hospital admission

Role of age and comorbidities in mortality of patients with infective endocarditis

[Purpose]: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. [Methods]: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups:<65 years,65 to 80 years,and ≥ 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. [Results]: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 ≥ 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients ≥80 years who underwent surgery were significantly lower compared with other age groups (14.3%,65 years; 20.5%,65-79 years; 31.3%,≥80 years). In-hospital mortality was lower in the <65-year group (20.3%,<65 years;30.1%,65-79 years;34.7%,≥80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%,≥80 years; p = 0.003).Independent predictors of mortality were age ≥ 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI ≥ 3 (HR:1.62; 95% CI:1.39–1.88),and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared,the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. [Conclusion]: There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group

A CLASSIFIER SYSTEM USING SMOOTH GRAPH COLORING

Unsupervised classifiers allow clustering methods with less or no human intervention. Therefore it is desirable to group the set of items with less data processing. This paper proposes an unsupervised classifier system using the model of soft graph coloring. This method was tested with some classic instances in the literature and the results obtained were compared with classifications made with human intervention, yielding as good or better results than supervised classifiers, sometimes providing alternative classifications that considers additional information that humans did not considered

SC: A NOVEL FUZZY CRITERION FOR SOLVING ENGINEERING AND CONSTRAINED OPTIMIZATION PROBLEMS

In this paper a novel fuzzy convergence system (SC) and its fundamentals are presented. The model was implemented on a monoobjetive PSO algorithm with three phases: 1) Stabilization, 2) generation and breadth-first search, and 3) generation and depth-first. The system SC-PSO-3P was tested with several benchmark engineering problems and with several CEC2006 problems. The computing experience and comparison with previously reported results is presented. In some cases the results reported in the literature are improved

Analysis and Characterization of the Spread of COVID-19 in Mexico through Complex Networks and Optimization Approaches

This work analyzes and characterizes the spread of the COVID-19 disease in Mexico, using complex networks and optimization approaches. Specifically, we present two methodologies based on the principle of the rupture for the GC and Newton's law of motion to quantify the robustness and identify the Mexican municipalities whose population causes a fast spread of the SARS-CoV-2 virus. Specifically, the first methodology is based on several characteristics of the original version of the Vertex Separator Problem (VSP), and the second is based on a new mathematical model (NLM). By solving VSP, we can find nodes that cause the rupture of the giant component (GC). On the other hand, solving the NLM can find more influential nodes for the entire system’s development. Specifically, we present an analysis using a coupled social network model with information about the main characteristics of the contagion and deaths caused by COVID-19 in Mexico for 19 months (January 2020–July 2021). This work aims to show through the approach of complex networks how the spread of the disease behaves, and, thus, researchers from other areas can delve into the characteristics that cause this behavior

Sex Classification via 2D-Skeletonization

Sex classification is a challenging open problem in computer vision. It is useful from statistics up to people recognition on surveillance video. So far, the best performance can be achieved by using 3D cameras, but this approach requires the use of some especial hardware. Other 2D approaches achieve good results on normal situations but fail when the person wears loose clothing and carries bags or the camera angle changes as they rely on calculating borders, silhouettes, or the energy of the person in the image. This work aims to provide a novel sex classification methodology based on the creation of a virtual skeleton for each individual from 2D images and video; then, the distances between some points of the skeleton are measured and work as input of a sex classifier. This improves the results since clothing, bags, and the camera angle affect little the skeletonization process