Corrosion behavior of Shape Memory Alloy in NaCl environment and deformation recovery maintenance in Cu-Zn-Al system

Shape memory effect (SME) and the relation with corrosion behavior of Cu-Zn-Al Smart Memory Alloys (SMAs) were investigated using different techniques: Scanning Electron Microscopy equipped with an Energy Dispersive System, X-Ray Diffraction analysis, Electrochemical Test in NaCl solutions with different concentrations (0.035%, 0.35% and 3.5%), which simulate coastal conditions, mechanical characterization through tensile test and guided bend test. SMAs are an important class of smart materials able to recover after deformation a pre-imposed shape through a temperature modification. These alloys show great potential, finding several applications in medicine and in different types of industry sectors (aerospace, architecture, automotive etc.). Cu-based SMAs, including Cu-Zn-Al alloys, have lower production costs with respect to Ni-Ti alloys as well as good possibility in seismic and architectural applications. A Cu-Zn-Al alloy with a theoretical composition of 25 wt.% Zn and 4 wt.% Al was produced by casting method. The aim of this study is to characterize the microstructure, the mechanical properties and the corrosion behavior through different kind of standard corrosion tests of this alloy and to evaluate the effect of corrosion damage on the shape memory recovery capability through a combination of corrosion and thermo-mechanical cyclic test and SEM observations

Risk Factors for new accidental falls in elderly patients at traumatology ambulatory center

Objective. To identify the risks factors for new accidental falls in elderly patients attended in the Traumatology Ambulatory of a University hospital in Rio Grande do Sul, Brazil. Methodology. Quantitative study of the type of multiple cases. Performed at the traumatology ambulatory, amongst fifteen elders that attended the inclusion criteria: age of sixty or more; patient at the traumatology ambulatory because of a fall motivated by accident, oriented and in conditions of answer an interview of data collectors. The data collection was made between April and June, 2013, with the Elderly Nursing Core Set scale (Lopes & Fonseca). The data analysis was made by a descriptive structure, which helped identify the existence of relation patterns among the cases. Results. The risk factors for new accidental falls identified with larger incidence amongst the elders studied were: impaired balance (15/15), age above 65 (11/15), use of antihypertensive drugs (9/15), absence of non-slip material at home environment (7/15), in seven cases; rugs scattered at the floor of the house (6/15). Conclusion. The combination of intrinsic and extrinsic factors that include the environmental risks is considered a much more relevant cause to occur the new falls. The minimization of the home dangers, allied to the control of the elder intrinsic factors, may reduce the risks of causes. In that sense, is necessary that the nursing team make available more attention to the elderly assisted at the ambulatories, mainly those with sequelae due to fall accidents

Graph analysis of semantic word association among children, adults, and the elderly

This study used graph analysis to investigate how age differences modify the structure of semantic word association networks of children and adults and if the networks present a small-world structure and a scale-free distribution which are typical of natural languages. Three age groups of Brazilian Portuguese speakers (children, adults and elderly people) participated in the experiment. Quantitative and qualitative measures suggested that adults and elderly speakers have similar network structures. Children's network showed fewer nodes, connections and clusters, and longer inter-node distances. All networks presented a small-world structure, but they did not show entirely scale-free distributions. These results suggest that from childhood to adulthood, there is an increase not only in the number of words semantically linked to a target but also an increase in the connectivity of the network

Mental health and wellbeing during the COVID-19 pandemic: longitudinal analyses of adults in the UK COVID-19 Mental Health & Wellbeing study

Background The effects of coronavirus disease 2019 (COVID-19) on the population's mental health and well-being are likely to be profound and long lasting. Aims To investigate the trajectory of mental health and well-being during the first 6 weeks of lockdown in adults in the UK. Method A quota survey design and a sampling frame that permitted recruitment of a national sample was employed. Findings for waves 1 (31 March to 9 April 2020), 2 (10 April to 27 April 2020) and 3 (28 April to 11 May 2020) are reported here. A range of mental health factors was assessed: pre-existing mental health problems, suicide attempts and self-harm, suicidal ideation, depression, anxiety, defeat, entrapment, mental well-being and loneliness. Results A total of 3077 adults in the UK completed the survey at wave 1. Suicidal ideation increased over time. Symptoms of anxiety, and levels of defeat and entrapment decreased across waves whereas levels of depressive symptoms did not change significantly. Positive well-being also increased. Levels of loneliness did not change significantly over waves. Subgroup analyses showed that women, young people (18–29 years), those from more socially disadvantaged backgrounds and those with pre-existing mental health problems have worse mental health outcomes during the pandemic across most factors. Conclusions The mental health and well-being of the UK adult population appears to have been affected in the initial phase of the COVID-19 pandemic. The increasing rates of suicidal thoughts across waves, especially among young adults, are concerning

Mental health and well-being during the second wave of COVID-19: longitudinal analyses of the UK COVID-19 Mental Health and Wellbeing study (UK COVID-MH)

