Search results for: nursing interventions classification

Authors: Gladys Gakenia Njoroge

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Mathematics is a compulsory subject in both primary and secondary schools in Kenya. However, learners’ poor performance in the subject in Kenya national examinations year in year out remains a serious concern for teachers of Mathematics, parents, curriculum developers, and the general public. This is particularly worrying because of the importance attached to the subject in national development hence the need to find out what could be affecting learning of Mathematics in Kenyan schools. The research on which this paper is based sought to examine the factors that influence performance in Mathematics in Kenyan schools; identify the characteristics of Mathematics learning disabilities; determine how the learners with such learning disabilities can be assessed and identified and interventions for these difficulties implemented. A case study was undertaken on class six learners in a primary school in Nairobi County. The tools used for the research were: classroom observations and an Individualized Education Program (IEP) developed by the teachers with the help of the researcher. This paper therefore highlights the findings from the research, discusses the implications of the findings and suggests the way forward as far as teaching, learning and assessment of Mathematics in Kenyan schools is concerned. Perhaps with the application of the right interventions, poor performance in Mathematics in the national examinations in Kenya will be a thing of the past.

Keywords: demystifying mathematics, individualized education program, learning difficulties, assessment

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Authors: Razak Zakariya, Fazliana Mustajap, Lenny Sharinee Sakai

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A sediment map is quite important in the marine environment. The sediment itself contains thousands of information that can be used for other research. This study was conducted by using a multibeam echo sounder Reson T20 on 15 August 2020 at the Karang Sela (coral reef area) at Pulau Bidong. The study aims to identify the sediment type around the coral reef by using bathymetry and backscatter data. The sediment in the study area was collected as ground truthing data to verify the classification of the seabed. A dry sieving method was used to analyze the sediment sample by using a sieve shaker. PDS 2000 software was used for data acquisition, and Qimera QPS version 2.4.5 was used for processing the bathymetry data. Meanwhile, FMGT QPS version 7.10 processes the backscatter data. Then, backscatter data were analyzed by using the maximum likelihood classification tool in ArcGIS version 10.8 software. The result identified three types of sediments around the coral which were very coarse sand, coarse sand, and medium sand.

Keywords: sediment type, MBES echo sounder, backscatter, ArcGIS

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Authors: Judson P. LaGrone

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Simulation-based education in curricula was once a luxurious component of nursing programs but now serves as a vital element of an individual’s learning experience. A debriefing occurs after the simulation scenario or clinical experience is completed to allow the instructor(s) or trained professional(s) to act as a debriefer to guide a reflection with a purpose of acknowledging, assessing, and synthesizing the thought process, decision-making process, and actions/behaviors performed during the scenario or clinical experience. Debriefing is a vital component of the simulation process and educational experience to allow the learner(s) to progressively build upon past experiences and current scenarios within a safe and welcoming environment with a guided dialog to enhance future practice. The aim of this integrative review was to assess current practices of debriefing models in simulation-based education for health care professionals and students. The following databases were utilized for the search: CINAHL Plus, Cochrane Database of Systemic Reviews, EBSCO (ERIC), PsycINFO (Ovid), and Google Scholar. The advanced search option was useful to narrow down the search of articles (full text, Boolean operators, English language, peer-reviewed, published in the past five years). Key terms included debrief, debriefing, debriefing model, debriefing intervention, psychological debriefing, simulation, simulation-based education, simulation pedagogy, health care professional, nursing student, and learning process. Included studies focus on debriefing after clinical scenarios of nursing students, medical students, and interprofessional teams conducted between 2015 and 2020. Common themes were identified after the analysis of articles matching the search criteria. Several debriefing models are addressed in the literature with similarities of effectiveness for participants in clinical simulation-based pedagogy. Themes identified included (a) importance of debriefing in simulation-based pedagogy, (b) environment for which debriefing takes place is an important consideration, (c) individuals who should conduct the debrief, (d) length of debrief, and (e) methodology of the debrief. Debriefing models supported by theoretical frameworks and facilitated by trained staff are vital for a successful debriefing experience. Models differed from self-debriefing, facilitator-led debriefing, video-assisted debriefing, rapid cycle deliberate practice, and reflective debriefing. A reoccurring finding was centered around the emphasis of continued research for systematic tool development and analysis of the validity and effectiveness of current debriefing practices. There is a lack of consistency of debriefing models among nursing curriculum with an increasing rate of ill-prepared faculty to facilitate the debriefing phase of the simulation.

Keywords: debriefing model, debriefing intervention, health care professional, simulation-based education

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Authors: Vesile Evrim, Aliyu Awwal

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By the evolvement in technology, the way of expressing opinions switched the direction to the digital world. The domain of politics as one of the hottest topics of opinion mining research merged together with the behavior analysis for affiliation determination in text which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 are constituted by Linguistic Inquiry and Word Count (LIWC) features are tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that Decision Tree, Rule Induction and M5 Rule classifiers when used with SVM and IGR feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “function” as an aggregate feature of the linguistic category, is obtained as the most differentiating feature among the 68 features with 81% accuracy by itself in classifying articles either as Republican or Democrat.

