Search results for: nursing interventions classification

Authors: Bacha Zakaria

Abstract:

This study aimed to investigate the reality of humane treatment and work environment problems for nurses in public hospitals and their repercussions on the patients arriving there. In this curve, our field study was based on a sample of nurses in Algiers hospitals estimated at 100 nurses. The questionnaire prepared by the two researchers was applied face to face with the nurses, and after obtaining and analyzing the data, we concluded the most important results: The presence of many problems in the work environment, such as work pressures, lack of appreciation, verbal and physical violence, risk of infection, poor salary and incentives, working during fatigue, administrative problems etc. And accordingly, The embodiment of humane dealing with patients requires providing a humane work environment for nurses and dealing with them humanely so that they embody positive behaviors while dealing with patients.

Keywords: nursing, future, family-focused care, health equity

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Authors: S. Nandagopalan, N. Pradeep

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The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, Bayesian, echocardiographic image, feature vector

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Authors: Obulesh Avuku, Satwik Sunnam, Sri Charan Mohan Janthuka, Keerthi Yalamaddi

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In the United States alone, there are over 1.5 million spine fractures per year, resulting in about 17,730 spinal cord injuries. The cervical spine is where fractures in the spine most frequently occur. The prevalence of spinal fractures in the elderly has increased, and in this population, fractures may be harder to see on imaging because of coexisting degenerative illness and osteoporosis. Nowadays, computed tomography (CT) is almost completely used instead of radiography for the imaging diagnosis of adult spine fractures (x-rays). To stop neurologic degeneration and paralysis following trauma, it is vital to trace any vertebral fractures at the earliest. Many approaches have been proposed for the classification of the cervical spine [2d models]. We are here in this paper trying to break the bounds and use the vision transformers, a State-Of-The-Art- Model in image classification, by making minimal changes possible to the architecture of ViT and making it 3D-enabled architecture and this is evaluated using a weighted multi-label logarithmic loss. We have taken this problem statement from a previously held Kaggle competition, i.e., RSNA 2022 Cervical Spine Fracture Detection.

Keywords: cervical spine, spinal fractures, osteoporosis, computed tomography, 2d-models, ViT, multi-label logarithmic loss, Kaggle, public score, private score

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Authors: Apra Sinha

Abstract:

The inverted U-shaped Environmental Kuznets curve (EKC) demonstrates(pollution-income relationship)that initially the pollution and environmental degradation surpass the level of income per capita; however this trend reverses since at the higher income levels, economic growth initiates environmental upgrading. However, what effect does increased environmental degradation has on growth is the missing feedback link which has not been addressed in the EKC hypothesis. This paper examines the missing feedback link in EKC hypothesis in Indian context by examining the casual association between fossil fuel consumption, carbon dioxide emissions and economic growth for India. Fossil fuel consumption here has been taken as a proxy of driver of economic growth. The casual association between the aforementioned variables has been analyzed using five interventions namely 1) urban development for which urbanization has been taken proxy 2) industrial development for which industrial value added has been taken proxy 3) trade liberalization for which sum of exports and imports as a share of GDP has been taken as proxy 4)financial development for which a)domestic credit to private sector and b)net foreign assets has been taken as proxies. The choice of interventions for this study has been done keeping in view the economic liberalization perspective of India. The main aim of the paper is to investigate the missing feedback link for Environmental Kuznets Curve Hypothesis before and after incorporating the intervening variables. The period of study is from 1971 to 2011 as it covers pre and post liberalization era in India. All the data has been taken from World Bank country level indicators. The Johansen and Juselius cointegration testing methodology and Error Correction based Granger causality have been applied on all the variables. The results clearly show that out of five interventions, only in two interventions the missing feedback link is being addressed. This paper can put forward significant policy implications for environment protection and sustainable development.

