Search results for: nursing interventions classification

Authors: Huey-Yeu Yan, Ching-Ying Lee, Hung-Ru Lin

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Background: Physical assessment is a vital clinical nursing competence. The gap between conventional teaching method and the way e-generation students’ preferred could be bridged owing to the support of Internet technology, i.e. interacting with online media to manage learning works. Nursing instructors in the wake of new learning pattern of the e-generation students are challenged to actively adjust and make teaching contents and methods more versatile. Objective: The objective of this research is to explore the effects on teaching and learning with live webcast-assisted on a specific topic, Physical Assessment technique, on a designated group of young nursing majors. It’s hoped that, with a way of nursing instructing, more versatile learning resources may be provided to facilitate self-directed learning. Design: This research adopts a cross-sectional descriptive survey. The instructor demonstrated physical assessment techniques and operation procedures via live webcast broadcasted online to all students. It increased both the off-time interaction between teacher and students concerning teaching materials. Methods: A convenient sampling was used to recruit a total of 52 nursing-majors at a certain university. The nursing majors took two-hour classes of Physical Assessment per week for 18 weeks (36 hrs. in total). The instruction covered four units with live webcasting and then conducted an online anonymous survey of learning outcomes by questionnaire. The research instrument was the online questionnaire, covering three major domains—online media used, learning outcome evaluation and evaluation result. The data analysis was conducted via IBM SPSS Statistics Version 2.0. The descriptive statistics was undertaken to describe the analysis of basic data and learning outcomes. Statistical methods such as descriptive statistics, t-test, ANOVA, and Pearson’s correlation were employed in verification. Results: Results indicated the following five major findings. (1) learning motivation, about four fifth of the participants agreed the online instruction resources are very helpful in improving learning motivation and raising the learning interest. (2) learning needs, about four fifth of participants agreed it was helpful to plan self-directed practice after the instruction, and meet their needs of repetitive learning and/or practice at their leisure time. (3) learning effectiveness, about two third agreed it was helpful to reduce pre-exam anxiety, and improve their test scores. (4) course objects, about three fourth agreed that it was helpful to achieve the goal of ‘executing the complete Physical Assessment procedures with proper skills’. (5) finally, learning reflection, about all of participants agreed this experience of online instructing, learning, and practicing is beneficial to them, they recommend instructor to share with other nursing majors, and they will recommend it to fellow students too. Conclusions: Live webcasting is a low-cost, convenient, efficient and interactive resource to facilitate nursing majors’ motivation of learning, need of self-directed learning and practice, outcome of learning. When live webcasting is integrated into nursing teaching, it provides an opportunity of self-directed learning to promote learning effectiveness, as such to fulfill the teaching objective.

Keywords: innovative teaching, learning effectiveness, live webcasting, physical assessment technique

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Authors: Kenan Morani, Esra Kaya Ayana

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This study aimed to develop a novel pipeline for COVID-19 detection using a large and rigorously annotated database of computed tomography (CT) images. The pipeline consists of UNet-based segmentation, lung extraction, and a classification part, with the addition of optional slice removal techniques following the segmentation part. In this work, a batch normalization was added to the original UNet model to produce lighter and better localization, which is then utilized to build a full pipeline for COVID-19 diagnosis. To evaluate the effectiveness of the proposed pipeline, various segmentation methods were compared in terms of their performance and complexity. The proposed segmentation method with batch normalization outperformed traditional methods and other alternatives, resulting in a higher dice score on a publicly available dataset. Moreover, at the slice level, the proposed pipeline demonstrated high validation accuracy, indicating the efficiency of predicting 2D slices. At the patient level, the full approach exhibited higher validation accuracy and macro F1 score compared to other alternatives, surpassing the baseline. The classification component of the proposed pipeline utilizes a convolutional neural network (CNN) to make final diagnosis decisions. The COV19-CT-DB dataset, which contains a large number of CT scans with various types of slices and rigorously annotated for COVID-19 detection, was utilized for classification. The proposed pipeline outperformed many other alternatives on the dataset.

Keywords: classification, computed tomography, lung extraction, macro F1 score, UNet segmentation

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Authors: Guoliang Lu, Changhou Lu, Xueyong Li

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In action recognition, previous studies have demonstrated the effectiveness of using multiple features to improve the recognition performance. We focus on two practical issues: i) most studies use a direct way of concatenating/accumulating multi features to evaluate the similarity between two actions. This way could be too strong since each kind of feature can include different dimensions, quantities, etc; ii) in many studies, the employed classification methods lack of a flexible and effective mechanism to add new feature(s) into classification. In this paper, we explore an unified scheme based on recently-proposed multi-dimensional dynamic time warping (MD-DTW). Experiments demonstrated the scheme's effectiveness of combining multi-feature and the flexibility of adding new feature(s) to increase the recognition performance. In addition, the explored scheme also provides us an open architecture for using new advanced classification methods in the future to enhance action recognition.

