Strategies for Communicating with Deaf Patients in the Public Physical Rehabilitation Unit in the Central South Region of Rio de Janeiro State, Brazil

Deaf people face communication difficulties in health units because of the lack of preparedness of health care professionals to deal with these patients. Objective: To identify how physical rehabilitation professionals associated with a regional public unit in the south central region of Rio de Janeiro State, Brazil interact with deaf patients. Method: Data were analyzed through descriptive statistics in the Statistical Package for Social Sciences (SPSS) software version 18. Results: Results showed that: 15.1% (5) of the team members did not know the Brazilian Sign Language (Libras) and required help in their interaction with deaf patients;12.1% (4) never assisted deaf patients;18.2% (6) used writing and gestures;21.2% (7) resorted to lip-reading or gestures;3.1% (1) spoke slowly;6.1% (2) used mime;15.1% (5) used the Brazilian Sign Language;9.1% (3) did not respond. Conclusion: Most of these professionals are not prepared to satisfactorily interact with deaf patients. They use improvised strategies to assist the deaf clientele, demonstrating that the planning and implementation of regular courses in sign language are fundamental in order to assist these patients effectively.

An Educational GUI-Based Software for Dynamic Analysis of Framed Structural Models

This paper introduces a new version of the open-source educational software, LESM (Linear Elements Structure Model), developed in MATLAB for structural analysis of one-dimensional models such as frames, trusses, and grillages. The updated program includes dynamic analysis, which incorporates inertial and damping effects, time-dependent load conditions, and a transient solver with multiple time integration schemes. The software assumes small displacements and linear-elastic material behavior. The paper briefly explains the theoretical basis for these developments and the reorganization of the source code using Object-Oriented Programming (OOP). The updated Graphical User Interface (GUI) allows interactive use of dynamic analysis features and displays new results such as animations, envelope diagrams of internal forces, phase portraits, and the response of degrees-of-freedom in time and frequency domain. The new version was used in a structural dynamics course, and new assignments were elaborated to improve students’ understanding of the subject.

Influence of variables related to soil weathering on the geomechanical performance of tropical soils

This paper presents an experimental and analytical investigation of the influence of variables related to soil weathering on the geomechanical performance of sand-silt mixtures containing lateritic soils,i.e.intensely weathered tropical soils with the influence of interparticle bonding.The sand-silt mixtures containing different relative proportions between uniform sand and lateritic soil were produced,and geomechanical soil characterization tests were performed.Based on the results,a transition from a primarily coarse-to a fine-grained prevailing soil structure was found to cause considerable impact on the geomechanical performance of these soils,as evidenced by design variables related to soil mineralogy and size distribution characteristics.Specifically,fines contents of both individual soil particles and soil aggregations were found to correlate with experimental results,while the relative proportion between sesquioxides(aluminum,and iron oxides),and silica,i.e.sesquioxide-silica ratios(SSR^(-1)),facilitated estimates concerning changes in geomechanical performance.Finally,the application of the sandsilt mixtures containing lateritic soil on soil walls reinforced with polymeric strips was also evaluated,further emphasizing the potential advantages of adopting variables related to soil weathering on design guidelines concerning tropical soils.

Zika Virus Controversies: Epidemics as a Legacy of Mega Events?

The current knowledge of the Zika Virus epidemic clearly lacks a comprehensive understanding of its determinants and clinical outcomes. Until recently regarded as a “simple” dengue-like infection, it nowadays turned into a real challenge to Public Health around the world. The Zika Epidemic shows a quick spread, affects unprepared health systems, and presents with severe neurological complications of newborns—a concrete threat to pregnancies. This re-emerging infectious disease is a source of deep doubts and harsh debates regarding Public Health and even bioethical issues. Several doubts still remain on how to deal with the various possible transmission ways of the disease, the surge of a generation of thousands of microcephalic newborns (and questions on how to handle them within limited health systems), and severe malformations concentrated so far in Northeastern Brazil. Finally, a debate is raised about how the “Endemic State” of Brazil, deeply merged in a health care crisis for almost 40 years now, currently confronts 3 current epidemics of the arboviruses Dengue, Chikungunya and now Zika, all associated with a lack of basic sanitation. Brazil must deal with these biological threats in the context of the Rio 2016 Olympic Games, since Chikungunya has probably been brought to Brazil during the FIFA Soccer World Cup (2014) and Zika Virus spread is associated with the Soccer Confederations Cup (2013).