Keywords: environmental Kuznets curve hypothesis, fossil fuel consumption, industrialization, trade liberalization, urbanization

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Authors: Kazeem Adesina Dauda, Waheed Babatunde Yahya

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To design a tree that maximizes within-node homogeneity, there is a need for a homogeneity measure that is appropriate for event history data with multiple risks. We consider the use of Deviance and Modified Cox-Snell residuals as a measure of impurity in Classification Regression Tree (CART) and compare our results with the results of Fiona (2008) in which homogeneity measures were based on Martingale Residual. Data structure approach was used to validate the performance of our proposed techniques via simulation and real life data. The results of univariate competing risk revealed that: using Deviance and Cox-Snell residuals as a response in within node homogeneity classification tree perform better than using other residuals irrespective of performance techniques. Bone marrow transplant data and double-blinded randomized clinical trial, conducted in other to compare two treatments for patients with prostate cancer were used to demonstrate the efficiency of our proposed method vis-à-vis the existing ones. Results from empirical studies of the bone marrow transplant data showed that the proposed model with Cox-Snell residual (Deviance=16.6498) performs better than both the Martingale residual (deviance=160.3592) and Deviance residual (Deviance=556.8822) in both event of interest and competing risks. Additionally, results from prostate cancer also reveal the performance of proposed model over the existing one in both causes, interestingly, Cox-Snell residual (MSE=0.01783563) outfit both the Martingale residual (MSE=0.1853148) and Deviance residual (MSE=0.8043366). Moreover, these results validate those obtained from the Monte-Carlo studies.

Keywords: within-node homogeneity, Martingale residual, modified Cox-Snell residual, classification and regression tree

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Authors: Louis Botha

Abstract:

Since the presentation of South Africa’s inclusive education strategy in Education White Paper 6 in 2001, very little has been accomplished in terms of its implementation. The failure to achieve the goals set by this policy document is related to teachers lacking confidence and knowledge about how to enact inclusive education, as well as challenges of inflexible curricula, limited resources in overcrowded classrooms, and so forth. This paper presents a socio-cultural approach to addressing these challenges of implementing inclusive education in the South African context. It takes its departure from the view that inclusive education has been adequately theorized and conceptualized in terms of its philosophical and ethical principles, especially in South African policy and debates. What is missing, however, are carefully theorized, practically implementable research interventions which can address the concerns mentioned above. Drawing on socio-cultural principles of learning and development and on cultural-historical activity theory (CHAT) in particular, this paper argues for the use of formative interventions which introduce appropriately constructed mediational artifacts that have the potential to initiate inclusive practices and pedagogies within South African schools and classrooms. It makes use of Vygotsky’s concept of double stimulation to show how the proposed artifacts could instigate forms of transformative agency which promote the adoption of inclusive cultures of learning and teaching.

Keywords: cultural-historical activity theory, double stimulation, formative interventions, transformative agency

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Authors: Aditya Manna, Lalit Kumar Khanra, Shyamal Kumar Sarkar

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Introduction: Here in India almost 75% of cancer patient die a sad death of neglect due to lack of awareness about palliative care and low economic level. Surveys in India show that two third of cancer patient do not get proper care during the terminal phase of their life. Palliative care through volunteers can make a significant difference in this respect. Objective: To identify and try to solve, to the extent possible, the main difficulties in giving palliative care to the terminal cancer patients of the area. And evaluate the impact of volunteer’s direct care of palliative patients and their families. Methods: Feedback from patients and their relatives regarding the palliative care they receive from nursing home and from volunteers and compare the two. Also feedback from volunteers regarding their positive and negative experience while delivering palliative care service. Then evaluate the data to compare and improve the quality of service. Results: We carried out two studies. One study was undertaken in nursing home palliative care and another was in home setting by volunteers. Both studies were in adult palliative care services. Since January 2015, 496 cases were studied to enquire about their experience in both home based care and nursing home care. Both the studies fulfilled our quality appraisal criteria. One found that those families and patients who received home visits from volunteers were significantly more satisfied. The study highlighted the value of the role of volunteers in better satisfaction of patients and their families. Conclusions: Further research is needed to evaluate the role of volunteers in palliative care and how it can be delivered appropriately and effectively. We also wish to compare our findings with similar studies elsewhere.

