Search results for: nursing interventions classification

Authors: Abdesselem Dakhli, Wajdi Bellil, Chokri Ben Amar

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DNA Barcode, a short mitochondrial DNA fragment, made up of three subunits; a phosphate group, sugar and nucleic bases (A, T, C, and G). They provide good sources of information needed to classify living species. Such intuition has been confirmed by many experimental results. Species classification with DNA Barcode sequences has been studied by several researchers. The classification problem assigns unknown species to known ones by analyzing their Barcode. This task has to be supported with reliable methods and algorithms. To analyze species regions or entire genomes, it becomes necessary to use similarity sequence methods. A large set of sequences can be simultaneously compared using Multiple Sequence Alignment which is known to be NP-complete. To make this type of analysis feasible, heuristics, like progressive alignment, have been developed. Another tool for similarity search against a database of sequences is BLAST, which outputs shorter regions of high similarity between a query sequence and matched sequences in the database. However, all these methods are still computationally very expensive and require significant computational infrastructure. Our goal is to build predictive models that are highly accurate and interpretable. This method permits to avoid the complex problem of form and structure in different classes of organisms. On empirical data and their classification performances are compared with other methods. Our system consists of three phases. The first is called transformation, which is composed of three steps; Electron-Ion Interaction Pseudopotential (EIIP) for the codification of DNA Barcodes, Fourier Transform and Power Spectrum Signal Processing. The second is called approximation, which is empowered by the use of Multi Llibrary Wavelet Neural Networks (MLWNN).The third is called the classification of DNA Barcodes, which is realized by applying the algorithm of hierarchical classification.

Keywords: DNA barcode, electron-ion interaction pseudopotential, Multi Library Wavelet Neural Networks (MLWNN)

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Authors: Modi Alsubaie, Willem Kuyken, Rebecca Abbott, Barnaby Dunn, Chris Dickens, Tina Keil, William Henley

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Background: Recently, there has been an increased interest in studying the effects of mindfulness-based interventions for people with psychological and physical problems. However, the mechanisms of action in these interventions that lead to beneficial physical and psychological outcomes have yet to be clearly identified. Purpose: The aim of this paper is to review, systematically, the evidence to date on the mechanisms of action in mindfulness interventions in populations with physical and/or psychological conditions. Method: Searches of seven databases (PsycINFO, Medline (Ovid), Cochrane Central Register of Controlled Trials, EMBASE, CINAHL, AMED, ClinicalTrials.gov) were undertaken in June 2014 and July 2015. We evaluated to what extent the studies we identified met the criteria suggested by Kazdin for establishing mechanisms of action within a psychological treatment (2007, 2009). Results: We identified four trials examining mechanisms of mindfulness interventions in those with comorbid psychological and physical health problems and 14 in those with psychological conditions. These studies examined a diverse range of potential mechanisms, including mindfulness and rumination. Of these candidate mechanisms, the most consistent finding was that greater self-reported change in mindfulness mediated superior clinical outcomes. However, very few studies fully met the Kazdin criteria for examining treatment mechanisms. Conclusion: There was evidence that global changes in mindfulness are linked to better outcomes. This evidence pertained more to interventions targeting psychological rather than physical health conditions. While there is promising evidence that MBCT/MBSR intervention effects are mediated by hypothesised mechanisms, there is a lack of methodological rigour in the field of testing mechanisms of action for both MBCT and MBSR, which precludes definitive conclusions.

Keywords: MBCT, MBSR, mechanisms, physical conditions, psychological conditions, systematic review

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Authors: Ahmed Kadry, Gwyn Dodd