Keywords: feature selection, LIWC, machine learning, politics

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Authors: Kasra Majbouri Yazdi, Adel Majbouri Yazdi, Saeid Khodayi, Jingyu Hou, Wanlei Zhou, Saeed Saedy

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Fake news and false information are big challenges of all types of media, especially social media. There is a lot of false information, fake likes, views and duplicated accounts as big social networks such as Facebook and Twitter admitted. Most information appearing on social media is doubtful and in some cases misleading. They need to be detected as soon as possible to avoid a negative impact on society. The dimensions of the fake news datasets are growing rapidly, so to obtain a better result of detecting false information with less computation time and complexity, the dimensions need to be reduced. One of the best techniques of reducing data size is using feature selection method. The aim of this technique is to choose a feature subset from the original set to improve the classification performance. In this paper, a feature selection method is proposed with the integration of K-means clustering and Support Vector Machine (SVM) approaches which work in four steps. First, the similarities between all features are calculated. Then, features are divided into several clusters. Next, the final feature set is selected from all clusters, and finally, fake news is classified based on the final feature subset using the SVM method. The proposed method was evaluated by comparing its performance with other state-of-the-art methods on several specific benchmark datasets and the outcome showed a better classification of false information for our work. The detection performance was improved in two aspects. On the one hand, the detection runtime process decreased, and on the other hand, the classification accuracy increased because of the elimination of redundant features and the reduction of datasets dimensions.

Keywords: clustering, fake news detection, feature selection, machine learning, social media, support vector machine

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Authors: Rahy Farooq, Maria Ahmad Khan, Ayesha Isani Majeed

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Objective: The main aim of this research is to assess the knowledge, attitude and practices of female nursing staff and nursing students regarding breast cancer, to provide a baseline for monitoring trends of breast cancer awareness in them. Background: Healthcare professionals are a direct source of information for the patients and the general public as a whole. It is, therefore, essential that the information they convey be accurate and helps in building additional awareness. However, clinical experience does not influence the knowledge, attitude and practices regarding breast cancer. Nurses, being the prime part of the healthcare professionals, play a significant role and hence, their awareness regarding this pressing issue is pertinent. Lack of awareness regarding common presenting symptoms or breast cancer risk factors translates to poor breast cancer screening practices and late diagnosis. Methodology: A cross-sectional study of 280 female nurses was conducted at a tertiary care hospital in Islamabad, Pakistan. A pre-tested structured questionnaire with additional variables like cultural barriers to seeking medical help was used. The scores for outcome variables including knowledge, attitude and practices were pre-defined. Data was analyzed using SPSSv23. Results: Of the 280 participants with a mean age of 28.99±9.98 years, 142 (50.7%) were married, and 138 (49.3%) were unmarried. Mean scores were computed to be 6.14±2.93 (out of 12), 0.30±0.7 (out of 3) and 9.53±1.92 (out of 16) for knowledge, attitude and practice respectively. Using independent sample T-test, a statistically significant correlation was found when means for the score of Attitude was compared with age. With a p-value of 0.018, 117 nurses of age more than 30 years, faced more practical, financial, emotional and service barriers as compared to 163 women younger than 30 years of age. Knowledge of age-related lifetime risks was also significantly poor more in single women; with a p-value of 0.006 for identification of correct age as a risk factor and a p-value of 0.005 for correct identification of risk for development of breast cancer in the lifetime of women. By application of Chi-square test, there was a significant correlation between marital status and cultural barriers to seeking medical help, showing that single women (58.7%) shy away from talking about breast cancer considering it a taboo (p-value 0.028) whereas, more married nurses (59.2%) were apprehensive that they might be considered at fault by the society, as compared to 40.8% of single nurses. (p-value 0.038). Conclusion: Owing to the scarcity of awareness among nurses, this study recognizes the need for delivering effective information to the female nurses regarding breast cancer. Educating patients is likely to be effective if the female nurses play their part and have correct attitudes towards breast cancer practices. A better understanding of the knowledge and practices regarding breast cancer among the nursing population will enable high-risk patients to be recognized early. Therefore, we recommend arrangement of special courses and seminars for all healthcare professionals including the nursing staff.

Keywords: breast cancer, cultural barriers, kap, nurses

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: L. Lindsay, S. A. Coleman, D. Kerr, B. J. Taylor, A. Moorhead

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Cognitive decline and frailty is apparent in older adults leading to an increased likelihood of the risk of falling. Currently health care professionals have to make professional decisions regarding such risks, and hence make difficult decisions regarding the future welfare of the ageing population. This study uses health data from The Irish Longitudinal Study on Ageing (TILDA), focusing on adults over the age of 50 years, in order to analyse health risk factors and predict the likelihood of falls. This prediction is based on the use of machine learning algorithms whereby health risk factors are used as inputs to predict the likelihood of falling. Initial results show that health risk factors such as long-term health issues contribute to the number of falls. The identification of such health risk factors has the potential to inform health and social care professionals, older people and their family members in order to mitigate daily living risks.

Keywords: classification, falls, health risk factors, machine learning, older adults

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Authors: Muhammad Ali Sarwar, Muhammad Farooq, Nayab Hassan, Hammad Hassan

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Pakistan is highly recognized for its agriculture and is well known for producing substantial amounts of wheat, cotton, and sugarcane. However, some factors contribute to a decline in crop quality and a reduction in overall output. One of the main factors contributing to this decline is the presence of weed and its late detection. This process of detection is manual and demands a detailed inspection to be done by the farmer itself. But by the time detection of weed, the farmer will be able to save its cost and can increase the overall production. The focus of this research is to identify and classify the four main types of weeds (Small-Flowered Cranesbill, Chick Weed, Prickly Acacia, and Black-Grass) that are prevalent in our region’s major crops. In this work, we implemented three different deep learning techniques: YOLO-v5, Inception-v3, and Deep CNN on the same Dataset, and have concluded that deep convolutions neural network performed better with an accuracy of 97.45% for such classification. In relative to the state of the art, our proposed approach yields 2% better results. We devised the architecture in an efficient way such that it can be used in real-time.