Optimization Techniques and Development of Neural Models Applied in Biosurfactant Production by <i>Bacillus subtilis</i>Using Alternative Substrates

Bacillus subtilis was investigated as production of biosurfactant using a combination based on waste of candy industry and glycerol from biodiesel production process as only substrate. The experimental design chosen for optimization by response surface methodology was a central composite rotatable design (CCRD) and dry weight (DW) and crude biosurfactant (CB) concentrations were selected as responses in analysis. Two techniques were implemented response surface methodology (RSM) and artificial neural network (ANN). First challenge of study was to assess the effects of the interactions between variables and reach optimum values. With the CCRD results, RSM and ANN models were developed, optimizing the production of biosurfactant. The correlation coefficients (R2) of RSM models explained 88% for DW and 73% for CB of the interactions among substrate concentrations, while ANN models explained 99% for DW and 98% for CB, demonstrating that developed ANN models were more accurate and consistent in predicting optimized conditions than RSM model. The maximum DW and CB produced in the optimum conditions were 25.60 ± 5.0 g/L and 668 ± 40 mg/L, respectively. The crude biosurfactant also showed applications in cases of oil spreading in water due to clear zone produced in Petri dishes assays.

Determination of a U-Notch Stress Concentration Factor Using Thermoelasticity

This paper uses the TSA （therrnoelastic stress analysis） technique to determine the stress concentration factor （Kt） of a U-notch in an aluminum plate, and then compares the results with those obtained from a FEA （finite elements analysis） of the same specimen. In order to do so, it devises a calculation procedure to extrapolate the thermoelastic data near the tip of the notch and then applies the resulting algorithm to seven distinct experiments that had different loading frequencies, mean loads and load ranges. The overall positive results suggest that the technique may be suitable for Kt measurements in real-world structures. A discussion about the calibration factor of the thermoelastic data is included by confronting the calibration results using independent tensile uniaxial tests and using the U-notch TSA and FEA paired specimen data.

Psychological Impacts of COVID-19 Pandemic on the Brazilian Population: Occupational Analysis

Context and objectives :The unexpected pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affected several activities and exposed workers, students and the large mass of unemployed people to an economic and psychological pressure that could lead to various problems, such as anxiety, fear, depression and insomnia, consequently causing impairment of quality of life. Method: A cross-sectional study was conducted using the COVID-19 Peri-Traumatic Distress Index (CPDI) and the Fear of COVID-19 Scale (FCV-19S) to measure peri-traumatic stress and fear, such as psychological reactions during the 2019 coronavirus disease (COVID-19) pandemic. An online spreadsheet was used as a data collection tool to send the questionnaire and scales to a sample of 1,844 participants. Data were analyzed in groups of health professionals, students and teachers, and an analysis was performed between unemployed and employed people in order to evaluate the impact of employment on the population’s mental health. Results: All groups presented from moderate to high CPDI and FCV-19S scores, and students and unemployed people had higher levels of stress and fear. Conclusion: COVID-19 pandemic had a significant impact on the Brazilian population, with students and the unemployed showing the highest rates of stress and fear.

Evaluation of Fear and Peritraumatic Distress during COVID-19 Pandemic in Brazil

COVID-19 pandemic continues to spread exponentially worldwide, especially in America. At the end of August 2020, Brazil is one of the most affected countries with more than three and a half million cases and up to 114,250 deaths. This study aims to assess the fear and peri-traumatic stress during the COVID-19 pandemics in Brazil, to enhance infection control methods, appropriate interventions, and public health policies. A cross-sectional survey has been conducted from April 12th to 18th using the Peri-Traumatic Distress Scale (CPDI) and the Fear Scale (FCV-19S) aiming to measure the peri-traumatic stress and fear as psychological reactions during the COVID-19 pandemic. For that purpose, an online spreadsheet was used to send the questionnaire and scales to a sample of 1844 participants as a collecting information tool. Significant positive relationship (r = 0.660, p < 0.001) and internal consistencies on CPDI and FCV-195 scales (Cronbach Alfa scores 0.90 and 0.88 respectively) were shown. Results highlight significant gender differences as in both scales women’s mean scores are higher showing that it is paramount that women’s voices were represented in policy spaces as socially constructed gender roles place them in a strategic position to enhance multi-level interventions (primary and secondary effects of COVID-19), equitable policies, and new approaches to control the pandemic.