Keywords: action recognition, multi features, dynamic time warping, feature combination

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Authors: Van Truong Pham

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Background: Self-management was described as a potential strategy for blood pressure control in patients with hypertension. However, the effects of self-management interventions on blood pressure, self-efficacy, medication adherence, and body mass index (BMI) in older adults with hypertension have not been systematically evaluated. We evaluated the effects of self-management interventions on systolic blood pressure (SBP) and diastolic blood pressure (DBP), self-efficacy, medication adherence, and BMI in hypertensive older adults. Methods: We followed the recommended guidelines of preferred reporting items for systematic reviews and meta-analyses. Searches in electronic databases including CINAHL, Cochrane Library, Embase, Ovid-Medline, PubMed, Scopus, Web of Science, and other sources were performed to include all relevant studies up to April 2019. Studies selection, data extraction, and quality assessment were performed by two reviewers independently. We summarized intervention effects as Hedges' g values and 95% confidence intervals (CI) using a random-effects model. Data were analyzed using Comprehensive Meta-Analysis software 2.0. Results: Twelve randomized controlled trials met our inclusion criteria. The results revealed that self-management interventions significantly improved blood pressure control, self-efficacy, medication adherence, whereas the effect of self-management on BMI was not significant in older adult patients with hypertension. The following Hedges' g (effect size) values were obtained: SBP, -0.34 (95% CI, -0.51 to -0.17, p < 0.001); DBP, -0.18 (95% CI, -0.30 to -0.05, p < 0.001); self-efficacy, 0.93 (95%CI, 0.50 to 1.36, p < 0.001); medication adherence, 1.72 (95%CI, 0.44 to 3.00, p=0.008); and BMI, -0.57 (95%CI, -1.62 to 0.48, p = 0.286). Conclusions: Self-management interventions significantly improved blood pressure control, self-efficacy, and medication adherence. However, the effects of self-management on obesity control were not supported by the evidence. Healthcare providers should implement self-management interventions to strengthen patients' role in managing their health care.

Keywords: self-management, meta-analysis, blood pressure control, self-efficacy, medication adherence, body mass index

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Authors: Zahra R. Almansoor

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Background: Selective serotonin reuptake inhibitors (SSRI’s) and cognitive behavioural therapy (CBT) are the main treatment methods used for patients with obsessive compulsive disorder (OCD) under the National Institute of Health and Care Excellence (NICE) guidelines. Yet many patients are left with residual symptoms or remit, so several other therapeutic approaches have been explored. Objective: The objective was to systematically review the available literature regarding the treatment efficacy of current and potential approaches and diagnostic strategies. Method: First, studies were examined concerning diagnosis, prognosis, and influencing factors. Then, one reviewer conducted a systematic search of six databases using stringent search terms. Results of studies exploring the efficacy of treatment interventions were analysed and compared separately for adults and children. This review was limited to randomised controlled trials (RCT’s) conducted from 2016 onwards, and an improved Y-BOCS (Yale- Brown obsessive compulsive scale) score was the primary outcome measure. Results: Technology-based interventions including internet-based cognitive behavioural therapy (iCBT) were deemed as potentially effective. Discrepancy remains about the benefits of SSRI use past one year, but potential medication adjuncts include amantadine. Treatments such as association splitting and family and mindfulness strategies also have future potential. Conclusion: A range of potential therapies exist, either as treatment adjuncts to current interventions or as sole therapies. To further improve efficacy, it may be necessary to remodel the current NICE stepped-care model, especially regarding the potential use of lower intensity, cheaper treatments, including iCBT. Although many interventions show promise, further research is warranted to confirm this.

Keywords: family and group treatment, mindfulness strategies, novel treatment approaches, standard treatment, technology-based interventions

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Authors: Yea-Pyng Lin

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Evidence-based nursing (EBN) can improve quality of patient care and reduce medical expenses. Development of training and consultation program according to nurses’ needs and difficulties is essential to promote their competence and self-efficacy in EBN. However, limited research evaluated the effects of EBN program on EBN self-efficacy among nurses. This study aimed to evaluate the effects of an EBN consultation program on self-efficacy and outcome expectancy of evidence-based practice (EBP) among nurses. A two-group pretest-posttest quasi-experimental design was used. A purposive sample of full-time nurses was recruited from a hospital. Experimental group (n=28) received the EBN consultation program including 18-hour EBN training courses, hand-on practices and group discussion by faculty mentors. Control group (n=33) received regular in-service education with no EBN program. All participants received baseline and post-test assessment using Chinese version of Self-Efficacy in EBP scale (SE-EBP) and Outcome Expectancy for EBP scale (OE-EBP). After receiving EBN consultation program, experimental group’s posttest scores of SE-EBP (t=-4.98, p<0.001) and OE-SEP (t=-3.65, p=0.001) were significantly higher than those of the pretests. By controlling the age and years of nursing work experience, the experimental group‘s SE-EBP(F=10.47, p=0.002) and OE-SEP(F=9.53, p=0.003) scores were significantly improved compared to those of the control group. EBN program focus on hand-on practice and group discussion by faculty mentors in addition to EBN training courses can improve EBP self-efficacy and outcome expectancy among nurses. EBN program focus on English literature reading, database searching, and appraisal practice according to nurses’ needs and difficulties can promote implementation of EBN.