Keywords: deep convolution networks, Yolo, machine learning, agriculture

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Authors: Bushra Zafar, Usman Qamar

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Large data sample size and dimensions render the effectiveness of conventional data mining methodologies. A data mining technique are important tools for collection of knowledgeable information from variety of databases and provides supervised learning in the form of classification to design models to describe vital data classes while structure of the classifier is based on class attribute. Classification efficiency and accuracy are often influenced to great extent by noisy and undesirable features in real application data sets. The inherent natures of data set greatly masks its quality analysis and leave us with quite few practical approaches to use. To our knowledge first time, we present a new approach for investigation of structure and quality of datasets by providing a targeted analysis of localization of noisy and irrelevant features of data sets. Machine learning is based primarily on feature selection as pre-processing step which offers us to select few features from number of features as a subset by reducing the space according to certain evaluation criterion. The primary objective of this study is to trim down the scope of the given data sample by searching a small set of important features which may results into good classification performance. For this purpose, a heuristic for wrapper-based feature selection using genetic algorithm and for discriminative feature selection an external classifier are used. Selection of feature based on its number of occurrence in the chosen chromosomes. Sample dataset has been used to demonstrate proposed idea effectively. A proposed method has improved average accuracy of different datasets is about 95%. Experimental results illustrate that proposed algorithm increases the accuracy of prediction of different diseases.

Keywords: data mining, generic algorithm, KNN algorithms, wrapper based feature selection

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Authors: Parham Bakhtiari

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Music performance anxiety (MPA) is a prevalent concern among musicians, manifesting through cognitive, physiological, and behavioral symptoms that can severely impact performance quality and overall well-being. This systematic review synthesizes research on coping strategies employed by musicians to manage MPA from 2016 to 2023, identifying a range of psychological and physical interventions, including acceptance and commitment therapy (ACT), cognitive behavioral therapy (CBT), mindfulness, and yoga. Findings reveal that these interventions significantly reduce anxiety and enhance psychological resilience, with ACT showing notable improvements in psychological flexibility. Physical approaches also proved effective in mitigating physiological symptoms associated with MPA. However, challenges such as small sample sizes and methodological limitations hinder the generalizability of results. The review underscores the necessity for multi-faceted intervention strategies tailored to the unique needs of different musicians and emphasizes the importance of future research employing larger, randomized controlled designs to further validate these findings. Overall, this review serves as a comprehensive resource for musicians seeking effective coping strategies for managing performance anxiety, highlighting the critical interplay between mental and physical approaches in promoting optimal performance outcomes.

Keywords: anxiety, performance, coping, music, strategy

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Authors: Ammal M. Metwally, Walaa A. Basha, Ghada A. Abdel-Latif, Amira S. El Rifay

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Introduction: Improving breastfeeding practices does not always link to interventions relying on improving nutrition awareness and education alone but needs cultural and behavioral insights. Aim: Our study provided educational intervention through the use of the social marketing (SM) approach, which was respectable to societal norms allowing more conscious choices by mothers to achieve the maximum potential of physical growth of their infants. This study evaluated the effectiveness of the used approach for improving breastfeeding practices and the physical growth of infants aged up to 2 years. Methodology: A quasi-experimental intervention design with a posttest-only control design was done over three years duration to motivate mothers’ voluntary behavioral change toward breastfeeding promotion using SM principles: product, price, place, and promotion. The interventions targeted 464 pregnant women in their last trimester, mothers of children up to 2 years, and 1454 women in their childbearing period. Results: Most mothers showed increased awareness about the benefits of breastfeeding and became interested in breastfeeding their children outside the house using the breastfeeding cover (Gawn). Breastfeeding initiation, exclusive breastfeeding under six months, frequency of breastfeeding per day, and percentage of children who continued breastfeeding till two years were significantly increased (from 30%, 23 %, 56%, and 32% to 62 %, 47.3 %, 69 %, and 43.5 %). With the attention of the nutritional educational sessions three or more times, the majority of indicators had the most significant improvement. The females who recorded underweight results over males during the first two years of life significantly improved after the intervention (from 53.8 % to 15.4%, respectively). At the same time, females that were found to be obese before the intervention (7.7 %) became no longer obese. Conclusions: Nutritional interventions that are based on the use SM approach showed improvement for the majority of the key performance indicators. Although they doubled their value before the intervention, the majority were still modest (below 50 %). With sustained use of the SM approach, infants will achieve their maximum potential for physical growth by providing economically disadvantaged mothers with breastfeeding support.