An operator methodology for the global dynamic analysis of stochastic nonlinear systems

In a global dynamic analysis,the coexisting attractors and their basins are the main tools to understand the system behavior and safety.However,both basins and attractors can be drastically influenced by uncertainties.The aim of this work is to illustrate a methodology for the global dynamic analysis of nondeterministic dynamical systems with competing attractors.Accordingly,analytical and numerical tools for calculation of nondeterministic global structures,namely attractors and basins,are proposed.First,based on the definition of the Perron-Frobenius,Koopman and Foias linear operators,a global dynamic description through phase-space operators is presented for both deterministic and nondeterministic cases.In this context,the stochastic basins of attraction and attractors’distributions replace the usual basin and attractor concepts.Then,numerical implementation of these concepts is accomplished via an adaptative phase-space discretization strategy based on the classical Ulam method.Sample results of the methodology are presented for a canonical dynamical system.

Identifying representative days of solar irradiance and wind speed in Brazil using machine learning techniques

The investment levels in electricity production capacity from variable Renewable Energy Sources have substantially grown in Brazil over the last decades,following the worldwide-seeking-goal of a carbon-neutral economy and the country’s incentives in diversifying its generation mix.From a long-term perspective,the current non-storable capability of renewable energy sources requires an adequate uncertainty characterization over the years.In this context,the main objective of this work is to provide a thorough descriptive analytics of the time-linked hourly-based daily dynamics of wind speed and solar irradiance in the main resourceful regions of Brazil.Leveraging on unsupervised Machine Learning methods,we focus on identifying similar days over the years,Representative Days,that can depict the fundamental underlying behaviour of each source.The analysis is based on a historical dataset of different sites with the highest potential and installed capacity of each source spread over the country:three in the Northeast and one in the South Regions,for wind speed;and three in the Northeast and one in the Southeast Regions,for solar irradiance.We use two Partitioning Methods(𝐾-Means and𝐾-Medoids),the Hierarchical Ward’s Method,and a Model-Based Method(Self-Organizing Maps).We identified that wind speed and solar irradiance can be effectively represented,respectively,by only two representative days,and two or three days,depending on the region and method(segments data with respect to the intensity of each source).Analysis with higher Representative Days highlighted important hidden patterns such as different wind speed modulations and solar irradiance peak-hours along the days.

Numerical and safety considerations about the Daguangbao landslide induced by the 2008 Wenchuan earthquake

The 2008 Wenchuan earthquake resulted in a large number of fatalities and caused significant economic losses.Thousands of landslides,many of which are very large,were triggered by the earthquake.A majority of catastrophic landslides were distributed along the central Longmenshan fault system,at the eastern margin of the Tibetan Plateau.Some of the landslides resulted in sudden damming of rivers causing flooding,which in turn induced secondary sliding disasters.Among the most significant landslides,the Daguangbao landslide was the largest in volume with the maximum thickness.For this,a numerical model of the Daguangbao landslide,using the material point method(MPM),was developed to simulate the interaction of the seismic loads imposed on the slope.The numerical results then are compared with the post-earthquake profile.As a consequence of the landslide,a nearly vertical head scarp with a maximum height of about 700 m was generated.This is considered as a high risk situation that requires constant monitoring and evaluation.Finally,we propose a methodology based on Bayesian networks(BNs)to manage the risk associated with the stability of the rockwall at the Daguangbao landslide site.

Risk assessment of CO_2 injection processes and storage in carboniferous formations:a review

Over the last decades,people from almost all over the world have realized that it is necessary to quickly develop strategies for the control and reduction of greenhouse gases (GHG) emissions.Among various GHGs,carbon dioxide (CO2) is the most abundant GHG.Its underground storage involves less risk and lower levels of dangerousness.The paper briefly describes the most effective technologies available in the market for background processes to storage (capture and transport) CO2,as well as the more secure solutions for its storage,in particular for the geological storage in carboniferous formations.This paper also outlines the methodologies for the risk assessment involved in storage of CO2,with a particular focus on cases where the injection is made into unminable coal seams and in abandoned coalmines.Methodologies used for risk analysis are described in detail with particular emphasis on Bayesian network (BN).Some applications regarding the risk assessment of CO2 injection processes and CO2 storage in carboniferous formations and contamination of aquifers are presented and analyzed.Finally,based on the applications of BN,several conclusions are drawn.

