Nursing students and the internet: a reflection of digital ethics.

Objectives: to identify how first-year nursing students use cyberspace and propose an orientation guide with criteria guiding the use of cyberspace.

Methods: qualitative and descriptive research, carried out with 24 nursing students from a federal public institution in Rio de Janeiro. Data collection was carried out through semi-structured interviews.

Terminological subset of ICNP® for people with chronic kidney disease on hemodialysis.

Objectives: To construct and validate a terminological subset of the International Classification of Nursing Practice (ICNP®) for people with chronic kidney disease on hemodialysis.

Method: Methodological study developed in accordance with the recommendations of the International Council of Nurses (ICN) and the Brazilian method, in the following stages: construction of ND/NO and NI statements of the ICNP® for nursing practice for people with chronic kidney disease on hemodialysis, based on previously constructed specialized terminology and in accordance with Wanda Horta's Basic Human Needs Theory; and content validation of the statements by focus groups with specialist nurses. The Content Validity Index was used and statements ≥ 0.

Detecting the Sigma Phase in Duplex Stainless Steel by Magnetic Noise and First Harmonic Analysis.

Non-destructive electromagnetic tests based on magnetic noise analysis have been developed to study, among others, residual stress, heat treatment outcomes, and harmful microstructures in terms of toughness. When subjected to thermal cycles above 550 °C, duplex stainless steels form an extremely hard and chromium-rich constituent that, if it is superior to 5%, compromises the steel's corrosion resistance and toughness. In the present work, a study was carried out concerning the interaction of excitation waves with duplex stainless steel.

Feature Extraction Based on Local Histogram with Unequal Bins and a Recurrent Neural Network for the Diagnosis of Kidney Diseases from CT Images.

Kidney disease remains one of the most common ailments worldwide, with cancer being one of its most common forms. Early diagnosis can significantly increase the good prognosis for the patient. The development of an artificial intelligence-based system to assist in kidney cancer diagnosis is crucial because kidney illness is a global health concern, and there are limited nephrologists qualified to evaluate kidney cancer.

Recovery of phosphorus and other minerals from greenhouse wastewater generated during soilless tomato cultivation by means of alkalizing agents.

The research was aimed at determining the possibility of recovering part of nutrients by precipitation from greenhouse wastewater (GW) from soilless tomato cultivation. Analyses included such elements as: P, S, N, Cl, Ca, Mg, K, Mo, Mn, Fe, Zn, Cu, and B. Three alkalizing agents were tested in a pH range of 6.

Transcription factor combinations that define human astrocyte identity encode significant variation of maturity and function.

Increasing evidence indicates that cellular identity can be reduced to the distinct gene regulatory networks controlled by transcription factors (TFs). However, redundancy exists in these states as different combinations of TFs can induce broadly similar cell types. We previously demonstrated that by overcoming gene silencing, it is possible to deterministically reprogram human pluripotent stem cells directly into cell types of various lineages.

Classification of Heart Sounds Using Chaogram Transform and Deep Convolutional Neural Network Transfer Learning.

Heart sounds convey important information regarding potential heart diseases. Currently, heart sound classification attracts many researchers from the fields of telemedicine, digital signal processing, and machine learning-among others-mainly to identify cardiac pathology as quickly as possible. This article proposes chaogram as a new transform to convert heart sound signals to colour images.

Transformers for Urban Sound Classification-A Comprehensive Performance Evaluation.

Many relevant sound events occur in urban scenarios, and robust classification models are required to identify abnormal and relevant events correctly. These models need to identify such events within valuable time, being effective and prompt. It is also essential to determine for how much time these events prevail.

Sound Classification and Processing of Urban Environments: A Systematic Literature Review.

Audio recognition can be used in smart cities for security, surveillance, manufacturing, autonomous vehicles, and noise mitigation, just to name a few. However, urban sounds are everyday audio events that occur daily, presenting unstructured characteristics containing different genres of noise and sounds unrelated to the sound event under study, making it a challenging problem. Therefore, the main objective of this literature review is to summarize the most recent works on this subject to understand the current approaches and identify their limitations.

Modified SqueezeNet Architecture for Parkinson's Disease Detection Based on Keypress Data.

Parkinson's disease (PD) is the most common form of Parkinsonism, which is a group of neurological disorders with PD-like motor impairments. The disease affects over 6 million people worldwide and is characterized by motor and non-motor symptoms. The affected person has trouble in controlling movements, which may affect simple daily-life tasks, such as typing on a computer.

Percolation in binary mixtures of linkers and particles: Chaining vs branching.

Equilibrium gels of colloidal particles can be realized through the introduction of a second species, a linker that mediates the bonds between colloids. A gel forming binary mixture whose linkers can self-assemble into linear chains while still promoting the aggregation of particles is considered in this work. The particles are patchy particles with f patches of type C and the linkers are patchy particles with 2 patches of type A and f patches of type B.

Human lower limb activity recognition techniques, databases, challenges and its applications using sEMG signal: an overview.

