A machine-learning approach for stress detection using wearable sensors in free-living environments.

Stress is a psychological condition resulting from the body's response to challenging situations, which can negatively impact physical and mental health if experienced over prolonged periods. Early detection of stress is crucial to prevent chronic health problems. Wearable sensors offer an effective solution for continuous and real-time stress monitoring due to their non-intrusive nature and ability to monitor vital signs, e.g., heart rate and activity. Typically, most existing research has focused on data collected in controlled environments. Yet, our study aims to propose a machine learning-based approach for detecting stress in a free-living environment using wearable sensors. We utilized the SWEET dataset, which includes data from 240 subjects collected via electrocardiography (ECG), skin temperature (ST), and skin conductance (SC). We assessed four machine learning models, i.e., K-Nearest Neighbors (KNN), Support Vector Classification (SVC), Decision Tree (DT), Random Forest (RF), and XGBoost (XGB) in four different settings. This study evaluates the performance of various machine learning models for stress classification using the SWEET dataset. The analysis included two binary classification scenarios (with and without SMOTE) and two multi-class classification scenarios (with and without SMOTE). The Random Forest model demonstrated superior performance in the binary classification without SMOTE, achieving an accuracy of 98.29 % and an F1-score of 97.89 %. For binary classification with SMOTE, the K-Nearest Neighbors model performed best, with an accuracy of 95.70 % and an F1-score of 95.70 %. In the three-level classification without SMOTE, the Random Forest model again excelled, achieving an accuracy of 97.98 % and an F1-score of 97.22 %. For three-level classification with SMOTE, XGBoost showed the highest performance, with an accuracy and F1-score of 98.98 %. These results highlight the effectiveness of different models under various conditions, emphasizing the importance of model selection and preprocessing techniques in enhancing classification performance.

Robust detection and refinement of saliency identification.

Salient object detection is an increasingly popular topic in the computer vision field, particularly for images with complex backgrounds and diverse object parts. Background information is an essential factor in detecting salient objects. This paper suggests a robust and effective methodology for salient object detection. This method involves two main stages. The first stage is to produce a saliency detection map based on the dense and sparse reconstruction of image regions using a refined background dictionary. The refined background dictionary uses a boundary conductivity measurement to exclude salient object regions near the image's boundary from a background dictionary. In the second stage, the CascadePSP network is integrated to refine and correct the local boundaries of the saliency mask to highlight saliency objects more uniformly. Using six evaluation indexes, experimental outcomes conducted on three datasets show that the proposed approach performs effectively compared to the state-of-the-art methods in salient object detection, particularly in identifying the challenging salient objects located near the image's boundary. These results demonstrate the potential of the proposed framework for various computer vision applications.

Effect of using nano-particles of magnesium oxide and titanium dioxide to enhance physical and mechanical properties of hip joint bone cement.

In this work, the effect of adding Magnesium Oxide (MgO) and Titanium Dioxide (TiO2) nanoparticles to enhance the properties of the bone cement used for hip prosthesis fixation. Related to previous work on enhanced bone cement properties utilizing MgO and TiO2, samples of composite bone cement were made using three different ratios (0.5%:1%, 1.5%:1.5%, and 1%:0.5%) w/w of MgO and TiO2 to determine the optimal enhancement ratio. Hardness, compression, and bending tests were calculated to check the mechanical properties of pure and composite bone cement. The surface structure was studied using Fourier transform infrared spectroscopy (FTIR) and Field emission scanning electron microscopy (FE-SEM). Setting temperature, porosity, and degradation were calculated for each specimen ratio to check values matched with the standard range of bone cement. The results demonstrate a slight decrease in porosity up to 2.2% and degradation up to 0.17% with NP-containing composites, as well as acceptable variations in FTIR and setting temperature. The compression strength increased by 2.8% and hardness strength increased by 1.89% on adding 0.5%w/w of MgO and 1.5%w/w TiO2 NPs. Bending strength increases by 0.35% on adding 1.5% w/w of MgO and 0.5% w/w TiO2 NPs, however, SEM scan shows remarkable improvement for surface structure.

Human cornea thermo-viscoelastic behavior modelling using standard linear solid model.

BACKGROUND: Corneal biomechanics is of great interest to researchers recently. Clinical findings relate them to corneal diseases and to outcomes of refractive surgery. To have a solid understanding of corneal diseases' progression, it is important to understand corneal biomechanics. Also, they are essential for better explaining outcomes of refractive surgeries and their undesired consequences. There is a difficulty for studying corneal biomechanics in-vivo and multiple limitations arise for ex-vivo studies. Hence mathematical modelling is considered as a proper solution to overcome such obstacles. Mathematical modelling of cornea in-vivo allows studying corneal viscoelasticity with taking into consideration all boundary conditions existing in real in-vivo situation. METHODS: Three mathematical models are used to simulate corneal viscoelasticity and thermal behavior in two different loading situations: constant and transient loading. Two models of the three are used for viscoelasticity simulation which are Kelvin-Voigt and standard linear solid models. Also, temperature rise due to the ultrasound pressure push is calculated using bioheat transfer model for both the axial direction and as a 2D spatial map using the third model (standard linear solid model). RESULTS: Viscoelasticity simulation results show that standard linear solid model is efficient for describing the viscoelastic behavior of human cornea in both loading conditions. Results show also that the deformation amplitude obtained from standard linear solid model is more reasonable for corneal soft-tissue deformation with respect to corresponding clinical findings than that obtained from Kelvin-Voigt model. Thermal behavior results estimated corneal temperature rise to be roughly 0.2 °C, which conforms with FDA regulations for soft tissue safety. CONCLUSION: Standard Linear Solid (SLS) model is better describing the human corneal behavior in response to constant and transient load more efficiently. Temperature rise (TR) for the corneal tissue of about 0.2 °C is conforming with FDA regulations and even less than the FDA regulations for soft tissue safety.