Mechanical characterization of sisal reinforced cement mortar

This work aims at evaluating the mechanical behavior of sisal fiber reinforced cement mortar. The composite material was produced from a mixture of sand, cement, and water. Sisal fibers were added to the mixture in different lengths. Mechanical characterization of both the composite and the plain mortar was carried out using three point bend, compression, and impact tests. Specimens containing notches of different root radii were loaded in three point bending in an effort to determine the effect of the fibers on the fracture toughness of the material. The results obtained indicate that, while fiber reinforcement leads to a decrease in compressive strength, J-integral calculations at maximum load for the different notch root radii have indicated, particularly for the case of long fibers, a significant superiority of the reinforced material in comparison with the plain cement mortar, in consistence with the impact test data.

Psychological Impacts Related to Stress and Fear during the COVID-19 Pandemic: Cardiovascular Diseases, Diabetes and Psychological Disorders as Risk Factors

Background and Aims: Patients and the general public are under insurmountable psychological pressure which may lead to various psychological problems, such as anxiety, fear, depression, and insomnia, causing, consequently, the impaired quality of life. Psychological crisis intervention plays a pivotal role in the overall deployment of health-related quality of life and disease control. A novel Severe Acute Respiratory Syndrome Coronavirus-2 (SARS- CoV-2), a pathogen of the new coronavirus disease (COVID-19), has affected several sector activities, including people’s health. To enhance infection control methods, appropriate interventions, and public health policies, the present study aims to assess the fear and peri-traumatic stress during the COVID-19 inBrazil. Method: A cross-sectional survey has been conducted from April 12th to 18th from 2020, using the Peri-Traumatic Distress Scale (CPDI) and the Fear Scale (FCV-19S) aiming to measure the peri-traumatic stress and fear as psychological reactions during the COVID-19 pandemic. For that purpose, an online spreadsheet was used to send the questionnaire and scales to a sample of 1844 participants as a collecting information tool. After the data analysis, the individuals were separated into 4 groups: Group 1 (1232) population without chronic health conditions;group 2 (298) patients with previous psychological suffering, group 3 (229) patients with cardiovascular diseases, group 4 (71) patients with diabetes. For analysis, G1 were considered control for comparison with groups 2, 3 and 4. Results: All the groups showed the CPDI and FCV-19S increased in comparison with the G1 group. Concerning CPDI, the G 3 was increased when compared to G1, G2 and G4. The G3 had the FCV-19S higher in comparison with G1, G2 and G4. The Wilcoxon-Mann-Whitney test showed a statistical difference between the control group in comparison with 2 and 3 groups (Mann-Whitney p < 0.05). Conclusion: The COVID-19 pandemic had a significant impact on the Brazilian population, with patients with heart disease and hypertension presenting the highest numbers of stress and fear, with numbers comparable and even higher than those who reported previous psychological distress.

Complex derivatives valuation: applying the Least-Squares Monte Carlo Simulation Method with several polynomial basis

Background:This article investigates the Least-Squares Monte Carlo Method by using different polynomial basis in American Asian Options pricing.The standard approach in the option pricing literature is to choose the basis arbitrarily.By comparing four different polynomial basis we show that the choice of basis interferes in the option's price.Methods:We assess Least-Squares Method performance in pricing four different American Asian Options by using four polynomial basis:Power,Laguerre,Legendre and Hermite A.To every American Asian Option priced,three sets of parameters are used in order to evaluate it properly.Results:We show that the choice of the basis interferes in the option's price by showing that one of them converges to the option's value faster than any other by using fewer simulated paths.In the case of an Amerasian call option,for example,we find that the preferable polynomial basis is Hermite A.For an Amerasian put option,the Power polynomial basis is recommended.Such empirical outcome is theoretically unpredictable,since in principle all basis can be indistinctly used when pricing the derivative.Conclusion:In this article The Least-Squares Monte Carlo Method performance is assessed in pricing four different types of American Asian Options by using four different polynomial basis through three different sets of parameters.Our results suggest that one polynomial basis is best suited to perform the method when pricing an American Asian option.Theoretically all basis can be indistinctly used when pricing the derivative.However,our results does not confirm these.We find that when pricing an American Asian put option,Power A is better than the other basis we have studied here whereas when pricing an American Asian call,Hermite A is better.