Background Waves 1 to 3 (March 2020 to May 2020) of the UK COVID-19 Mental Health and Wellbeing study suggested an improvement in some indicators of mental health across the first 6 weeks of the UK lockdown; however, suicidal ideation increased. Aims To report the prevalence of mental health and well-being of adults in the UK from March/April 2020 to February 2021. Method Quota sampling was employed at wave 1 (March/April 2020), and online surveys were conducted at seven time points. Primary analyses cover waves 4 (May/June 2020), 5 (July/August 2020), 6 (October 2020) and 7 (February 2021), including a period of increased restrictions in the UK. Mental health indicators were suicidal ideation, self-harm, suicide attempt, depression, anxiety, defeat, entrapment, loneliness and well-being. Results A total of 2691 (87.5% of wave 1) individuals participated in at least one survey between waves 4 and 7. Depressive symptoms and loneliness increased from October 2020 to February 2021. Defeat and entrapment increased from July/August 2020 to October 2020, and remained elevated in February 2021. Well-being decreased from July/August 2020 to October 2020. Anxiety symptoms and suicidal ideation did not change. Young adults, women, those who were socially disadvantaged and those with a pre-existing mental health condition reported worse mental health. Conclusions The mental health and well-being of the UK population deteriorated from July/August 2020 to October 2020 and February 2021, which coincided with the second wave of COVID-19. Suicidal thoughts did not decrease significantly, suggesting a need for continued vigilance as we recover from the pandemic

Association of kidney disease measures with risk of renal function worsening in patients with type 1 diabetes

Background: Albuminuria has been classically considered a marker of kidney damage progression in diabetic patients and it is routinely assessed to monitor kidney function. However, the role of a mild GFR reduction on the development of stage 653 CKD has been less explored in type 1 diabetes mellitus (T1DM) patients. Aim of the present study was to evaluate the prognostic role of kidney disease measures, namely albuminuria and reduced GFR, on the development of stage 653 CKD in a large cohort of patients affected by T1DM. Methods: A total of 4284 patients affected by T1DM followed-up at 76 diabetes centers participating to the Italian Association of Clinical Diabetologists (Associazione Medici Diabetologi, AMD) initiative constitutes the study population. Urinary albumin excretion (ACR) and estimated GFR (eGFR) were retrieved and analyzed. The incidence of stage 653 CKD (eGFR < 60 mL/min/1.73 m2) or eGFR reduction > 30% from baseline was evaluated. Results: The mean estimated GFR was 98 \ub1 17 mL/min/1.73m2 and the proportion of patients with albuminuria was 15.3% (n = 654) at baseline. About 8% (n = 337) of patients developed one of the two renal endpoints during the 4-year follow-up period. Age, albuminuria (micro or macro) and baseline eGFR < 90 ml/min/m2 were independent risk factors for stage 653 CKD and renal function worsening. When compared to patients with eGFR > 90 ml/min/1.73m2 and normoalbuminuria, those with albuminuria at baseline had a 1.69 greater risk of reaching stage 3 CKD, while patients with mild eGFR reduction (i.e. eGFR between 90 and 60 mL/min/1.73 m2) show a 3.81 greater risk that rose to 8.24 for those patients with albuminuria and mild eGFR reduction at baseline. Conclusions: Albuminuria and eGFR reduction represent independent risk factors for incident stage 653 CKD in T1DM patients. The simultaneous occurrence of reduced eGFR and albuminuria have a synergistic effect on renal function worsening

Spectra of a shallow sea-unmixing for class identification and monitoring of coastal waters

Ocean colour-based monitoring of water masses is a promising alternative to monitoring concentrations in heterogeneous coastal seas. Fuzzy methods, such as spectral unmixing, are especially well suited for recognition of water masses from their remote sensing reflectances. However, such models have not yet been applied for water classification and monitoring. In this study, a fully constrained endmember model with simulated endmembers was developed for water class identification in the shallow Wadden Sea and adjacent German Bight. Its performance was examined on in situ measured reflectances and on MERIS satellite data. Water classification by means of unmixing reflectance spectra proved to be successful. When the endmember model was applied to MERIS data, it was able to visualise well-known spatial, tidal, seasonal, and wind-related variations in optical properties in the heterogeneous Wadden Sea. Analyses show that the method is insensitive to small changes in endmembers. Therefore, it can be applied in similar coastal areas. For use in open ocean situations or coastal or inland waters with other specific inherent optical properties, re-simulation of the endmember spectra with local optical properties is required. However, such an adaptation requires only a limited number of local in situ measurements

Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given

Genetic Programming for Feature Selection and Feature Construction in Skin Cancer Image Classification

The incidence of skin cancer, particularly, malignant melanoma, continues to increase worldwide. If such a cancer is not treated at an early stage, it can be fatal. A computer system based on image processing and computer vision techniques, having good diagnostic ability, can provide a quantitative evaluation of these skin cancer cites called skin lesions. The size of a medical image is usually large and therefore requires reduction in dimensionality before being processed by a classification algorithm. Feature selection and construction are effective techniques in reducing the dimensionality while improving classification performance. This work develops a novel genetic programming (GP) based two-stage approach to feature selection and feature construction for skin cancer image classification. Local binary pattern is used to extract gray and colour features from the dermoscopy images. The results of our proposed method have shown that the GP selected and constructed features have promising ability to improve the performance of commonly used classification algorithms. In comparison with using the full set of available features, the GP selected and constructed features have shown significantly better or comparable performance in most cases. Furthermore, the analysis of the evolved feature sets demonstrates the insights of skin cancer properties and validates the feature selection ability of GP to distinguish between benign and malignant cancer images