Stripping back the mask: Working conditions on digital labour platforms during the COVID-19 pandemic.

Digital labour platforms have been widely promoted as a solution to the unemployment crisis sparked by the COVID-19 pandemic. However, the pandemic has also highlighted the harms to gig workers-who are exposed either to income loss, or to infection while carrying out essential work, but excluded from labour protections. We examine the COVID-19 policies of 191 platforms in 43 countries to understand how the crisis has shifted the conventions of the gig economy. Using a typology of "fair platform work" we report the introduction of some positive worker protections, but also significant shortfalls, including entrenchment of precarious work as platforms leverage the opportunities arising from the crisis.

Automated brain tumor segmentation from multi-slices FLAIR MRI images.

Brain tumors are considered to be a leading cause of cancer death among young people. Early diagnosis is thus essential for treatment. The brain segmentation process is still challenging due to complexity and variation of the tumor structure, intensity similarity between tumor tissues and normal brain tissues. In this paper, a fully automated and reliable brain tumor segmentation system is proposed. This system is able to detect range of slices from a volume that is likely to contain tumor in MRI images. An iterated k-means algorithm is used for the segmentation process in conjunction with a cluster validity index to select the optimal number of clusters. The proposed approach is evaluated using simulated and real MRI of human brain from multimodal brain tumor image segmentation benchmark (BRATS) organized by MICCAI 2012 challenge. Our results achieved average for Dice overlap and Jaccard index for complete tumor region of 91.96% and 98.31% respectively when testing a set of 77 volumes. This shows the robustness of the new technique for clinical routine use.

Ethnocultural Gerontological Nursing: An Integrative Literature Review.

The aging population is growing increasingly more diverse, with one in four older adults from an ethnic minority group by 2050, while the nursing force will largely remain members of a single race White population. The purpose of this review is to appraise the state of nursing knowledge in relationship to meeting the needs of elders in unique racial/ethnic groups using two approaches: evaluating the efficacy of current knowledge and evaluating the state of nursing knowledge about ethnocultural gerontological nursing based on an integrative review of nursing literature. Thirty-four articles were reviewed. Most articles used qualitative methodology focused on a single ethnic group, with several articles focused on health promotion/prevention. Cultural perspectives were better addressed than aging concepts and few articles integrated ethnocultural and gerontological nursing concepts. This evaluation indicates many gaps in the knowledge base about ethnocultural gerontological nursing. Specific areas for future knowledge development are identified.

Developing and proposing the ethno-cultural gerontological nursing model.

By 2050, for the first time in U.S. history, almost half of elders will be from ethnic minority groups. To meet the needs of this rapidly diversifying population, nurses need to be able to marry transcultural nursing knowledge with gerontological nursing knowledge. The purpose of this article is to propose a new theoretical model for explaining health outcomes and health responses for older individuals in unique ethno-cultural groups and to discuss implications and applications of the model to transcultural gerontological nursing practice and research. The discussion will include (1) an overview of currently available theoretical knowledge in the area, (2) a description of the theory development process, (3) presentation of the proposed ethno-cultural gerontological nursing theoretical model, and (4) discussion of how this model can enhance nursing's contributions to reducing health disparities. This model is presented not as a finished product but as a basis for future discussion and refinement.

Nursing assistants' communication styles in Korean American older adults with dementia: a review of the literature.

As ethnic diversity increases in the United States with the anticipated increase in dementia, it is critical to understand the implications of dementia and culturally appropriate communication for ethnic minority older adults with dementia. Utilizing the Ethno-Cultural Gerontological Nursing model and the Progressively Lowered Stress Threshold model, this article describes the relationship between nursing assistants' communication style and behavioral symptoms of dementia, focused on Korean American older adults with dementia residing in nursing homes. The discussion includes reviewing currently available studies, nursing implications, and suggestions for future studies.

Segmentation of retinal blood vessels using a novel clustering algorithm (RACAL) with a partial supervision strategy.

In this paper, segmentation of blood vessels from colour retinal images using a novel clustering algorithm with a partial supervision strategy is proposed. The proposed clustering algorithm, which is a RAdius based Clustering ALgorithm (RACAL), uses a distance based principle to map the distributions of the data by utilising the premise that clusters are determined by a distance parameter, without having to specify the number of clusters. Additionally, the proposed clustering algorithm is enhanced with a partial supervision strategy and it is demonstrated that it is able to segment blood vessels of small diameters and low contrasts. Results are compared with those from the KNN classifier and show that the proposed RACAL performs better than the KNN in case of abnormal images as it succeeds in segmenting small and low contrast blood vessels, while it achieves comparable results for normal images. For automation process, RACAL can be used as a classifier and results show that it performs better than the KNN classifier in both normal and abnormal images.

Campylobacter upsaliensis: Another pathogen for consideration in the United States.

While evaluating quinolone resistance in a sample of Campylobacter isolates recovered from patients with campylobacteriosis in Los Angeles County, California, in 1998, we discovered that the second most frequently isolated species was Campylobacter upsaliensis (6 [4%] of 155 isolates). The ability of laboratories to recover this species may be dependent on the culture conditions and the media used. Three dogs living in the households of 2 of these 6 patients had C. upsaliensis isolated in their stool specimens.