Keywords: evidence-based nursing, evidence-based practice, consultation program, self-efficacy, outcome expectancy

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Authors: Anukriti Kumar, Tanmay Singh, Dinesh Kumar Vishwakarma

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Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Keywords: multiclass classification, convolution neural network, OpenCV

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Authors: Ebtehal Aljedaani, Maha Aljohani

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Privacy and security patterns are both important for developing software that protects users' data and privacy. Privacy patterns are designed to address common privacy problems, such as unauthorized data collection and disclosure. Security patterns are designed to protect software from attack and ensure reliability and trustworthiness. Using privacy and security patterns, software engineers can implement security and privacy by design principles, which means that security and privacy are considered throughout the software development process. These patterns are available to translate "security & privacy-by-design" into practical advice for software engineering. Previous research on privacy and security patterns has typically focused on one category of patterns at a time. This paper aims to bridge this gap by merging the two categories and identifying their similarities and differences. To do this, the authors conducted a systematic literature review of 25 research papers on privacy and security patterns. The papers were analysed based on the category of the pattern, the classification of the pattern, and the security requirements that the pattern addresses. This paper presents the results of a comprehensive review of privacy and security design patterns. The review is intended to help future IT designers understand the relationship between the two types of patterns and how to use them to design secure and privacy-preserving software. The paper provides a clear classification of privacy and security design patterns, along with examples of each type. The authors found that there is only one widely accepted classification of privacy design patterns, while there are several competing classifications of security design patterns. Three types of security design patterns were found to be the most commonly used.

Keywords: design patterns, security, privacy, classification of patterns, security patterns, privacy patterns

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Authors: R. R. Ramsheeja, R. Sreeraj

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For view the internal structures of the human body such as liver, brain, kidney etc have a wide range of different modalities for medical images are provided nowadays. Computer Tomography is one of the most significant medical image modalities. In this paper use CT liver images for study the use of automatic computer aided techniques to calculate the volume of the liver tumor. Segmentation method is used for the detection of tumor from the CT scan is proposed. Gaussian filter is used for denoising the liver image and Adaptive Thresholding algorithm is used for segmentation. Multiple Region Of Interest(ROI) based method that may help to characteristic the feature different. It provides a significant impact on classification performance. Due to the characteristic of liver tumor lesion, inherent difficulties appear selective. For a better performance, a novel proposed system is introduced. Multiple ROI based feature selection and classification are performed. In order to obtain of relevant features for Support Vector Machine(SVM) classifier is important for better generalization performance. The proposed system helps to improve the better classification performance, reason in which we can see a significant reduction of features is used. The diagnosis of liver cancer from the computer tomography images is very difficult in nature. Early detection of liver tumor is very helpful to save the human life.

Keywords: computed tomography (CT), multiple region of interest(ROI), feature values, segmentation, SVM classification

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Authors: Kailen Krame

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This paper presents a review of the literature on the efficacy of mindfulness-based interventions for reducing occupational stress and burnout among K-12 educators. An in-depth discussion of the incidence and impact of teacher stress and burnout is provided, as a wealth of literature indicates that this issue is of growing concern and has important implications not only for individual educators but also for students and the school as an organization. Given the evidence of a link between teacher wellbeing and student outcomes, this topic is worth further investigation in order to better understand sources of stress and burnout and propose adequate coping strategies for today’s educators. As a potential solution to teacher stress and burnout, mindfulness-based interventions are reviewed in-depth, and an overview of the history, diverse application, and effects of mindfulness practice are provided. Relevant outcomes of educator mindfulness training presented in the literature include increased emotional regulation, self-efficacy, and personal wellbeing, among several others. Lastly, additional implications and benefits of educator mindfulness training are explored, limitations of the current research are reviewed, and apparent best practices are proposed.

Keywords: educator burnout, emotional regulation, mindfulness, self-efficacy, stress-reduction

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Authors: Mayank Gupta, Siba Prasad Samal, Vasu Kakkirala

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The internet world and its priorities have changed considerably in the last decade. Browsing on smart phones has increased manifold and is set to explode much more. Users spent considerable time browsing different websites, that gives a great deal of insight into user’s preferences. Instead of plain information classifying different aspects of browsing like Bookmarks, History, and Download Manager into useful categories would improve and enhance the user’s experience. Most of the classification solutions are server side that involves maintaining server and other heavy resources. It has security constraints and maybe misses on contextual data during classification. On device, classification solves many such problems, but the challenge is to achieve accuracy on classification with resource constraints. This on device classification can be much more useful in personalization, reducing dependency on cloud connectivity and better privacy/security. This approach provides more relevant results as compared to current standalone solutions because it uses content rendered by browser which is customized by the content provider based on user’s profile. This paper proposes a Naive Bayes based lightweight classification engine targeted for a resource constraint devices. Our solution integrates with Web Browser that in turn triggers classification algorithm. Whenever a user browses a webpage, this solution extracts DOM Tree data from the browser’s rendering engine. This DOM data is a dynamic, contextual and secure data that can’t be replicated. This proposal extracts different features of the webpage that runs on an algorithm to classify into multiple categories. Naive Bayes based engine is chosen in this solution for its inherent advantages in using limited resources compared to other classification algorithms like Support Vector Machine, Neural Networks, etc. Naive Bayes classification requires small memory footprint and less computation suitable for smartphone environment. This solution has a feature to partition the model into multiple chunks that in turn will facilitate less usage of memory instead of loading a complete model. Classification of the webpages done through integrated engine is faster, more relevant and energy efficient than other standalone on device solution. This classification engine has been tested on Samsung Z3 Tizen hardware. The Engine is integrated into Tizen Browser that uses Chromium Rendering Engine. For this solution, extensive dataset is sourced from dmoztools.net and cleaned. This cleaned dataset has 227.5K webpages which are divided into 8 generic categories ('education', 'games', 'health', 'entertainment', 'news', 'shopping', 'sports', 'travel'). Our browser integrated solution has resulted in 15% less memory usage (due to partition method) and 24% less power consumption in comparison with standalone solution. This solution considered 70% of the dataset for training the data model and the rest 30% dataset for testing. An average accuracy of ~96.3% is achieved across the above mentioned 8 categories. This engine can be further extended for suggesting Dynamic tags and using the classification for differential uses cases to enhance browsing experience.