Keywords: palliative care, terminal care, cancer, home care

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Authors: R. Abdulrahman, A. Eardley, A. Soliman

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The proliferation in the development of mobile learning (m-learning) has played a vital role in the rapidly growing electronic learning market. This relatively new technology can help to encourage the development of in learning and to aid knowledge transfer a number of areas, by familiarizing students with innovative information and communications technologies (ICT). M-learning plays a substantial role in the deployment of learning methods for nursing students by using the Internet and portable devices to access learning resources ‘anytime and anywhere’. However, acceptance of m-learning by students is critical to the successful use of m-learning systems. Thus, there is a need to study the factors that influence student’s intention to use m-learning. This paper addresses this issue. It outlines the outcomes of a study that evaluates the unified theory of acceptance and use of technology (UTAUT) model as applied to the subject of user acceptance in relation to m-learning activity in nurse education. The model integrates the significant components across eight prominent user acceptance models. Therefore, a standard measure is introduced with core determinants of user behavioural intention. The research model extends the UTAUT in the context of m-learning acceptance by modifying and adding individual innovativeness (II) and quality of service (QoS) to the original structure of UTAUT. The paper goes on to add the factors of previous experience (of using mobile devices in similar applications) and the nursing students’ readiness (to use the technology) to influence their behavioural intentions to use m-learning. This study uses a technique called ‘convenience sampling’ which involves student volunteers as participants in order to collect numerical data. A quantitative method of data collection was selected and involves an online survey using a questionnaire form. This form contains 33 questions to measure the six constructs, using a 5-point Likert scale. A total of 42 respondents participated, all from the Nursing Institute at the Armed Forces Hospital in Saudi Arabia. The gathered data were then tested using a research model that employs the structural equation modelling (SEM), including confirmatory factor analysis (CFA). The results of the CFA show that the UTAUT model has the ability to predict student behavioural intention and to adapt m-learning activity to the specific learning activities. It also demonstrates satisfactory, dependable and valid scales of the model constructs. This suggests further analysis to confirm the model as a valuable instrument in order to evaluate the user acceptance of m-learning activity.

Keywords: mobile learning, nursing institute students’ acceptance of m-learning activity in Saudi Arabia, unified theory of acceptance and use of technology model (UTAUT), structural equation modelling (SEM)

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Authors: Maha Salah, Hanaa Hashem, Mahmoud M. Alsagheir, Mohammed Salah

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Acute respiratory distress syndrome (ARDS) represents a complex clinical syndrome and carries a high risk for mortality. The severity of the clinical course, the uncertainty of the outcome, and the reliance on the full spectrum of critical care resources for treatment mean that the entire health care team is challenged. Researchers and clinicians have investigated the nature of the pathological process and explored treatment options with the goal of improving outcome. Through this application of research to practice, we know that some previous strategies have been ineffective, and innovations in mechanical ventilation, sedation, nutrition, and pharmacological intervention remain important research initiatives. Developed Clinical pathway is multidisciplinary plans of best clinical practice for this specified groups of patients that aid in the coordination and delivery of high quality care. They are a documented sequence of clinical interventions that help a patient to move, progressively through a clinical experience to a desired outcome. Although there is a lot of heterogeneity in patients with ARDS, this suggested developed clinical pathway with alternatives was built depended on a lot of researches and evidence based medicine and nursing practices which may be helping these patients to improve outcomes, quality of life and decrease mortality.

Keywords: acute respiratory distress syndrome (ARDS), clinical pathway, clinical syndrome

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Authors: Abebe Mulugojam Negash, Yongbin Yu, Ekong Favour, Bekalu Nigus Dawit, Molla Woretaw Teshome, Aynalem Birtukan Yirga

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Breast cancer remains a significant health concern, necessitating advancements in diagnostic methodologies. Addressing this, our paper confronts the notable challenges in breast cancer classification, particularly the imbalance in datasets and the constraints in the accuracy and interpretability of prevailing deep learning approaches. We proposed an attention-based residual neural network (ResNet), which effectively combines the robust features of ResNet with an advanced attention mechanism. Enhanced through strategic data augmentation and positive weight adjustments, this approach specifically targets the issue of data imbalance. The proposed model is tested on the BreakHis dataset and achieved accuracies of 99.00%, 99.04%, 98.67%, and 98.08% in different magnifications (40X, 100X, 200X, and 400X), respectively. We evaluated the performance by using different evaluation metrics such as precision, recall, and F1-Score and made comparisons with other state-of-the-art methods. Our experiments demonstrate that the proposed model outperforms existing approaches, achieving higher accuracy in breast cancer classification.

Keywords: residual neural network, attention mechanism, positive weight, data augmentation

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Authors: Bliss Singhal

Abstract:

Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer.