Keywords: social marketing approach, early breastfeeding initiation, exclusive breastfeeding, responsiveness to cues of hunger and satiety, physical growth of infants

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Authors: Tsietsi J. Pilusa, Tumisang G. Seodigeng

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This study evaluates the benefits of advanced waste management practices in unlocking waste-to-energy opportunities within the solid waste industry. The key drivers of sustainable waste management practices, specifically with respect to packaging waste-to-energy technology options are discussed. The success of a waste-to-energy system depends significantly on the appropriateness of available technologies, including those that are well established as well as those that are less so. There are hard and soft interventions to be considered when packaging an integrated waste treatment solution. Technology compatibility with variation in feedstock (waste) quality and quantities remains a key factor. These factors influence the technology reliability in terms of production efficiencies and product consistency, which in turn, drives the supply and demand network. Waste treatment technologies rely on the waste material as feedstock; the feedstock varies in quality and quantities depending on several factors; hence, the technology fails, as a result. It is critical to design an advanced waste treatment technology in an integrated approach to minimize the possibility of technology failure due to unpredictable feedstock quality, quantities, conversion efficiencies, and inconsistent product yield or quality. An integrated waste-to-energy approach offers a secure system design that considers sustainable waste management practices.

Keywords: emerging markets, evaluation tool, interventions, waste treatment technologies

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Authors: Aaron Walayat

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Protection of the environment in international law has been one of the most developed in the context of international humanitarian law. This paper examines the history of the protection of the environment in times of armed conflict, beginning with the traditional notion of restraint observed in antiquity towards the obligation to protect the environment, examining the treaties and agreements, both binding and non-binding which have contributed to environmental protection in war. The paper begins with a discussion of the ancient concept of restraint. This section examines the social norms in favor of protection of the environment as observed in the Bible, Greco-Roman mythology, and even more contemporary literature. The study of the traditional rejection of total war establishes the social foundation on which the current legal regime has stemmed. The paper then studies the principle of restraint as codified in international humanitarian law. It mainly examines Additional Protocol I of the Geneva Convention of 1949 and existing international law concerning civilian objects and the principles of international humanitarian law in the classification between civilian objects and military objectives. The paper then explores the environment’s classification as both a military objective and as a civilian object as well as explores arguments in favor of the classification of the whole environment as a civilian object. The paper will then discuss the current legal regime surrounding the protection of the environment, discussing some declarations and conventions including the 1868 Declaration of St. Petersburg, the 1907 Hague Convention No. IV, the Geneva Conventions, and the 1976 Environmental Modification Convention. The paper concludes with the outline noting the movement from codification of the principles of restraint into the various treaties, agreements, and declarations of the current regime of international humanitarian law. This paper provides an analysis of the history and significance of the relationship between international humanitarian law as a major contributor to the growing field of international environmental law.

Keywords: armed conflict, environment, legal regime, restraint

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Authors: Md. Ashikur Rahman

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Optimal breastfeeding practice is essential to reducing childhood morbidity and mortality and helps to achieve Millennium Development Goal (MDG). A cross-sectional study was conducted in a rural area in Dhaka district to explore the barrier to optimal breastfeeding practices. The population of this study constitutes all nursing mothers having children aged 0-6 months, and they were selected purposively. The study adopted a structured and in-depth interview procedure consisting of open and closed-ended questions. Four hundred rural nursing mothers constituted the sample of the structured interview, while 15 were involved in the in-depth interview. Among the respondent's majority (67%) were in the age group 17-25 years, with a mean age of 24.44 years. Most (39.5%) of the mothers were housewives with a secondary level of education (46.5%). About 32% of mothers started breastfeeding within one hour after birth. But delayed initiation was reported in 31.5% of mothers, whereas 36.8% of mothers forgot the exact time of initiation of breastfeeding. The main reason not to practice colostrum was mothers tried to breastfeed, but there was no milk, stated 13.8% of mothers. In addition, about one-third (34.3%) of the respondents practiced pre-lacteal feeding, and among them, 12.8% introduced sugar with water. Reasons given by the mothers for bottle-feeding was that baby was not satisfied with breast milk only; 22.0% of mothers indicated this cause. The main influence to take formula milk by their mother and mothers-in-law was stated by 18.8% of mothers. Some mothers stated that major constraints to EBF were the perception of not having enough milk (25.5 %) and babies crying seems to be hungry (8.8%). One-third of the mothers (31.5%) felt uncomfortable during breastfeeding. Access to antenatal and postnatal counseling in the study area also was a key obstacle to optimal breastfeeding practices. In a qualitative survey, some mothers believed that there was no difference between breast milk and formula milk. Colostrum feeding, pre-lacteal feeding, early initiation of breastfeeding, and exclusive breastfeeding were strongly associated with family type, family member, birth order, religion, husbands' occupation, delivery attendants and delivery type, postnatal care, and health care facilities. To reduce the barriers to the successful practice of exclusive breastfeeding, there is a need for a grass-roots approach to educating and counseling nursing mothers with identifying factors influencing or discouraging the optimal practice.

Keywords: exclusive, breast feeding, practices, Bangladesh

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Authors: Galal Omer, Onisimo Mutanga, Elfatih M. Abdel-Rahman, Elhadi Adam

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Threatened tree species (TTS) play a significant role in ecosystem functioning and services, land use dynamics, and other socio-economic aspects. Such aspects include ecological, economic, livelihood, security-based, and well-being benefits. The development of techniques for mapping and monitoring TTS is thus critical for understanding the functioning of ecosystems. The advent of advanced imaging systems and supervised learning algorithms has provided an opportunity to classify TTS over fragmenting landscape. Recently, vegetation maps have been produced using advanced imaging systems such as WorldView-2 (WV-2) and robust classification algorithms such as support vectors machines (SVM) and artificial neural network (ANN). However, delineation of TTS in a fragmenting landscape using high resolution imagery has widely remained elusive due to the complexity of the species structure and their distribution. Therefore, the objective of the current study was to examine the utility of the advanced WV-2 data for mapping TTS in the fragmenting Dukuduku indigenous forest of South Africa using SVM and ANN classification algorithms. The results showed the robustness of the two machine learning algorithms with an overall accuracy (OA) of 77.00% (total disagreement = 23.00%) for SVM and 75.00% (total disagreement = 25.00%) for ANN using all eight bands of WV-2 (8B). This study concludes that SVM and ANN classification algorithms with WV-2 8B have the potential to classify TTS in the Dukuduku indigenous forest. This study offers relatively accurate information that is important for forest managers to make informed decisions regarding management and conservation protocols of TTS.