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There has been a growing call globally for police forces to embed evidence-based policing research methods into police interventions in order to better understand and evaluate their impact. This research study highlights the success and challenges that police forces may encounter when trying to embed evidence-based research methods within their organisation. 10 in-depth qualitative interviews were conducted with police officers and staff at Greater Manchester Police (GMP) who were tasked with integrating evidence-based research methods into their operational interventions. The findings of the study indicate that with adequate resources and individual expertise, evidence-based research methods can be applied to operational work, including the testing of initiatives with strict controls in order to fully evaluate the impact of an intervention. However, the findings also indicate that this may only be possible where an operational intervention is heavily resourced with police officers and staff who have a strong understanding of evidence-based policing research methods, attained for example through their own graduate studies. In addition, the findings reveal that ample planning time was needed to trial operational interventions that would require strict parameters for what would be tested and how it would be evaluated. In contrast, interviewees underscored that operational interventions with the need for a speedy implementation were less likely to have evidence-based research methods applied. The study contributes to the wider literature on evidence-based policing by providing considerations for police forces globally wishing to apply evidence-based research methods to more of their operational work in order to understand their impact. The study also provides considerations for academics who work closely with police forces in assisting them to embed evidence-based policing. This includes how academics can provide their expertise to police decision makers wanting to underpin their work through evidence-based research methods, such as providing guidance on how to evaluate the impact of their work with varying research methods that they may otherwise be unaware of.  

Keywords: evidence based policing, evidence-based practice, operational policing, organisational change

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Authors: Hsing Yuan Liu

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Background: Considerable theoretical and empirical work has identified a relationship between collaboration interactions and creativity in an organizational context. The mechanisms underlying this link, however, are not well understood in healthcare education. Objectives: The aims of this study were to explore the impact of collaboration interactions on team creativity and its underlying mechanism and to verify a moderated mediation model. Design, setting, and participants: This study utilized a cross-sectional, quantitative, descriptive design. The survey data were collected from 177 nursing students who enrolled in 18-week capstone courses of small interdisciplinary groups collaborating to design healthcare products in Taiwan during 2018 and 2019. Methods: Questionnaires assessed the nursing students' perceptions about their teams' swift trust (of cognition- and affect-based), conflicts (of task, process, and relationship), interaction behaviors (constructive controversy, helping behaviors, and spontaneous communication), and creativity. This study used descriptive statistics to compare demographics, swift trust scores, conflict scores, interaction behavior scores, and creativity scores for interdisciplinary teams. Data were analyzed using Pearson’s correlation coefficient and simple and hierarchical multiple regression models. Results: Pearson’s correlation analysis showed the cognition-based team swift trust was positively correlated with team creativity. The mediation model indicated constructive controversy fully mediated the effect of cognition-based team swift trust on student teams’ creativity. The moderated mediation model indicated that task conflict negatively moderates the mediating effect of the constructive controversy on the link between cognition-based team swift trust and team creativity. Conclusion: Our findings suggest nursing student teams’ interaction behaviors and task conflict are crucial mediating and moderated mediation variables on the relationship between collaboration interactions and team creativity, respectively. The empirical data confirms the validity of our proposed moderated mediation models of team creativity. Therefore, this study's validated moderated mediation model could provide guidance for nursing educators to improve collaboration interaction outcomes and creativity on nursing student teams.

Keywords: team swift trust, team conflict, team interaction behavior, moderated mediating effects, interdisciplinary education, nursing students

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Authors: A. Elsehemy, M. Abdeen , T. Nazmy

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Since the appearance of the Semantic web, many semantic search techniques and models were proposed to exploit the information in ontology to enhance the traditional keyword-based search. Many advances were made in languages such as English, German, French and Spanish. However, other languages such as Arabic are not fully supported yet. In this paper we present a framework for ontology based information retrieval for Arabic language. Our system consists of four main modules, namely query parser, indexer, search and a ranking module. Our approach includes building a semantic index by linking ontology concepts to documents, including an annotation weight for each link, to be used in ranking the results. We also augmented the framework with an automatic document categorizer, which enhances the overall document ranking. We have built three Arabic domain ontologies: Sports, Economic and Politics as example for the Arabic language. We built a knowledge base that consists of 79 classes and more than 1456 instances. The system is evaluated using the precision and recall metrics. We have done many retrieval operations on a sample of 40,316 documents with a size 320 MB of pure text. The results show that the semantic search enhanced with text classification gives better performance results than the system without classification.