Keywords: chromium, lightweight engine, mobile computing, Naive Bayes, Tizen, web browser, webpage classification

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Authors: Doyin Afolabi, Phillip Adewole, Oladipupo Sennaike

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Most well-known classifiers, including the decision tree algorithm, can make predictions on balanced datasets efficiently. However, the decision tree algorithm tends to be biased towards imbalanced datasets because of the skewness of the distribution of such datasets. To overcome this problem, this study proposes a weighted decision tree algorithm that aims to remove the bias toward the majority class and prevents the reduction of majority observations in imbalance datasets classification. The proposed weighted decision tree algorithm was tested on three imbalanced datasets- cancer dataset, german credit dataset, and banknote dataset. The specificity, sensitivity, and accuracy metrics were used to evaluate the performance of the proposed decision tree algorithm on the datasets. The evaluation results show that for some of the weights of our proposed decision tree, the specificity, sensitivity, and accuracy metrics gave better results compared to that of the ID3 decision tree and decision tree induced with minority entropy for all three datasets.

Keywords: data mining, decision tree, classification, imbalance dataset

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Authors: Eiman Kattan

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This study aims to evaluate the impact of classifying labelled remote sensing images conventional neural network (CNN) architecture, i.e., AlexNet on different land cover scenarios based on two remotely sensed datasets from different point of views such as the computational time and performance. Thus, a set of experiments were conducted to specify the effectiveness of the selected convolutional neural network using two implementing approaches, named fully trained and fine-tuned. For validation purposes, two remote sensing datasets, AID, and RSSCN7 which are publicly available and have different land covers features were used in the experiments. These datasets have a wide diversity of input data, number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in training, validation, and testing. As a result, the fully trained approach has achieved a trivial result for both of the two data sets, AID and RSSCN7 by 73.346% and 71.857% within 24 min, 1 sec and 8 min, 3 sec respectively. However, dramatic improvement of the classification performance using the fine-tuning approach has been recorded by 92.5% and 91% respectively within 24min, 44 secs and 8 min 41 sec respectively. The represented conclusion opens the opportunities for a better classification performance in various applications such as agriculture and crops remote sensing.

Keywords: conventional neural network, remote sensing, land cover, land use

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Authors: Arian Hosseini, Mahmudul Hasan

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To address the increasing need for efficient and accurate content moderation, we propose an efficient and lightweight deep classification ensemble structure. Our approach is based on a combination of simple visual features, designed for high-accuracy classification of violent content with low false positives. Our ensemble architecture utilizes a set of lightweight models with narrowed-down color features, and we apply it to both images and videos. We evaluated our approach using a large dataset of explosion and blast contents and compared its performance to popular deep learning models such as ResNet-50. Our evaluation results demonstrate significant improvements in prediction accuracy, while benefiting from 7.64x faster inference and lower computation cost. While our approach is tailored to explosion detection, it can be applied to other similar content moderation and violence detection use cases as well. Based on our experiments, we propose a "think small, think many" philosophy in classification scenarios. We argue that transforming a single, large, monolithic deep model into a verification-based step model ensemble of multiple small, simple, and lightweight models with narrowed-down visual features can possibly lead to predictions with higher accuracy.

Keywords: deep classification, content moderation, ensemble learning, explosion detection, video processing

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Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

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Harbor protection systems are so important. The need for automatic protection systems has increased over the last years. Diver detection active sonar has great significance. It used to detect underwater threats such as divers and autonomous underwater vehicle. To automatically detect such threats the sonar image is processed by algorithms. These algorithms used to detect, track and classify of underwater objects. In this work, divers tracking and classification algorithm is improved be proposing a robust wake detection method. To detect objects the sonar images is normalized then segmented based on fixed threshold. Next, the centroids of the segments are found and clustered based on distance metric. Then to track the objects linear Kalman filter is applied. To reduce effect of noise and creation of false tracks, the Kalman tracker is fine tuned. The tuning is done based on our active sonar specifications. After the tracks are initialed and updated they are subjected to a filtering stage to eliminate the noisy and unstable tracks. Also to eliminate object with a speed out of the diver speed range such as buoys and fast boats. Afterwards the result tracks are subjected to a classification stage to deiced the type of the object been tracked. Here the classification stage is to deice wither if the tracked object is an open circuit diver or a close circuit diver. At the classification stage, a small area around the object is extracted and a novel wake detection method is applied. The morphological features of the object with his wake is extracted. We used support vector machine to find the best classifier. The sonar training images and the test images are collected by ARMELSAN Defense Technologies Company using the portable diver detection sonar ARAS-2023. After applying the algorithm to the test sonar data, we get fine and stable tracks of the divers. The total classification accuracy achieved with the diver type is 97%.