Keywords: artificial neural network, threatened tree species, indigenous forest, support vector machines

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Authors: Youhang Zhou, Weimin Zeng, Qi Xie

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Abstract: In order to solve the wear recognition problem of the machine tool guide surface, a new machine tool guide surface recognition method based on the radar-graph barycentre feature is presented in this paper. Firstly, the gray mean value, skewness, projection variance, flat degrees and kurtosis features of the guide surface image data are defined as primary characteristics. Secondly, data Visualization technology based on radar graph is used. The visual barycentre graphical feature is demonstrated based on the radar plot of multi-dimensional data. Thirdly, a classifier based on the support vector machine technology is used, the radar-graph barycentre feature and wear original feature are put into the classifier separately for classification and comparative analysis of classification and experiment results. The calculation and experimental results show that the method based on the radar-graph barycentre feature can detect the guide surface effectively.

Keywords: guide surface, wear defects, feature extraction, data visualization

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Authors: Ghada Shahrour, Nusiebeh Ananbh, Heyam Dalky, Mohammad Rababa, Fatmeh Alzoubi

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Positive teacher-student relationship has been found to affect students’ attitudes towards bullying and, in turn, their engagement in bullying behavior. However, no investigation has been conducted to explore whether teacher-student relationship affects teachers’ attitudes towards bullying. The aim of this study was to examine the role of teacher-student relationship on teachers’ attitudes towards bullying in terms of bullying seriousness, empathic responding, and likelihood to intervene in bullying situation. A cross-sectional, descriptive design was employed among a convenience sample of 173 school teachers (50.9% female) of 12 to 17-year-old students. The teachers were recruited from secondary public schools of three governorates in the Northern district of Jordan. Each group of students has multiple teachers for different subjects. Results showed that teacher-student relationship is partially related to teachers’ attitudes towards bullying. More specifically, having a close teacher-student relationship significantly increased teachers’ perception of bullying seriousness and empathy but not the likelihood to intervene. Research is needed to examine teachers’ obstacles for not providing bullying interventions, as the barriers may be culturally contextualized. Meanwhile, interventions that promote quality teacher-student relationship are necessary to increase teachers’ perception of bullying seriousness and empathy. Students have been found to adopt the values of their teachers, and this may deter them from engaging in bullying behavior.

Keywords: school bullying, teachers’ attitudes, teacher-student relationship, adolescent students

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Authors: Nowbuth Manta Devi, Rasmally Mohammed Hussein

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Flood is one of the most devastating climate impacts that many countries are facing. Many different causes have been associated with the intensity of floods being recorded over time. Unplanned development, low carrying capacity of drains, clogged drains, construction in flood plains or increasing intensity of rainfall events. While a combination of these causes can certainly aggravate the flood conditions, in many cases, increasing drainage capacity has not reduced flood risk to the level that was expected. The present study analyzed the extent to which land use is contributing to aggravating impacts of flooding in a city. Satellite images have been analyzed over a period of 20 years at intervals of 5 years. Both unsupervised and supervised classification methods have been used with the image processing module of ArcGIS. The unsupervised classification was first compared to the basemap available in ArcGIS to get a first overview of the results. These results also aided in guiding data collection on-site for the supervised classification. The island of Mauritius is small, and there are large variations in land use over small areas, both within the built areas and in agricultural zones involving food crops. Larger plots of agricultural land under sugar cane plantations are relatively more easily identified. However, the growth stage and health of plants vary and this had to be verified during ground truthing. The results show that although there have been changes in land use as expected over a span of 20 years, this was not significant enough to cause a major increase in flood risk levels. A digital elevation model was analyzed for further understanding. It could not be noted that overtime, development tampered with natural flow paths in addition to increasing the impermeable areas. This situation results in backwater flows, hence increasing flood risks.

Keywords: climate change, flood, natural flow paths, small islands

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Authors: Aisulu Tileukulova, Zhexebay Dauren

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Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.

Keywords: radar, neural network, convolutional neural network, echo signals

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Authors: Rajesh Ranjan, Marimuthu Palaniswami, A. A. Hashmi

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With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor (essential tremor). The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. In our research, we focus on developing a system for automatic classification of tremor which can accurately differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device, so that adequate diagnosis can be provided to the correct patient. In this research, a study was conducted in the neuro-clinic to assess the upper wrist movement of the patient suffering from Pure (Essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed in accordance with Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fast-Fourier transform based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as the input feature for various classification tools for distinguishing the PT and ET tremor types. An automatic system for efficient classification of tremor was developed using feature extraction methods, and superior performance was achieved using K-nearest neighbors and Support Vector Machine classifiers respectively.