Keywords: Arabic text classification, ontology based retrieval, Arabic semantic web, information retrieval, Arabic ontology

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Authors: Feride Eskin Bacaksiz, Arzu K. Harmanci Seren

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The problems about the working hours of nursing personnel, such as overworking, numbers of night or daytime shifts, is stated one of the most complained issues by nurses. Otherwise, besides of nursing shortage, absenteeism of nurses because of sickness, some other health problems, or other reasons enforces nurse managers to make working schedules. In this study, it was aimed to analyse and evaluate the working schedules of nurses working in public hospitals. Working schedule lists of nursing personnel for the months of October and November in two public hospitals were analysed and evaluated. Approvals were acquired from the head nurse managers. Descriptive and comparative analyses were used. Totally 36 lists were analysed from two hospitals. There were totally 416 nurses (manager nurse: 25, nurse: 391) and 6-28 (12.6, SD=5.34) nurses in each list. It was found that nurses were working 8 (42.5%), 12 (27.9%) and 16 (20%) hours, in inpatient clinics (74.8%). Totally 20.2% of the nurses were absent during analysing time due to the annual, paid or unpaid leaves. Nurses were generally working 20-112 hours (80.27, SD=11.92). Most of the nurses over worked 1.5-443 hours (47.436, SD=60.78) the year before that year. It was determined that 11.8% of the nurses (n: 49) were working only night shifts and 42.1% (n: 175) of the nurses were working only daytime shifts. It was found that there were inequities in the working hours of nurses.

Keywords: nurse, public hospitals, working conditions, working schedules

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Authors: Saurav Kumar Suman, P. Karthigayani

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In this paper the height of the tree is estimated and forest types is classified from the multi temporal RISAT-1 Horizontal-Horizontal (HH) and Horizontal-Vertical (HV) Polarised Satellite Aperture Radar (SAR) data. The novelty of the proposed project is combined use of the Back-scattering Coefficients (Sigma Naught) and the Coherence. It uses Water Cloud Model (WCM). The approaches use two main steps. (a) Extraction of the different forest parameter data from the Product.xml, BAND-META file and from Grid-xxx.txt file come with the HH & HV polarized data from the ISRO (Indian Space Research Centre). These file contains the required parameter during height estimation. (b) Calculation of the Vegetation and Ground Backscattering, Coherence and other Forest Parameters. (c) Classification of Forest Types using the ENVI 5.0 Tool and ROI (Region of Interest) calculation.

Keywords: RISAT-1, classification, forest, SAR data

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Authors: Merve Kadioğlu, Nevin H. Şahin

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Purpose: This research is organized to determine the influence of the web-based learning program. The program has been developed to gain information about normal delivery skill that is one of the topics of nursing students who take the woman health and illness. Material and Methods: The methodology of this study was applied as pre-test post-test single-group quasi-experimental. The pilot study consisted of 28 nursing student study groups who agreed to participate in the study. The findings were gathered via web-based technologies: student information form, information evaluation tests, Web Based Training Material Evaluation Scale and web-based learning environment feedback form. In the analysis of the data, the percentage, frequency and Wilcoxon Signed Ranks Test were used. The Web Based Instruction Program was developed in the light of full learning model, Mayer's research-based multimedia development principles and Gagne's Instructional Activities Model. Findings: The average scores of it was determined in accordance with the web-based educational material evaluation scale: ‘Instructional Suitability’ 4.45, ‘Suitability to Educational Program’ 4.48, ‘Visual Adequacy’ 4.53, ‘Programming Eligibility / Technical Adequacy’ 4.00. Also, the participants mentioned that the program is successful and useful. A significant difference was found between the pre-test and post-test results of the seven modules (p < 0.05). Results: According to pilot study data, the program was rated ‘very good’ by the study group. It was also found to be effective in increasing knowledge about normal labor.

Keywords: normal delivery, web-based learning, nursing students, e-learning

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Authors: Joshua Midha

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The basis for information campaigns and behavioral interventions has long reigned as a tactic. From the Soviet-era propaganda machines to the opinion hijacks in Iran, these measures are now commonplace and are used for dissemination and disassembly. However, the use of these tools for strategic diffusion, specifically in a counter-terrorism setting, has only been explored on the surface. This paper aims to introduce a larger conceptual portion of guided information campaigns into preexisting terror cells and situations. It provides an alternative, low-risk intervention platform for future military strategy. This paper highlights a theoretical framework to lay out the foundationary details and explanations for behavioral interventions and moves into using a case study to highlight the possibility of implementation. It details strategies, resources, circumstances, and risk factors for intervention. It also sets an expanding foundation for offensive PsyOps and argues for tactical diffusion of information to battle extremist sentiment. The two larger frameworks touch on the internal spread of information within terror cells and external political sway, thus charting a larger holistic purpose of strategic operations.