Keywords: breast cancer, principal component analysis, genetic algorithm, k-nearest neighbors, decision tree classifier, logistic regression

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Authors: Mouli Banga, Chaya Ravindra

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Wound classification is a crucial step in wound diagnosis. An effective classifier can aid wound specialists in identifying wound types with reduced financial and time investments, facilitating the determination of optimal treatment procedures. This study presents a deep neural network-based classifier that leverages wound images and their corresponding locations to categorize wounds into various classes, such as diabetic, pressure, surgical, and venous ulcers. By incorporating a developed body map, the process of tagging wound locations is significantly enhanced, providing healthcare specialists with a more efficient tool for wound analysis. We conducted a comparative analysis between two prominent convolutional neural network models, ResNet50 and MobileNetV2, utilizing a dataset of 730 images. Our findings reveal that the RestNet50 outperforms MovileNetV2, achieving an accuracy of approximately 90%, compared to MobileNetV2’s 83%. This disparity highlights the superior capability of ResNet50 in the context of this dataset. The results underscore the potential of integrating deep learning with spatial data to improve the precision and efficiency of wound diagnosis, ultimately contributing to better patient outcomes and reducing healthcare costs.

Keywords: wound classification, MobileNetV2, ResNet50, multimodel

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Authors: Gholam Hossein Javanmard, Roghayeh Mohammadi Garegozlo

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The present study was a randomized three-group double-blind clinical trial with repeated measures designs which aimed to determine the pure effect and combined effect of mindfulness based-relaxation (MBR) technique and Transcranial Electrical Simulation (tCES) on psychological distress decreasing of patients with type-2 diabetes. The sample of the study consisted of 30 patients with type-2 diabetes who were selected from the Diabetes Association of Bonab city in Iran. The participants were matched and then randomly assigned to the three groups of 10 subjects (MBR, CES, MBR+CES). The subjects received interventions related to their group in 10 individual sessions. Pre-test, post-test, and one-month follow-up were conducted using DASS-42. Analysis of variance with repeated measures showed a significant change in psychological distress. Multivariate covariance analysis and the paired interpersonal comparative test of Ben Foruni indicated that both interventions of MBR and CES have a similar effect on psychological distress decreasing in the post-test and follow-up phase. But, the combined therapy of MBR+CES was more efficient, and it had a more stable effect. However, all three interventions, especially combined intervention of MBR+CES, as efficient and stable treatment, are suggested for improving the psychological status of diabetic patients.

Keywords: mindfulness based-relaxation, transcranial electrical simulation, type 2 diabetes, psychological distress

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Authors: Nene Pearl Eluchie

Abstract:

Globally, metal ion, particularly those generated from oil and gas effluents, form environmental pollution, particularly in developing regions like Nigeria, where water borne disease is fatal. This is clear evidence for metal ion contamination within the environment. Ecofriendly and cost effective biomaterials are the best ways of reducing metal ion contamination, thus reducing the need for chemical treatment of oil and gas effluent. Despite this, research efforts to understand the mechanism of adsorption and possible bio-adsorptive remediation interventions are limited. The study combined biomaterial and adsorption techniques: A. Indica, UV-Visible spectroscopy, SEM, FTIR in a progressive manner to provide insight. The biosorption efficiency of Azadirachta Indica silver nanoparticle AI-AgNPs was within the range of 63-95%. The study demonstrates that AI-AgNPs can be a promising agent, cheap, efficient, and biodegradable bio-sorbent for lowering oil and gas effluents. This is one of the studies to show that Azadirachta Indica is just one of the many biomaterials to synthesize silver nanoparticles through the reduction of active constituents (Nimbidin) present in them to ensure stability and surface properties, which are critical for their performance in effluent treatment. Therefore, leveraging the knowledge from this study to raise awareness through public health initiatives and community engagement will help. The prevalence of metal ions observed in the visible region in the study indicates the need for bio-adsorptive remediation interventions, not only in social settings but also in the immediate environment. There is, thus, an urgent need for targeted interventions in vulnerable communities.