Keywords: harbor protection, diver detection, active sonar, wake detection, diver classification

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Authors: Salima Smiti, Ines Gasmi, Makram Soui

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Credit risk is the most important issue for financial institutions. Its assessment becomes an important task used to predict defaulter customers and classify customers as good or bad payers. To this objective, numerous techniques have been applied for credit risk assessment. However, to our knowledge, several evaluation techniques are black-box models such as neural networks, SVM, etc. They generate applicants’ classes without any explanation. In this paper, we propose to assess credit risk using rules classification method. Our output is a set of rules which describe and explain the decision. To this end, we will compare seven classification algorithms (JRip, Decision Table, OneR, ZeroR, Fuzzy Rule, PART and Genetic programming (GP)) where the goal is to find the best rules satisfying many criteria: accuracy, sensitivity, and specificity. The obtained results confirm the efficiency of the GP algorithm for German and Australian datasets compared to other rule-based techniques to predict the credit risk.

Keywords: credit risk assessment, classification algorithms, data mining, rule extraction

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Authors: Yasaman Mohammadi

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Emotion regulation is a complex process that allows individuals to manage and modulate their emotional responses in order to adaptively respond to environmental demands. As individuals age, emotion regulation abilities may decline, leading to an increased vulnerability to mood disorders and other negative health outcomes. Advances in neuroimaging techniques have greatly enhanced our understanding of the neural substrates underlying emotion regulation and age-related changes in these neural systems. Additionally, genetic research has identified several candidate genes that may influence age-related changes in emotion regulation. In this paper, we review recent findings from neuroimaging and genetic research on age-related changes in the neural substrates of emotion regulation, highlighting the mechanisms and consequences of these changes. We also discuss potential interventions, including cognitive and behavioral approaches, that may be effective in mitigating age-related declines in emotion regulation. We propose that a better understanding of the mechanisms underlying age-related changes in emotion regulation may lead to the development of more targeted interventions aimed at promoting healthy emotional functioning in older adults. Overall, this paper highlights the importance of studying age-related changes in emotion regulation and provides a roadmap for future research in this field.

Keywords: emotion regulation, aging, neural substrates, neuroimaging, emotional functioning, healthy aging

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Authors: Yulong Wang, Yuan Yan Tang, Cuiming Zou, Lina Yang

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This paper presents a novel sparse representation method for robust pattern classification. Generalized orthogonal matching pursuit (GOMP) is a recently proposed efficient sparse representation technique. However, GOMP adopts the mean square error (MSE) criterion and assign the same weights to all measurements, including both severely and slightly corrupted ones. To reduce the limitation, we propose an information-theoretic GOMP (ITGOMP) method by exploiting the correntropy induced metric. The results show that ITGOMP can adaptively assign small weights on severely contaminated measurements and large weights on clean ones, respectively. An ITGOMP based classifier is further developed for robust pattern classification. The experiments on public real datasets demonstrate the efficacy of the proposed approach.

Keywords: correntropy induced metric, matching pursuit, pattern classification, sparse representation

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Authors: Ahlam Alnatour

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Background: To our knowledge, there is lack of studies that describe the experience of studying health promotion courses using an interactive approach, and compare students’ and teachers perceptions about this method of teaching. The purpose of this study is to provide a comparison between student and teacher experiences and perspectives in learning health promotion course using interactive learning. Design: A descriptive qualitative design was used to provide an in-depth description and understanding of students’ and teachers experiences and perceptions of learning health promotion courses using an interactive learning. Study Participants: About 14 fourteen students (seven male, seven female) and eight teachers at governmental university in northern Jordan participated in this study. Data Analysis: Conventional content analysis approach was used for participants’ scripts to gain an in-depth description for both students' and teacher’s experiences. Results: The main themes emerged from the data analysis describing the students’ and teachers perceptions of the interactive health promotion class: teachers’ and students positive experience in adopting interactive learning, advantages and benefits of interactive teaching, barriers to interactive teaching, and suggestions for improvement. Conclusion: Both teachers and students reflected positive attitudes toward interactive learning. Interactive learning helped to engage in learning process physically and cognitively. Interactive learning enhanced learning process, promote student attention, enhanced final performance, and satisfied teachers and students accordingly. Interactive learning approach should be adopted in teaching graduate and undergraduate courses using updated and contemporary strategies. Nursing scholars and educators should be motivated to integrate interactive learning in teaching different nursing courses.

Keywords: interactive learning, nursing, health promotion, qualitative study

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Authors: Qi Xiu, Hiromu Hota, Yohsuke Ishii, Takuya Oda

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Recently, collectable manufacturing data are rapidly increasing. On the other hand, mega recall is getting serious as a social problem. Under such circumstances, there are increasing needs for preventing mega recalls by defect analysis such as root cause analysis and abnormal detection utilizing manufacturing data. However, the time to classify strings in manufacturing data by traditional method is too long to meet requirement of quick defect analysis. Therefore, we present String Length Distribution Classification method (SLDC) to correctly classify strings in a short time. This method learns character features, especially string length distribution from Product ID, Machine ID in BOM and asset list. By applying the proposal to strings in actual manufacturing data, we verified that the classification time of strings can be reduced by 80%. As a result, it can be estimated that the requirement of quick defect analysis can be fulfilled.