Keywords: terrorism, behavioral intervention, propaganda, SNA, extremism

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Authors: Ho-Hsin Lee, Yue-Min Jiang, Shu-Hui Tsai, Jian-Ren Chen, Mei-Yu XU, Wen-Tien Wu

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This project presents an AI-based method and system for wound status analysis. The system uses a three-in-one sensor device to analyze wound status, including color, temperature, and a 3D sensor to provide wound information up to 2mm below the surface, such as redness, heat, and blood circulation information. The system has a 90% accuracy rate, requiring only one manual correction in 70% of cases, with a one-second delay. The system also provides an offline application that allows for manual correction of the wound bed range using color-based guidance to estimate wound bed size with 96% accuracy and a maximum of one manual correction in 96% of cases, with a one-second delay. Additionally, AI-assisted wound bed range selection achieves 100% of cases without manual intervention, with an accuracy rate of 76%, while AI-based wound tissue type classification achieves an 85.3% accuracy rate for five categories. The AI system also includes similar case search and expert recommendation capabilities. For AI-assisted wound range selection, the system uses WIFI6 technology, increasing data transmission speeds by 22 times. The project aims to save up to 64% of the time required for human wound record keeping and reduce the estimated time to assess wound status by 96%, with an 80% accuracy rate. Overall, the proposed AI method and system integrate multiple sensors to provide accurate wound information and offer offline and online AI-assisted wound bed size estimation and wound tissue type classification. The system decreases delay time to one second, reduces the number of manual corrections required, saves time on wound record keeping, and increases data transmission speed, all of which have the potential to significantly improve wound care and management efficiency and accuracy.

Keywords: wound status analysis, AI-based system, multi-sensor integration, color-based guidance

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Authors: Sondos M. Hammad, Sherin M. ElGokhy, Mahmoud M. Fahmy, Elsayed A. Sallam

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In this paper, an ensemble-based Support Vector Machine (SVM) classification approach is proposed. It is used for miRNA prediction. Three problems, commonly associated with previous approaches, are alleviated. These problems arise due to impose assumptions on the secondary structural of premiRNA, imbalance between the numbers of the laboratory checked miRNAs and the pseudo-hairpins, and finally using a training data set that does not consider all the varieties of samples in different species. We aggregate the predicted outputs of three well-known SVM classifiers; namely, Triplet-SVM, Virgo and Mirident, weighted by their variant features without any structural assumptions. An additional SVM layer is used in aggregating the final output. The proposed approach is trained and then tested with balanced data sets. The results of the proposed approach outperform the three base classifiers. Improved values for the metrics of 88.88% f-score, 92.73% accuracy, 90.64% precision, 96.64% specificity, 87.2% sensitivity, and the area under the ROC curve is 0.91 are achieved.

Keywords: MiRNAs, SVM classification, ensemble algorithm, assumption problem, imbalance data

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Authors: Amal Shehata

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Low back pain related to disc prolapse is localized in the lumbar area and it may be radiated to the lower extremities, starting from neurons near or around the spinal canal. Most of the population may be affected with disc prolapse within their lifetime and leads to lost productivity, disability and loss of function. The study purpose was to examine the effect of rehabilitative nursing program on pain intensity and functional status among patients with discectomy. Design: Aquasi experimental design was utilized. Setting: The study was carried out at neurosurgery department and out patient's clinic of Menoufia University and Teaching hospitals at Menoufia governorate, Egypt. Instrument of the study: Five Instruments were used for data collection: Structured interviewing questionnaire, Functional assessment instrument, Observational check list, Numeric rating Scale and Oswestry low back pain disability questionnaire. Results: There was an improvement in mean total knowledge score about disease process, discectomy and rehabilitation program in study group (25.32%) than control group (7.32%). There was highly statistically significant improvement in lumbar flexibility among study group (80%) than control group (30%) after rehabilitation program than before. Also there was a decrease in pain score in study group (58% no pain) than control group (28% no pain) after rehabilitation program. There was an improvement in total disability score of study group (zero %) regarding effect of pain on the activity of daily living after rehabilitation program than control group (16%). Conclusion: Application of rehabilitative nursing program for patient with discectomy had proven a positive effect in relation to knowledge score, pain reduction, activity of daily living and functional abilities. Recommendation: A continuous rehabilitative nursing program should be carried out for all patients immediately after discectomy surgery on regular basis. Also A colored illustrated booklet about rehabilitation program should be available and distributed for all patients before surgery.