Keywords: Azadirachta indica, bioadsorption, biosynthesis, effluent, nimbidin, silver nanoparticle

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The widespread use of machine learning applications in production is significantly accelerated by improved computing power and increasing data availability. Predictive quality enables the assurance of product quality by using machine learning models as a basis for decisions on test results. The use of real Bosch production data based on geometric gauge blocks from machining, mating data from assembly and hydraulic measurement data from final testing of directional valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: machine learning, classification, predictive quality, hydraulics, supervised learning

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Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: Weiwei Cui, Dejian Lin, Leigang Zhang, Yao Wang, Zheng Sun, Lianfeng Li

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Built-in test (BIT) is an important technology in testability field, and it is widely used in state monitoring and fault diagnosis. With the improvement of modern equipment performance and complexity, the scope of BIT becomes larger, and it leads to the emergence of false alarm problem. The false alarm makes the health assessment unstable, and it reduces the effectiveness of BIT. The conventional false alarm suppression methods such as repeated test and majority voting cannot meet the requirement for a complicated system, and the intelligence algorithms such as artificial neural networks (ANN) are widely studied and used. However, false alarm has a very low frequency and small sample, yet a method based on ANN requires a large size of training sample. To recognize the false alarm, we propose a method based on multi-classification support vector machine (SVM) in this paper. Firstly, we divide the state of a system into three states: healthy, false-alarm, and faulty. Then we use multi-classification with '1 vs 1' policy to train and recognize the state of a system. Finally, an example of fault injection system is taken to verify the effectiveness of the proposed method by comparing ANN. The result shows that the method is reasonable and effective.

Keywords: false alarm, fault diagnosis, SVM, k-means, BIT

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Authors: Zdenek Silar, Martin Dobrovolny

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This article deals with the obstacle detection on a railway crossing (clearance detection). Detection is based on the optical flow estimation and classification of the flow vectors by K-means clustering algorithm. For classification of passing vehicles is used optical flow direction determination. The optical flow estimation is based on a modified Lucas-Kanade method.

Keywords: background estimation, direction of optical flow, K-means clustering, objects detection, railway crossing monitoring, velocity vectors

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Authors: Christopher Wearmouth, Jacob Smith

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Administration of tranexamic acid (TXA) reduces all-cause mortality in trauma patients when given within 3 hours of injury. Due to geographical distance and lack of emergency medical services patients often present late, following trauma, to our emergency department. Additionally, we found patients that may have benefited from TXA did not receive it, often due to lack of staff awareness, staff shortages out of hours and lack of equipment for delivering infusions. Our objective was to develop a protocol for nurse-led administration of TXA in the emergency department. We developed a protocol using physiological observations along with criteria from the South African Triage Scale to allow nursing staff to identify patients with, or at risk of, significant haemorrhage. We will monitor the use of the protocol to ensure appropriate compliance and for any adverse events reported.

Keywords: emergency department, emergency nursing, rural healthcare, tranexamic acid, trauma, triage

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Authors: H. Hotait, X. Chiementin, L. Rasolofondraibe

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This paper presents a continuous work to detect the abnormal state in the rolling bearing by studying the vibration signature analysis and calculation of the remaining useful life. To achieve these aims, two methods; the first method is the classification to detect the degradation state by the AOM-OPTICS (Acousto-Optic Modulator) method. The second one is the prediction of the degradation state using least-squares support vector regression and then compared with the linear degradation model. An experimental investigation on ball-bearing was conducted to see the effectiveness of the used method by applying the acquired vibration signals. The proposed model for predicting the state of bearing gives us accurate results with the experimental and numerical data.

Keywords: bearings, automatization, optimization, prognosis, classification, defect detection

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Authors: Daniel M. Wolff, Patricia Gray, Rafael de la Parra Venegas

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An unsupervised machine listening system is constructed and applied to a dataset of 17,195 30-second marine hydrophone recordings. The system is then heuristically supplemented with anecdotal listening, contextual recording information, and supervised learning techniques to reduce the number of false positives. Features for classification are assembled by extracting the following data from each of the audio files: the spectral centroid, root-mean-squared values for each frequency band of a 10-octave filter bank, and mel-frequency cepstral coefficients in 5-second frames. In this way both time- and frequency-domain information are contained in the features to be passed to a clustering algorithm. Classification is performed using the k-means algorithm and then a k-nearest neighbors search. Different values of k are experimented with, in addition to different combinations of the available feature sets. Hypothesized class labels are 'primarily anthrophony' and 'primarily biophony', where the best class result conforming to the former label has 104 members after heuristic pruning. This demonstrates how a large audio dataset has been made more tractable with machine learning techniques, forming the foundation of a framework designed to acoustically monitor and gauge biological and anthropogenic activity in a marine environment.