Keywords: string classification, data quality, feature selection, probability distribution, string length

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Authors: Xiao Chen, Alice Ho, Mabel Tie, Xiaoheng Xu

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Background and Significance of the Study: School-enterprise cooperation has been the cornerstone of vocational education in China and many other countries. Researchers and policymakers have paid much attention to ensuring the implementation and improving the quality of school-enterprise cooperation. However, many problems still exist on the implementation level of the cooperation. On the one hand, the enterprises lack the motivation to participate in the cooperation. On the other hand, there is a lack of effective guidance and management during the cooperation. Furthermore, the current literature focuses greatly on policy recommendations on the national level while failing to provide a detailed practical understanding of how school-enterprise cooperation is carried out on the ground level. With emerging social problems, such as the aging population in China, there is an increasing need for diverse nursing services and better nursing quality. Methodology: To gain a deeper understanding of the influencing factors of the implementation of school-enterprise cooperation, this work conducted 37 exploratory interviews in four Chinese cities spanning first-tier to fourth-tier cities with hospital department directors, vocational school deans, nurses, and vocational students. Multiple critical policy documents that founded the current vocational education system in China were analyzed, along with the data collected from the interviews. Major Findings: Based on the policy and interview analyses, this work reveals a set of influencing factors for school-enterprise cooperation implementation. Findings from each region contribute to an overall model of influencing factors for implementing school-enterprise cooperation in vocational nursing education in China, which leads to practical insights for policy recommendation. The key influencing factors are found based on the policy, hospital, school, and social levels. Following practical policy recommendations were presented. Moving forward, further research on the implementation of school-enterprise cooperation in specific industries will become increasingly critical to improving the effectiveness of educational policies and the quality of vocational education.

Keywords: nursing, policy recommendation, school-enterprise cooperation, vocational education

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Authors: Yun Chan Lee

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Background: End-of-life care is a large area of all nursing practice and student nurses are likely to meet dying patients in many placement areas. It is therefore important to understand the emotional responses and coping strategies of student nurses in order for nursing education systems to have some appreciation of how nursing students might be supported in the future. Methodology: This research used a qualitative phenomenological approach. Six student nurses understanding a degree-level adult nursing course were interviewed. Their responses to questions were analyzed using interpretative phenomenological analysis. Finding: The findings identified 3 main themes. First, the common experience of ‘unpreparedness’. A very small number of participants felt that this was unavoidable and that ‘no preparation is possible’, the majority felt that they were unprepared because of ‘insufficient input’ from the university and as a result of wider ‘social taboos’ around death and dying. The second theme showed that emotions were affected by ‘the personal connection to the patient’ and the important sub-themes of ‘the evoking of memories’, ‘involvement in care’ and ‘sense of responsibility’. The third theme, the coping strategies used by students, seemed to fall into two broad areas those ‘internal’ with the student and those ‘external’. In terms of the internal coping strategies, ‘detachment’, ‘faith’, ‘rationalization’ and ‘reflective skills’ are the important components of this part. Regarding the external coping strategies, ‘clinical staff’ and ‘the importance of family and friends’ are the importance of accessing external forms of support. Implication: It is clear that student nurses are affected emotionally by caring for dying patients and many of them have apprehension even before they begin on their placements but very often this is unspoken. Those anxieties before the placement become more pronounced during and continue after the placements. This has implications for when support is offered and possibly its duration. Another significant point of the study is that participants often highlighted their wish to speak to qualified nurses after their experiences of being involved in end-of-life care and especially when they had been present at the time of death. Many of the students spoke that qualified nurses were not available to them. This seemed to be due to a number of reasons. Because the qualified nurses were not available, students had to make use of family members and friends to talk to. Consequently, the implication of this study is not only to educate student nurses but also to educate the qualified mentors on the importance of providing emotional support to students.

Keywords: nursing students, coping strategies, end-of-life care, emotional responses

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Authors: Samiah Alammari, Nassim Ammour

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When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on HSI dataset Indian Pines. The results confirm the capability of the proposed method.

Keywords: continual learning, data reconstruction, remote sensing, hyperspectral image segmentation

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Authors: T. Frawley, G. O'Kelly

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The UCD School of Nursing, Midwifery and Health Systems Higher Diploma in Mental Health (HDMH) nursing programme commenced in January 2017. Forty students successfully completed the programme. Programme evaluation was conducted from the outset. Research ethics approval was granted by the UCD Human Research Ethics Committee – Sciences in November 2016 (LS-E-16-163). Plan for Sustainability: Each iteration of the programme continues to be evaluated and adjusted accordingly. Aims: The ultimate purpose of the HDMH programme is to prepare registered nurses (registered children’s nurse (RCN), registered nurse in intellectual disability (RNID) and registered general nurse (RGN)) to function as effective registered psychiatric nurses in all settings which provide care and treatment for people experiencing mental health difficulties. Curriculum evaluation is essential to ensure that the programme achieves its purpose, that aims and expected outcomes are met and that required changes are highlighted for the programme’s continuing positive development. Methods: Both quantitative and qualitative methods were used in the evaluation. A series of questionnaires were used (the majority pre and post programme) to determine student perceptions of the programme, behaviour and attitudinal change from commencement to completion. These included the student assessment of learning gains (SALG); mental health knowledge schedule (MAKS); mental health clinician attitudes scale (MICA); reported and intended behaviour scale (RIBS); and community attitudes towards the mentally ill (CAMI). In addition, student and staff focus groups were conducted. Evaluation methods also incorporated module feedback. Outcome/Results: The evaluation highlighted a very positive response in relation to the achievement of programme outcomes and preparation for future work as registered psychiatric nursing. Some areas were highlighted for further development, which have been taken cognisance of in the 2019 iteration of the programme.