Keywords: machine learning approach for neurological disorder assessment, automatic classification of tremor types, feature extraction method for tremor classification, neurological movement disorder, parkinsonian tremor, essential tremor

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Authors: Uswah Khairuddin, Rubiyah Yusof, Nenny Ruthfalydia Rosli

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This paper presents a comparative study between k-Nearest Neighbour (k-NN) and Multi-Layer Perceptron Neural Network (MLP-NN) classifier using Genetic Algorithm (GA) as feature selector for wood recognition system. The features have been extracted from the images using Grey Level Co-Occurrence Matrix (GLCM). The use of GA based feature selection is mainly to ensure that the database used for training the features for the wood species pattern classifier consists of only optimized features. The feature selection process is aimed at selecting only the most discriminating features of the wood species to reduce the confusion for the pattern classifier. This feature selection approach maintains the ‘good’ features that minimizes the inter-class distance and maximizes the intra-class distance. Wrapper GA is used with k-NN classifier as fitness evaluator (GA-kNN). The results shows that k-NN is the best choice of classifier because it uses a very simple distance calculation algorithm and classification tasks can be done in a short time with good classification accuracy.

Keywords: feature selection, genetic algorithm, optimization, wood recognition system

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Authors: Jung Yoon Kim

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Introduction: Wounds inevitably cause patients to experience discomfort, distress, and consequentially reduced quality of life due to entailed pain, maceration, and foul odor. The dressing has been a universal wound care method in which wounds are covered and protected, and an optimum environment for healing is provided. This study aimed to investigate Korean nurses’ level of awareness of pain and skin tearing in wound beds and/or peri-wound skin at dressing change. Methods: A descriptive study was performed. Convenience sampling was employed, and registered nurses were recruited from attendees of continuing education program. A total of 399 participants (RN) completed the questionnaire. Data were collected from September to November 2022. Results: Many of them perceived skin tearing and wound-related pain associated with dressing changes, but most of them did not assess and record pain and skin tearing at dressing change. More than half of the respondents reported that they did not provide nursing intervention to prevent pain and skin tearing. Many of them reported that a systematic educational program for preventing pain and skin tearing at dressing changes was needed. Discussion: Many of the respondents were aware of pain and skin tearing at dressing change but did not take any further necessary measures, including nursing intervention, for the most appropriate, systematic pain and skin tearing management. Therefore, this study suggested that a systematic and comprehensive educational program for Korean healthcare professionals needs to be developed and implemented in Korea’s hospital settings.

Keywords: skin tearing, pain, dressing change, nurses

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Authors: Fernanda Ludmilla Rossi Rocha, Thamiris Cavazzani Vegro, Silvia Helena Henriques Camelo, Carmen Silvia Gabriel, Andrea Bernardes

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Introduction: Organizations are cultural, symbolic and imaginary systems composed by values and norms. These values and norms represent the organizational culture, which determines the behavior of the workers, guides the work practices and impacts the quality of care and the safety culture of health services worldwide. Objective: To analyze the organizational culture of a public and a private hospital in Brazil. Method: Descriptive study with quantitative approach developed in a public and in a private hospital of Brazil. Sample was composed by 281 nursing workers, of which 73 nurses and 208 nursing auxiliaries and technicians. The data collection instrument comprised the Brazilian Instrument for Assessing Organizational Culture. Data were collected from March to December 2013. Results: At the public hospital, the results showed an average score of 2.85 for the values concerning cooperative professionalism (CP); 3.02 for values related to hierarchical rigidity and the centralization of power (HR); 2.23 for individualistic professionalism and competition at work (IP); 2.22 for values related to satisfaction, well-being and motivation of workers (SW); 3.47 for external integration (EI); 2.03 for rewarding and training practices (RT); 2.75 for practices related to the promotion of interpersonal relationships (IR) About the private hospital, the results showed an average score of 3.24 for the CP; 2.83 for HR; 2.69 for IP; 2.71 for SW; 3.73 for EI; 2.56 for RT; 2.83 for IR at the hospital. Discussion: The analysis of organizational values of the studied hospitals shows that workers find the existence of hierarchical rigidity and the centralization of power in the institutions; believed there was cooperation at workplace, though they perceived individualism and competition; believed that values associated with the workers’ well-being, satisfaction and motivation were seldom acknowledged by the hospital; believed in the adoption of strategic planning actions within the institution, but considered interpersonal relationship promotion, continuous education and the rewarding of workers to be little valued by the institution. Conclusion: This work context can lead to professional dissatisfaction, compromising the quality of care and contributing to the occurrence of occupational diseases.

Keywords: nursing management, organizational culture, quality of care, interpersonal relationships

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Authors: Yunwei Zhang, Na Li, Yuhong Niu

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Bone tissue engineering scaffold was regarded as an important clinical technology of curing bony defect. The patent protection of bone tissue engineering scaffold had been paid more attention and strengthened all over the world. This study analyzed the future development trends of international technologies in the field of bone tissue engineering scaffold and its patent protection. This study used the methods of data classification and classification indexing to analyze 2718 patents retrieved in the patent database. Results showed that the patents coming from United States had a competitive advantage over other countiries in the field of bone tissue engineering scaffold. The number of patent applications by a single company in U.S. was a quarter of that of the world. However, the capability of R&D in China was obviously weaker than global level, patents mainly coming from universities and scientific research institutions. Moreover, it would be predicted that synthetic organic materials as new materials would be gradually replaced by composite materials. The patent technology protections of composite materials would be more strengthened in the future.