Keywords: anthrophony, hydrophone, k-means, machine learning

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Authors: S. Nandagopalan, N. Pradeep

Abstract:

The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, bayesian, echocardiographic image, feature vector

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Authors: Amanda Madden

Abstract:

The purpose of this research was to identify students understanding of well-being and perceptions of the effective components of school-based well-being programs they have participated in during their time in secondary school. With one in four adolescents suffering from some form of mental health disorder, which has the potential to directly impact their academic ability, schools have moved towards a more holistic approach to education, resulting in the growth of school-based well-being programs. There is limited research on the effectiveness of school-based well-being programs, with fewer studies examining students’ perspectives on their well-being. A mixed-method design was utilized, framed by a social constructivist methodology. Quantitative data was collected through a researcher-developed self-report survey, and qualitative data were collected through one-on-one interviews and a semi-structured focus group undertaken with Year 12 students from three independent co-educational schools in Western Australia. Preliminary findings indicate that participants have experienced a minimal impact, either positively or negatively, on their well-being from school-based well-being programs. The data detailed that adolescents consider happiness, positive attitude, good physical health, balance, emotional fulfillment and confidence components of well-being. The findings also highlighted sports, positive family relationships, positive friendships, and pets positively enhanced well-being. This research suggests that researchers and educational leaders should consider students’ understanding of well-being in the development of school-based well-being assessments and interventions. Students are the recipients of school-based well-being programs and are best placed to inform what they will and will not respond to in the determination of appropriate well-being content.

Keywords: wellbeing, school based wellbeing, adolescents, wellbeing interventions

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Authors: M. Abdullah, Al-Wadud

Abstract:

Sensor-based activity recognition systems usually accounts which sensors have been activated to perform an activity. The system then combines the conditional probabilities of those sensors to represent different activities and takes the decision based on that. However, the information about the sensors which are not activated may also be of great help in deciding which activity has been performed. This paper proposes an approach where the sensory data related to both usage and non-usage of objects are utilized to make the classification of activities. Experimental results also show the promising performance of the proposed method.

Keywords: Naïve Bayesian, based classification, activity recognition, sensor data, object-usage model

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Authors: Pin Yu Chen, Puay Chuan Lee, Yu Jen Loo, Ju Xia Zhang, Deborah Teo, Jack Wei Chieh Tan, Biauw Chi Ong

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Background: Synchronous communication, such as telephone calls, remains the standard communication method between nurses and other healthcare professionals in Singapore public hospitals despite advances in asynchronous technological platforms, such as instant messaging. Although miscommunication is one of the most common causes of lapses in patient care, there is a scarcity of research characterizing baseline inter-professional healthcare communications in a hospital setting due to logistic difficulties. Objective: This study aims to characterize the frequency and patterns of communication behaviours among healthcare professionals. Methods: The one-week observational study was conducted on Monday through Sunday at the nursing station of a cardiovascular medicine and cardiothoracic surgery inpatient ward at the National Heart Centre Singapore. Subjects were shadowed by two physicians for sixteen hours or consecutive morning and afternoon nursing shifts. Communications were logged and characterized by type, duration, caller, and recipient. Results: A total of 1,023 communication events involving the attempted use of the common telephones at the nursing station were logged over a period of one week, corresponding to a frequency of one event every 5.45 minutes (SD 6.98, range 0-56 minutes). Nurses initiated the highest proportion of outbound calls (38.7%) via the nursing station common phone. A total of 179 face-to-face communications (17.5%), 362 inbound calls (35.39%), 481 outbound calls (47.02%), and 1 emergency alert (0.10%) were captured. Average response time for task-oriented communications was 159 minutes (SD 387.6, range 86-231). Approximately 1 in 3 communications captured aimed to clarify patient-related information. The total duration of time spent on synchronous communication events over one week, calculated from total inbound and outbound calls, was estimated to be a total of 7 hours. Conclusion: The results of our study showed that there is a significant amount of time spent on inter-professional healthcare communications via synchronous channels. Integration of patient-related information and use of asynchronous communication channels may help to reduce the redundancy of communications and clarifications. Future studies should explore the use of asynchronous mobile platforms to address the inefficiencies observed in healthcare communications.