Keywords: learning gains, mental health, nursing, stigma

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Authors: Jularat Chumnaul

Abstract:

In forensic science, corpses from various homicides are different; there are both complete and incomplete, depending on causes of death or forms of homicide. For example, some corpses are cut into pieces, some are camouflaged by dumping into the river, some are buried, some are burned to destroy the evidence, and others. If the corpses are incomplete, it can lead to the difficulty of personally identifying because some tissues and bones are destroyed. To specify gender of the corpses from skeletal remains, the most precise method is DNA identification. However, this method is costly and takes longer so that other identification techniques are used instead. The first technique that is widely used is considering the features of bones. In general, an evidence from the corpses such as some pieces of bones, especially the skull and pelvis can be used to identify their gender. To use this technique, forensic scientists are required observation skills in order to classify the difference between male and female bones. Although this technique is uncomplicated, saving time and cost, and the forensic scientists can fairly accurately determine gender by using this technique (apparently an accuracy rate of 90% or more), the crucial disadvantage is there are only some positions of skeleton that can be used to specify gender such as supraorbital ridge, nuchal crest, temporal lobe, mandible, and chin. Therefore, the skeletal remains that will be used have to be complete. The other technique that is widely used for gender specifying in forensic science and archeology is skeletal measurements. The advantage of this method is it can be used in several positions in one piece of bones, and it can be used even if the bones are not complete. In this study, the classification and cluster analysis are applied to this technique, including the Kth Nearest Neighbor Classification, Classification Tree, Ward Linkage Cluster, K-mean Cluster, and Two Step Cluster. The data contains 507 particular individuals and 9 skeletal measurements (diameter measurements), and the performance of five methods are investigated by considering the apparent error rate (APER). The results from this study indicate that the Two Step Cluster and Kth Nearest Neighbor method seem to be suitable to specify gender from human skeletal remains because both yield small apparent error rate of 0.20% and 4.14%, respectively. On the other hand, the Classification Tree, Ward Linkage Cluster, and K-mean Cluster method are not appropriate since they yield large apparent error rate of 10.65%, 10.65%, and 16.37%, respectively. However, there are other ways to evaluate the performance of classification such as an estimate of the error rate using the holdout procedure or misclassification costs, and the difference methods can make the different conclusions.

Keywords: skeletal measurements, classification, cluster, apparent error rate

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Authors: Evan Lowhorn, Rocio Alba-Flores

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The purpose of this work is to develop a method for classifying hand signals and using the output in a drone control algorithm. To achieve this, methods based on Convolutional Neural Networks (CNN) were applied. CNN's are a subset of deep learning, which allows grid-like inputs to be processed and passed through a neural network to be trained for classification. This type of neural network allows for classification via imaging, which is less intrusive than previous methods using biosensors, such as EMG sensors. Classification CNN's operate purely from the pixel values in an image; therefore they can be used without additional exteroceptive sensors. A development bench was constructed using a desktop computer connected to a high-definition webcam mounted on a scissor arm. This allowed the camera to be pointed downwards at the desk to provide a constant solid background for the dataset and a clear detection area for the user. A MATLAB script was created to automate dataset image capture at the development bench and save the images to the desktop. This allowed the user to create their own dataset of 12,000 images within three hours. These images were evenly distributed among seven classes. The defined classes include forward, backward, left, right, idle, and land. The drone has a popular flip function which was also included as an additional class. To simplify control, the corresponding hand signals chosen were the numerical hand signs for one through five for movements, a fist for land, and the universal “ok” sign for the flip command. Transfer learning with PyTorch (Python) was performed using a pre-trained 18-layer residual learning network (ResNet-18) to retrain the network for custom classification. An algorithm was created to interpret the classification and send encoded messages to a Ryze Tello drone over its 2.4 GHz Wi-Fi connection. The drone’s movements were performed in half-meter distance increments at a constant speed. When combined with the drone control algorithm, the classification performed as desired with negligible latency when compared to the delay in the drone’s movement commands.

Keywords: classification, computer vision, convolutional neural networks, drone control

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Authors: B. Schofield, F. Gul, S. McClean, R. Hoskins, R. Terry, U. Rolfe, A. Gibson, S. Voss, J. Benger