Keywords: discectomy, rehabilitative nursing program, pain intensity, functional status

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Authors: Ayse Inel Manav, Saliha Bozdogan Yesilot, Pinar Yesil Demirci, Gursel Oztunc

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Introduction: This study was conducted with a view to evaluating cognitive function level, depression, and quality of life of elderly people living in a nursing home. Methods: This study, which is cross-sectional and descriptive in nature, was conducted in the Nursing and Rehabilitation Center for the Elderly in Adana/Turkey between 1st of May and 1st of August, 2016. The participants included 118 elderly people who were chosen using simple random sampling method. The data were collected using the Personal Information Form, the Standardized Mini Mental State Exam (SMMSE), the Geriatric Depression Scale (GDS), and the World Health Organization Quality of Life-OLD (WHOQOL-OLD) module. The data were analyzed using IBM SPSS Statistics 22 (IBM, SPSS, Turkey) program. Results: Of all the participants, 36,4% (n=43) were female, 63,6% (n=75) were male, and average age was 74,08 ± 8,23 years. The participants’ SMMSE mean score was found 20,37 ± 7,08, GDS mean score was 14,92 ± 4,29, and WHOQOL-OLD module mean score was 69,76 ± 11,54. There was a negative, significant relationship between SMMSE and GDS scores, a positive relationship between WHOQOL-OLD module total scores and a negative, significant relationship between GDS scores and WHOQOL-OLD module total scores. Discussıon and Conclusion: Results showed that more than half of the elderly people living in the nursing home experienced cognitive deterioration and depression; and cognitive state, depression, and quality of life were found to be significantly related to each other.

Keywords: depression, cognitive function level, quality of life

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Authors: Cuneyt Yucelbas, Seral Ozsen, Sule Yucelbas, Gulay Tezel

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Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.

Keywords: artificial immune system, breast cancer diagnosis, Euclidean function, Gaussian function

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Authors: Shamsa Al Sharji, Athar Al Jabri, Haitham Al Dughaishi, Mirfat Al Barwani, Raja Al Rawahi, Raiya Al Rajhi, Shurooq Al Ruqaishi, Thamreen Al Zadjali, Iman Al Humaidi

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Background: External Ventricular Drains (EVDs) are critical in managing traumatic brain injuries and hydrocephalus by controlling intracranial pressure, but they carry a high risk of infection. Infection rates vary globally, ranging from 5% to 45%, leading to increased morbidity, prolonged hospital stays, and higher healthcare costs. Nursing protocols play a pivotal role in reducing these infection rates. This study investigates the impact of a structured nursing protocol on EVD-associated infections in adult neurosurgery patients at the Directorate General of Khoula Hospital, Oman, from January to September 2024. Methods: A cluster-randomized trial was conducted across neurosurgery wards and the ICU. The intervention group followed a comprehensive nursing protocol, including strict sterile insertion, standardized dressing changes, infection control training, and regular clinical audits. The control group received standard care. The primary outcome was the incidence of EVD-associated infections, with secondary outcomes including protocol compliance, infection severity, recovery times, length of stay, and 30-day mortality. Statistical analysis was conducted using Chi-square tests, paired t-tests, and logistic regression to assess the differences between groups. Results: The study involved 75 patients, with an overall infection rate of 13.3%. The intervention group showed a reduced infection rate of 8.9% compared to 20% in the control group. Compliance rates for key nursing actions were high, with 89.7% for hand hygiene and 86.2% for wound dressing. The relative risk of infection was 0.44 in the intervention group, reflecting a 55.6% reduction. Logistic regression identified obesity as a significant predictor of EVD infections. Although mortality rates were slightly higher in the intervention group, the number needed to treat (NNT) of 9 suggests that the nursing protocol may improve survival outcomes. Conclusion: This study demonstrates that structured nursing protocols can reduce EVD-related infections and improve patient outcomes in neurosurgery. While the findings are promising, further research with larger sample sizes is needed to confirm these results and optimize infection control strategies in neurosurgical care.