The Use of Ground Penetrating Radar in Municipal Solid Waste Landfill Geotechnical Investigation

The use of GPR （ground penetrating radar） as an auxiliary tool in geotechnical and environmental site investigations has increased in Brazil during the past few years. GPR has been used to delineate contamination plumes, to aid in geological modelling, to detect buried structures and in archaeological surveys. This paper describes and discusses the results ofa GPR site investigation carried out at Gramacho Municipal Solid Waste Landfill in the State of Rio de Janeiro, Brazil. A field study was conducted to detect failure surfaces in its slopes and within the waste mass. The results have shown that： （l） Slip surfaces could be indicated by small continuous voids within the waste mass since there is a good contrast between the dielectric constant of air and municipal waste; （2） Greenhouse gases pools could also be indicated by large voids within the waste mass since there is a good contrast between the dielectric constant of carbon dioxide, methane and municipal waste; （3） Leachate pools present a high electric conductivity that could be easily detected by GPR.

The Relationship Amongst Countries＇ Governance, Energy and Sustainable Development

In this paper we construct a theoretical framework linking governance practiced by some countries to some of their economical, financial, social and environmental variables. To achieve this goal, we selected a sample of data composed of variables from 55 countries, available at the World Bank website. On the other hand, we measured the countries＇ level of governance by the indicator promoted by Transparency International. The period considered in this research was 8 years, from 2000 to 2008. We used the method of linear regression with panel data, in order to investigate statistical relationships among governance and the other variables. We applied the method of simultaneous equations in order to make a robustness test on the first results and findings. The results of the two models converged and showed the existence of a statistical significant relationship between governance and several of the variables investigated. This allows us to infer that the development of the countries govemanee is closely linked with the long-term sustainable development.

Using Thermoelastic Stress Analysis to Detect Damaged and Hot Spot Areas in Structural Components

This paper discusses the suitability of using TSA （thermoelastic stress analysis） as an advanced tool to detect damaged areas and highly stressed （hot spot） areas in structural components. Such components can be, for example, parts of large structural panels built of welded metallic or composite materials. Besides detecting hot spot areas, it is expected that stresses in these areas can be suitably quantified and processed in order to predict crack initiation and propagation due to in-service loads. The paper starts with references to selected review and application articles on the subject. Two simple laboratory experiments are presented which illustrate the quality of the results that can be achieved using TSA. In the first experiment, a stainless steel T-joint designed to model a welded structural component is analysed. The T-joint had a machine-notched crack-like flaw close to the component＇s weld toe. The qualitative and quantitative experimental results determined along four specified areas of the T-joint model showed that TSA can indeed be used as a tool to detect loaded cracks and hot spots in large metallic structures, and that stresses can be accurately evaluated. In the second experiment, a prismatic bar made of CFRE （carbon fibre-reinforced-epoxy） was tested to locate three subsurface areas of damage introduced beforehand into the component. Two of these inside damaged areas were detected to be 3.1 mm and 7.1 mm from the observed surface. The positive results achieved with the two lab experiments, along with a review of the selected research publications, indicate that TSA application can be extended to the real-world field of structural components. Topics to be addressed in this research field should have to do with components that work under random or quasi-cyclic service loading, problems where adiabatic conditions do not prevail, and reduction of the cost of infra-red cameras.

Accurate Measurements of High Strain Gradients near Notches Using a Feature-Based DIC Algorithm

In this work, the efficiency of a feature-based DIC （digital image correlation） algorithm for measuring high strain gradients was investigated by means of numerical and actual experiments. The so-called SIFT-Meshless method consisted of a novel formulation involving the SIFT （scale-invariant feature transform） feature detector with a self-adaptive meshless formulation. Whereas the numerical experiments aimed to evaluate the accuracy and the spatial resolution, the actual experiments aimed to demonstrate in practice the above findings. A stereoscopic system and a micro-stereoscopic system were used to perform high strain gradient measurements in notched specimens of different materials and notch sizes. This paper concludes that the feature-based algorithm is able to provide accurate strain measurements at high strain gradient regions, even under conditions of plasticity. Moreover, the algorithm showed its efficiency to capture the peak strain near the notch boundary. Lastly, a spatial resolution study proposes a link between the desired accuracy and the pixel resolution required to perform accurate measurements of high strain gradients.