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Aim: The aim of this study is to determine whether and how health promotion activities are undertaken by paramedics and emergency department nurses and investigate ways of overcoming potential barriers. Background: Paramedics and emergency department nurses are uniquely placed to reach millions of people and could use these contacts as positive opportunities to help people improve their health by identifying people with risk factors and provide information, brief interventions, and signposting to locally provided services. These interventions can be carried out when the opportunity arises, typically take no more than a few minutes, have a low financial cost and can be a highly efficient method of health promotion. Methodology: Three NHS Emergency Departments and four Ambulance Trusts in England were recruited to the study. A link to an online survey was distributed to paramedics and emergency department nurses at participating sites. Staff were invited to participate in virtual semi-structured interviews. Patients seen, treated, and discharged at the participating sites were invited to virtual semistructured interviews. Findings: A total of 331 survey responses were received, 21 virtual semi-structured staff interviews and 11 patient interviews were completed. Staff reported lack of time to prioritise, lack of knowledge, resources, and confidence as barriers. Receptiveness of patients guided their decision to undertake health promotion activities. They reported a desire to learn how to undertake health promotion conversations. Emergency department nurses felt more supported than paramedics by their organisations to undertake health promotion activities. Patients were not aware of health promotion activities and reported fear and lack of privacy as barriers. Conclusions: These results will guide the development of an intervention to support the provision of health promotion by staff in urgent and emergency care settings. The components of the intervention will be mapped to a framework which will consider the needs of staff working within these settings, patients they treat, and organisational issues and practices related to the implementation of such an intervention.

Keywords: emergency service, hospital, nursing, allied health personnel, emergency medical services, health promotion

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Authors: Daniel D'Souza, Ping Zou

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The period before, during, and after menopause is a sensitive time for women as they experience intense physical and psychological health changes and symptoms. These changes accompany the hormonal changes that mark the end of a woman’s reproductive age. To help mitigate and cope with these changes, prompt and correct treatment is needed. eHealth has emerged as a branch of telemedicine in the past few decades as an alternate avenue for patients to receive care quickly and conveniently, as it relies on the Internet and computers. Within the past few years, eHealth has also given rise to mHealth, which is the use of personal mobile devices to receive treatment and care. However, there is a lack of study on their use for menopause. This review aimed to review and summarize the literature for eHealth or mHealth and menopause. Several databases related to women’s health and digital health were searched for original studies about eHealth or mHealth and menopause. The search yielded 25 results. The results were generally positive, with these interventions being feasible and having positive effects on physical and psychosocial outcomes. However, several issues were raised regarding their design process that may inadvertently prevent these interventions from addressing the needs of all potential users. Therefore, while eHealth and mHealth certainly represent a future model of healthcare delivery for menopausal women, further research and design modifications are needed before this can happen.

Keywords: eHealth, menopause, mHealth, midlife women

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Authors: Javier Alfonso Bonilla-Chávez, Angélica Lozano

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In North America, more than 2,000 people annually die in accidents related to railroad tracks. In 2020, collisions at grade crossings were the main cause of deaths related to railway accidents in Mexico. Railway networks have constant interaction with motor transport users, cyclists, and pedestrians, mainly in grade crossings, where is the greatest vulnerability and risk of accidents. Usually, accidents at grade crossings are directly related to risky behavior and non-compliance with regulations by motorists, cyclists, and pedestrians, especially in developing countries. Around the world, countries classify these crossings in different ways. In Mexico, according to their dangerousness (high, medium, or low), types A, B and C have been established, recommending for each one different type of auditive and visual signaling and gates, as well as horizontal and vertical signaling. This classification is based in a weighting, but regrettably, it is not explained how the weight values were obtained. A review of the variables and the current approach for the grade crossing classification is required, since it is inadequate for some crossings. In contrast, North America (USA and Canada) and European countries consider a broader classification so that attention to each crossing is addressed more precisely and equipment costs are adjusted. Lack of a proper classification, could lead to cost overruns in the equipment and a deficient operation. To exemplify the lack of a good classification, six crossings are studied, three located in the rural area of Mexico and three in Mexico City. These cases show the need of: improving the current regulations, improving the existing infrastructure, and implementing technological systems, including informative signals with nomenclature of the involved crossing and direct telephone line for reporting emergencies. This implementation is unaffordable for most municipal governments. Also, an inventory of the most dangerous grade crossings in urban and rural areas must be obtained. Then, an approach for improving the classification of grade crossings is suggested. This approach must be based on criteria design, characteristics of adjacent roads or intersections which can influence traffic flow through the crossing, accidents related to motorized and non-motorized vehicles, land use and land management, type of area, and services and economic activities in the zone where the grade crossings is located. An expanded classification of grade crossing in Mexico could reduce accidents and improve the efficiency of the railroad.

Keywords: accidents, grade crossing, railroad, traffic safety

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Authors: P. S. Jagadeesh Kumar, Yang Yung, Wenli Hu

Abstract:

Speech emotion classification is a dominant research field in finding a sturdy and profligate classifier appropriate for different real-life applications. This effort accentuates on classifying different emotions from speech signal quarried from the features related to pitch, formants, energy contours, jitter, shimmer, spectral, perceptual and temporal features. Tensor deep stacking neural networks were supported to examine the factors that influence the classification success rate. Facial electromyography signals were composed of several forms of focuses in a controlled atmosphere by means of audio-visual stimuli. Proficient facial electromyography signals were pre-processed using moving average filter, and a set of arithmetical features were excavated. Extracted features were mapped into consistent emotions using bilinear mapping. With facial electromyography signals, a database comprising diverse emotions will be exposed with a suitable fine-tuning of features and training data. A success rate of 92% can be attained deprived of increasing the system connivance and the computation time for sorting diverse emotional states.

Keywords: speech emotion classification, tensor deep stacking neural networks, facial electromyography, bilinear mapping, audio-visual stimuli

Procedia PDF Downloads 254