Keywords: healthcare communication, healthcare management, nursing, qualitative observational study

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Authors: Paulina Pergoł, Paulina Pergoł, Tomasz Kryczka

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Aging of societies is a phenomenon that is visible all over the world. The above has social, economic, and medical consequences. Many methods of pro-health stimulation of the elderly, improving cognitive functions and quality of life, are described in the world literature. The most frequently used method of activating the elderly is physical activity, in which, in addition to standard exercise programs, activities such as dance, tai chi, and yoga are often introduced. The introduction of physical activity may be limited due to the disability that often accompanies people aged 65+. Other activating methods mentioned in the literature are therapies with the participation of animals - animal therapy or plant therapy - hortitherapy, as well as music therapy, which is increasingly popular. All of the above-mentioned therapeutic methods require the involvement of a person who would conduct the so-called occupational therapist, which can be a financial barrier for many nursing homes. Researchers all over the world are trying to find solutions that can be applied in any care institution, even in those where financial support for running such centers is small. One of the forms of therapy that is recommended in the group of elderly people and which does not require large financial outlays is the so-called reminiscence therapy based on recalling personal experiences and experiences. Thanks to reminiscence therapy, seniors can return to happy moments in their lives. Research shows that reminiscence therapy increases self-esteem and reduces symptoms of depression. The method of activating older people with the use of correspondence can be an effective and low-cost form of therapy, as shown by the pilot study conducted in 2019 as part of the Queen Silvia Nursing Award competition, consisting in a systematic exchange of correspondence between volunteers and residents of the Nursing Home. This study was conducted with the participation of a small group of participants, therefore, in order to identify and prove the beneficial effect of this form of activation, it should be carried out on a larger group of respondents using standardized scales assessing various psychological parameters.

Keywords: nursing, elderly care, psychiatry, psychology

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

Abstract:

Digitalisation in production technology is a driver for the application of machine learning methods. Through the application of predictive quality, the great potential for saving necessary quality control can be exploited through the data-based prediction of product quality and states. However, the serial use of machine learning applications is often prevented by various problems. Fluctuations occur in real production data sets, which are reflected in trends and systematic shifts over time. To counteract these problems, data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets to extract stable features. Successful process control of the target variables aims to centre the measured values around a mean and minimise variance. Competitive leaders claim to have mastered their processes. As a result, much of the real data has a relatively low variance. For the training of prediction models, the highest possible generalisability is required, which is at least made more difficult by this data availability. The implementation of a machine learning application can be interpreted as a production process. The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that describes the life cycle of data science. As in any process, the costs to eliminate errors increase significantly with each advancing process phase. For the quality prediction of hydraulic test steps of directional control valves, the question arises in the initial phase whether a regression or a classification is more suitable. In the context of this work, the initial phase of the CRISP-DM, the business understanding, is critically compared for the use case at Bosch Rexroth with regard to regression and classification. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. Suitable methods for leakage volume flow regression and classification for inspection decision are applied. Impressively, classification is clearly superior to regression and achieves promising accuracies.

Keywords: classification, CRISP-DM, machine learning, predictive quality, regression

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Authors: Hsin-Yu Lee, Ming-Zhong Li, Wen-Hsi Chiu, Su-Fen Cheng, Shwu-Wen Lin

Abstract:

Background: The use of medical terminology is essential to professional nurses on clinical practice. However, most nursing students consider traditional lecture-based teaching of medical terminology as boring and overly conceptual and lack motivation to learn. It is thus an issue to be discussed on how to enhance nursing students’ self-directed learning and improve learning outcomes of medical terminology. Digital game-based learning is a learner-centered way of learning. Past literature showed that the most common game-based learning for language education has been immersive games and teaching games. Thus, this study selected role-playing games (RPG) and digital puzzle games for observation and comparison. It is interesting to explore whether digital game-based learning has positive impact on nursing students’ learning of medical terminology and whether students can adapt well on this type of learning. Results can be used to provide references for institutes and teachers on teaching medical terminology. These instructions give you guidelines for preparing papers for the conference. Use this document as a template if you are using Microsoft Word. Otherwise, use this document as an instruction set. The electronic file of your paper will be formatted further at WASET. Define all symbols used in the abstract. Do not cite references in the abstract. Do not delete the blank line immediately above the abstract; it sets the footnote at the bottom of this column. Page margins are 1,78 cm top and down; 1,65 cm left and right. Each column width is 8,89 cm and the separation between the columns is 0,51 cm. Objective: The purpose of this research is to explore respectively the impact of RPG and puzzle game on nursing students’ self-directed learning and technology acceptance. The study further discusses whether different game types bring about different influences on students’ self-directed learning and technology acceptance. Methods: A quasi-experimental design was adopted in this study so that repeated measures between two groups could be conveniently conducted. 103 nursing students from a nursing college in Northern Taiwan participated in the study. For three weeks of experiment, the experiment group (n=52) received “traditional teaching + RPG” while the control group (n=51) received “traditional teaching + puzzle games”. Results: 1. On self-directed learning: For each game type, there were significant differences for the delayed tests of both groups as compared to the pre and post-tests of each group. However, there were no significant differences between the two game types. 2. On technology acceptance: For the experiment group, after the intervention of RPG, there were no significant differences concerning technology acceptance. For the control group, after the intervention of puzzle games, there were significant differences regarding technology acceptance. Pearson-correlation coefficient and path analysis conducted on the results of the two groups revealed that the dimension were highly correlated and reached statistical significance. Yet, the comparison of technology acceptance between the two game types did not reach statistical significance. Conclusion and Recommend: This study found that through using different digital games on learning, nursing students have effectively improved their self-directed learning. Students’ technology acceptances were also high for the two different digital game types and each dimension was significantly correlated. The results of the experimental group showed that through the scenarios of RPG, students had a deeper understanding of medical terminology, which reached the ‘Understand’ dimension of Bloom’s taxonomy. The results of the control group indicated that digital puzzle games could help students memorize and review medical terminology, which reached the ‘Remember’ dimension of Bloom’s taxonomy. The findings suggest that teachers of medical terminology could use digital games to assist their teaching according to their goals on cognitive learning. Adequate use of those games could help improve students’ self-directed learning and further enhance their learning outcome on medical terminology.

Keywords: digital game-based learning, medical terminology, nursing education, self-directed learning, technology acceptance model

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Authors: Serge Provost, Yishan Zang

Abstract:

Following a review of various approaches that are utilized for classifying the tail behavior of a distribution, an easily implementable methodology that relies on an arctangent transformation is presented. The classification criterion is actually based on the difference between two specific quantiles of the transformed distribution. The resulting categories enable one to classify distributional tails as distinctly short, short, nearly medium, medium, extended medium and somewhat long, providing that at least two moments exist. Distributions possessing a single moment are said to be long tailed while those failing to have any finite moments are classified as having an extremely long tail. Several illustrative examples will be presented.

Keywords: arctangent transformation, tail classification, heavy-tailed distributions, distributional moments

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Authors: Karwan Khudhir

Abstract:

A cross-sectional study was carried out to determine the prevalence of musculoskeletal disorders (MSDs) and psychosocial factors associated with it, among Kurdistan nursing professionals. Simple random sampling was used to select 220 nurses and data were collected by self-administrative questionnaire. Results of the study showed that the overall prevalence of MSDs among Kurdistan nurses was 74% in different body regions and, by body regions, neck pain was reported to be the highest complaint of twelve-month MSDs (48.4%) compared to other body parts. Logistic regression analysis indicated 6 variables that are significantly associated with musculoskeletal disorders: smoking (OR=19.472, 95% CI: 5.396, 70.273), BMI (OR= 5.106, 95% CI: 1.735, 15.025), physical activity (OR=8.639, 95% CI: 3.075, 24.271), psychological demand (OR=6.685, 95% CI: 3.318, 13.468), social support (OR=3.143, 95% CI: 1.202, 4.814) and job satisfaction (OR=2.44, 95% CI: 1.04, 5.63). Prevention strategies and health education which emphasizes on psychosocial risk factors and how to improve working conditions should be introduced.

Keywords: Kurdistan Region, Iraq, musculoskeletal disorders, nurses, psycho-social factors

Procedia PDF Downloads 221