Keywords: bone tissue engineering, patent analysis, Scaffold material, patent protection

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Authors: Elzbieta Sikorska-Simmons

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Advanced dementia is a progressive terminal brain disease that is accompanied by a syndrome of difficult to manage symptoms and complications that eventually lead to death. The management of advanced dementia poses major challenges to family caregivers who act as patient health care proxies in making medical treatment decisions. Little is known, however, about how they manage advanced dementia and how their treatment choices influence the quality of patient life. This prospective qualitative study examines the key medical treatment decisions that family caregivers make while managing advanced dementia. The term ‘family caregiver’ refers to a relative or a friend who is primarily responsible for managing patient’s medical care needs and legally authorized to give informed consent for medical treatments. Medical decision-making implies a process of choosing between treatment options in response to patient’s medical care needs (e.g., worsening comorbid conditions, pain, infections, acute medical events). Family caregivers engage in this process when they actively seek treatments or follow recommendations by healthcare professionals. Better understanding of medical decision-making from the family caregiver perspective is needed to design interventions that maximize the quality of patient life and limit inappropriate treatments. Data were collected in three waves of semi-structured interviews with 20 family caregivers for patients with advanced dementia. A purposive sample of 20 family caregivers was recruited from a senior care center in Central Florida. The qualitative personal interviews were conducted by the author in 4-5 months intervals. The ethical approval for the study was obtained prior to the data collection. Advanced dementia was operationalized as stage five or higher on the Global Deterioration Scale (GDS) (i.e., starting with the GDS score of five, patients are no longer able survive without assistance due to major cognitive and functional impairments). Information about patients’ GDS scores was obtained from the Center’s Medical Director, who had an in-depth knowledge of each patient’s health and medical treatment history. All interviews were audiotaped and transcribed verbatim. The qualitative data analysis was conducted to answer the following research questions: 1) what treatment decisions do family caregivers make while managing the symptoms of advanced dementia and 2) how do these treatment decisions influence the quality of patient life? To validate the results, the author asked each participating family caregiver if the summarized findings accurately captured his/her experiences. The identified medical decisions ranged from seeking specialist medical care to end-of-life care. The most common decisions were related to arranging medical appointments, medication management, seeking treatments for pain and other symptoms, nursing home placement, and accessing community-based healthcare services. The most challenging and consequential decisions were related to the management of acute complications, hospitalizations, and discontinuation of treatments. Decisions that had the greatest impact on the quality of patient life and survival were triggered by traumatic falls, worsening psychiatric symptoms, and aspiration pneumonia. The study findings have important implications for geriatric nurses in the context of patient/caregiver-centered dementia care. Innovative nursing approaches are needed to support family caregivers to effectively manage medical care needs of patients with advanced dementia.

Keywords: advanced dementia, family caregiver, medical decision-making, symptom management

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Authors: Huang Wei-Yi

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Objective: This article explores the intensive care nursing experience of a terminal lung cancer patient who received palliative care after tracheal intubation. The patient was nearing death, and the family experienced sadness and grief as they faced the patient’s deteriorating condition and impending death. Methods: The patient was diagnosed with lung cancer in 2018 and received chemotherapy and radiation therapy with regular outpatient follow-ups. Due to brain metastasis and recent poor pain control and treatment outcomes, the patient was admitted to the intensive care unit (ICU), where the tracheal tube was removed, and palliative care was initiated. During the care period, a holistic assessment was conducted, addressing the physical, psychological, social, and spiritual aspects of care. Medical records were reviewed, interviews and family meetings were held, and a comprehensive assessment was carried out by the critical care team in collaboration with the palliative care team. The primary nursing issues identified included pain, ineffective breathing patterns, fear of death, and altered tissue perfusion. Results: Throughout the care process, the palliative care nurse, along with the family, utilized listening, caring, companionship, pain management, essential oil massage, distraction, and comfortable positioning to alleviate the patient’s pain and breathing difficulties. The use of Morphine 6mg in 0.9% N/S 50ml IV drip q6h reduced the FLACC pain score from 6 to 3. The patient’s respiratory rate improved from 28 breaths/min to 18-22 breaths/min, and sleep duration increased from 4 to 7 uninterrupted hours. The holistic palliative care approach, coupled with the involvement of the palliative care team, facilitated expressions of gratitude, apologies, and love between the patient and family. Visiting hours were extended, and with the nurse’s assistance, these moments were recorded and shared with the patient’s consent, providing cherished memories for the family. The patient’s end-of-life experience was thus improved, and the family was able to find peace. This case also served to promote the concept of palliative care, ensuring that more patients and families receive high-quality nursing care. Conclusion: When caring for terminal patients, collaboration with the palliative care team, including social workers, clergy, psychologists, and nutritionists, is essential. Involving the family in decision-making and providing opportunities for closeness and expressions of gratitude improve personalized care and enhance the patient's quality of life. Upon transferring to the ward, the patient’s hemodynamic stability was maintained, including SBP 110-130 mmHg, respiratory rate 20-22 breaths/min, and pain score <3. The patient was later discharged and transitioned to home hospice care for ongoing support.