Human lower limb activity recognition (HLLAR) has grown in popularity over the last decade mainly because to its applications in the identification and control of neuromuscular disorders, security, robotics, and prosthetics. Surface electromyography (sEMG) sensors provide various advantages over other wearable or visual sensors for HLLAR applications, including quick response, pervasiveness, no medical monitoring, and negligible infection. Recognizing lower limb activity from sEMG signals is also challenging owing to the noise in the sEMG signal.

Road networks structure analysis: A preliminary network science-based approach.

Road network studies attracted unprecedented and overwhelming interest in recent years due to the clear relationship between human existence and city evolution. Current studies cover many aspects of a road network, for example, road feature extraction from video/image data, road map generalisation, traffic simulation, optimisation of optimal route finding problems, and traffic state prediction. However, analysing road networks as a complex graph is a field to explore.

Machine learning models for Parkinson's disease detection and stage classification based on spatial-temporal gait parameters.

Background: Parkinson's disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.

Convolutional Neural Networks and Heuristic Methods for Crowd Counting: A Systematic Review.

The crowd counting task has become a pillar for crowd control as it provides information concerning the number of people in a scene. It is helpful in many scenarios such as video surveillance, public safety, and future event planning. To solve such tasks, researchers have proposed different solutions.

Using Deep Neural Networks for Human Fall Detection Based on Pose Estimation.

Requests for caring for and monitoring the health and safety of older adults are increasing nowadays and form a topic of great social interest. One of the issues that lead to serious concerns is human falls, especially among aged people. Computer vision techniques can be used to identify fall events, and Deep Learning methods can detect them with optimum accuracy.

9-DOF IMU-Based Attitude and Heading Estimation Using an Extended Kalman Filter with Bias Consideration.

The attitude and heading reference system (AHRS) is an important concept in the area of navigation, image stabilization, and object detection and tracking. Many studies and works have been conducted in this regard to estimate the accurate orientation of rigid bodies. In most research in this area, low-cost MEMS sensors are employed, but since the system's response will diverge over time due to integration drift, it is necessary to apply proper estimation algorithms.

A ensemble methodology for automatic classification of chest X-rays using deep learning.

Chest radiographies, or chest X-rays, are the most standard imaging exams used in daily hospitals. Responsible for assisting in detecting numerous pathologies and findings that directly interfere in the patient's life, this exam is therefore crucial in screening patients. This work proposes a methodology based on a Convolutional Neural Networks (CNNs) ensemble to aid the diagnosis of chest X-ray exams by screening them with a high probability of being normal or abnormal.

Dual tasking reduces gait asymmetry of trajectory deviation during obstacle circumvention in people with Parkinson's disease.

Background: Certain aspects of gait may depend on cognition, and the effects of dual-tasking (DT) on gait asymmetry (GA) during obstacle circumvention in people with Parkinson's disease (pwPD) may be walking phase-dependent. While the approaching phase requires allocating significant cognitive resources to perform the task, outweighing the available resources may increase GA. During the avoiding phase, the obstacle could be used as an external cue, enhancing the attention to the obstacle and, consequently, improving gait control and regulating GA.

Binaural Acoustic Scene Classification Using Wavelet Scattering, Parallel Ensemble Classifiers and Nonlinear Fusion.

The analysis of ambient sounds can be very useful when developing sound base intelligent systems. Acoustic scene classification (ASC) is defined as identifying the area of a recorded sound or clip among some predefined scenes. ASC has huge potential to be used in urban sound event classification systems.

Safety culture in a nephrology service at a university hospital: A mixed method study.

Objective: To evaluate the safety culture from the perception of the multidisciplinary team of the nephrology service of a university hospital.

Method: Mixed methods study with concomitant triangulation of data, with 56 participants. In the adopted approach, data are weighted with QUAN+qual notation.

Semi-automatic segmentation of skin lesions based on superpixels and hybrid texture information.

Dermoscopic images are commonly used in the early diagnosis of skin lesions, and several computational systems have been proposed to analyze them. The segmentation of the lesions is a fundamental step in many of these systems. Therefore, a semi-automatic segmentation method is proposed here, which begins by building the superpixels of the image under analysis based on the zero parameter version of the simple linear iterative clustering (SLIC0) algorithm.

A cross-sectional analysis of the muscle strength, spinal shrinkage, and recovery during a working day of military police officers.

Objective: Military personnel has a large prevalence of back pain, especially those involved in patrolling routines, as they wear heavy protective equipment. Patrolling includes long periods of sustaining the protective equipment in a sitting or in a motor vehicle (motorcycle or car). Thus, understanding spinal loading of military police officers after patrolling by car (CAR; n = 14), motorcycle (MOT; n = 14), and administrative (ADM; n = 14) routines is relevant to establish preventive strategies.

Segmentation of male pelvic organs on computed tomography with a deep neural network fine-tuned by a level-set method.

Computed Tomography (CT) imaging is used in Radiation Therapy planning, where the treatment is carefully tailored to each patient in order to maximize radiation dose to the target while decreasing adverse effects to nearby healthy tissues. A crucial step in this process is manual organ contouring, which if performed automatically could considerably decrease the time to starting treatment and improve outcomes. Computerized segmentation of male pelvic organs has been studied for decades and deep learning models have brought considerable advances to the field, but improvements are still demanded.