Keywords: intensive care, lung cancer, palliative care, ICU

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Authors: Gianluca Castelnuovo, Gian Mauro Manzoni, Giada Pietrabissa, Stefania Corti, Emanuele Giusti, Roberto Cattivelli, Enrico Molinari, Susan Simpson

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Obesity is currently an important public health problem of epidemic proportions (globesity). Moreover Binge Eating Disorder (BED) is typically connected with obesity, even if not occurring exclusively in conjunction with overweight conditions. Typically obesity with BED requires a longer term treatment in comparison with simple obesity. Rehabilitation interventions that aim at improving weight-loss, reducing obesity-related complications and changing dysfunctional behaviors, should ideally be carried out in a multidisciplinary context with a clinical team composed of psychologists, dieticians, psychiatrists, endocrinologists, nutritionists, physiotherapists, etc. Long-term outpatient multidisciplinary treatments are likely to constitute an essential aspect of rehabilitation, due to the growing costs of a limited inpatient approach. Internet-based technologies can improve long-term obesity rehabilitation within a collaborative approach. The new m health (m-health, mobile health) paradigm, defined as clinical practices supported by up to date mobile communication devices, could increase compliance- engagement and contribute to a significant cost reduction in BED and obesity rehabilitation. Five psychological components need to be considered for successful m Health-based obesity rehabilitation in order to facilitate weight-loss.1) Self-monitoring. Portable body monitors, pedometers and smartphones are mobile and, therefore, can be easily used, resulting in continuous self-monitoring. 2) Counselor feedback and communication. A functional approach is to provide online weight-loss interventions with brief weekly or monthly counselor or psychologist visits. 3) Social support. A group treatment format is typically preferred for behavioral weight-loss interventions. 4) Structured program. Technology-based weight-loss programs incorporate principles of behavior therapy and change with structured weekly protocolos including nutrition, exercise, stimulus control, self-regulation strategies, goal-setting. 5) Individually tailored program. Interventions specifically designed around individual’s goals typically record higher rates of adherence and weight loss. Opportunities and limitations of m health approach in clinical psychology for obesity and BED are discussed, taking into account future research directions in this promising area.

Keywords: obesity, rehabilitation, out-patient, new technologies, tele medicine, tele care, m health, clinical psychology, psychotherapy, chronic care management

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Authors: Elissar Mansour, Emily Chen, Tracee Fernandez, Mariam Basheti, Christopher Gordon, Bandana Saini

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Sleep is an essential process for the maintenance of several neurobiological processes such as memory consolidation, mood, and metabolic processes. It is known that sleep patterns vary with age and is affected by multiple factors. While non-pharmacological strategies are generally considered first-line, sedatives are excessively used in the older population. This study aimed to explore the management of sleep in residential aged care facilities (RACFs) by nurse professionals and to identify the key factors that impact provision of optimal sleep health care. An inductive thematic qualitative research method was employed to analyse the data collected from semi-structured interviews with registered nurses working in RACF. Seventeen interviews were conducted, and the data yielded three themes: 1) the nurses’ observations and knowledge of sleep health, 2) the strategies employed in RACF for the management of sleep disturbances, 3) the organizational barriers to evidence-based sleep health management. Nurse participants reported the use of both non-pharmacological and pharmacological interventions. Sedatives were commonly prescribed due to their fast action and accessibility despite the guidelines indicating their use in later stages. Although benzodiazepines are known for their many side effects, such as drowsiness and oversedation, temazepam was the most commonly administered drug. Sleep in RACF was affected by several factors such as aging and comorbidities (e.g., dementia, pain, anxiety). However, the were also many modifiable factors that negatively impacted sleep management in RACF. These include staffing ratios, nursing duties, medication side effects, and lack of training and involvement of allied health professionals. This study highlighted the importance of involving a multidisciplinary team and the urge to develop guidelines and training programs for healthcare professionals to improve sleep health management in RACF.

Keywords: registered nurses, residential aged care facilities, sedative use, sleep

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Authors: Apostolos Kalatzis, Ashish Teotia, Vishnunarayan Girishan Prabhu, Laura Stanley

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Emotions are functional behaviors influenced by thoughts, stimuli, and other factors that induce neurophysiological changes in the human body. Understanding and classifying emotions are challenging as individuals have varying perceptions of their environments. Therefore, it is crucial that there are publicly available databases and virtual reality (VR) based environments that have been scientifically validated for assessing emotional classification. This study utilized two commercially available VR applications (Guided Meditation VR™ and Richie’s Plank Experience™) to induce acute stress and calm state among participants. Subjective and objective measures were collected to create a validated multimodal dataset and classification scheme for affective state classification. Participants’ subjective measures included the use of the Self-Assessment Manikin, emotional cards and 9 point Visual Analogue Scale for perceived stress, collected using a Virtual Reality Assessment Tool developed by our team. Participants’ objective measures included Electrocardiogram and Respiration data that were collected from 25 participants (15 M, 10 F, Mean = 22.28  4.92). The features extracted from these data included heart rate variability components and respiration rate, both of which were used to train two machine learning models. Subjective responses validated the efficacy of the VR applications in eliciting the two desired affective states; for classifying the affective states, a logistic regression (LR) and a support vector machine (SVM) with a linear kernel algorithm were developed. The LR outperformed the SVM and achieved 93.8%, 96.2%, 93.8% leave one subject out cross-validation accuracy, precision and recall, respectively. The VR assessment tool and data collected in this study are publicly available for other researchers.

Keywords: affective computing, biosignals, machine learning, stress database

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