Keywords: EVD, CSF, nursing protocol, EVD infection

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Authors: Lukas Kober, Vladimir Littva, Vladimir Siska

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The pandemic has had a significant impact on our lives. One of the most affected professions is the nursing profession. Nurses are closest to the patient, spend the most time with him, support him, often replace the closest family members, and of course, are part of the whole treatment process. Current nurses have more competencies and roles than in the past. The healthcare system has reached a turning point, also in connection with the spreading Delta variant and the risk of the arrival of the third wave. The lack of nurses is a long-term problem, but it did not arise by itself. The reasons for the departure of nurses from the health care system are not only due to the increasing average age of nurses and midwives in Slovakia and their retirement. Thousands of nurses are leaving due to poor working conditions, low wages, and poor management of individual workplaces. We need to keep older nurses in the health care system, otherwise, we risk their early departure. The pandemic only exacerbates this situation, and the associated risks, such as occupational infections or enormous overload and exhaustion, only accelerate the exit from the profession. According to current data from the register of nurses and midwives, we canceled 772 registrations from January to September 2021, and 584 nurses requested the suspension of registration due to non-performance of the profession. During the same period, we registered only 240 new nurses graduate. We have had this significant disparity here for a long time. For the whole of 2020, we canceled 911 registrations and suspended 973 registrations. We registered a total of 389 graduates. Our system loses hundreds of graduates a year and loses experienced nurses with decades of experience who leave due to poor working conditions, wages and suffer from burnout. Such compensation should also be awarded to the families of health professionals who have lost their lives due to work and to COVID-19. These options can also be motivating for promising people interested in studying nursing, who can gradually replace the missing workforce. This purchase is supported by the KEGA project no. 015KU-4/2019.

Keywords: pandemic, COVID-19, nursing, nursing workforce, lack of nurses

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Authors: Rosa L. Figueroa, Christopher A. Flores, Qing Zeng-Treitler

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A regular expression consists of sequence characters which allow describing a text path. Usually, in clinical research, regular expressions are manually created by programmers together with domain experts. Lately, there have been several efforts to investigate how to generate them automatically. This article presents a text classification algorithm based on regexes. The algorithm named REX was designed, and then, implemented as a simplified method to create regexes to classify Spanish text automatically. In order to classify ambiguous cases, such as, when multiple labels are assigned to a testing example, REX includes an information gain method Two sets of data were used to evaluate the algorithm’s effectiveness in clinical text classification tasks. The results indicate that the regular expression based classifier proposed in this work performs statically better regarding accuracy and F-measure than Support Vector Machine and Naïve Bayes for both datasets.

Keywords: information gain, regular expressions, smith-waterman algorithm, text classification

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Authors: Maria Luisa T. Uayan

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The global need for qualified nurses to take care of the clients with various health needs is an incessant occurrence that persistently cause migration of nurses from developing to developed countries. The pull-push theory of migration greatly affects health care delivery systems of sending countries which is the same way affects nursing management. The exodus of nurses prepared to provide the much needed leadership at the bedside leaves the country in clusters giving health care institutions limited time to develop the next front-line managers that will assure quality patient care. This paper focuses on the extent and consequences of the massive recurring migration phenomena that is felt ONLY IN THE PHILIPPINE health care arena. It deals with the causes, problems, and effects of the cyclical loss of competent Filipina nurses in terms of emigration. Also, it will highlights the difficulties confronted by nursing service departments and health care teams when more experienced nurses set out for the “greener pastures” and patients are placed under the care of novice nurses. Fundamentally, it will emphasize the impact of suffering the loss of competent nurse managers in the Philippine health care institutions and provide contemporary recommendations on how to responsd accordingly to this very timely issue.

Keywords: Migration, Nurse Manager, Philippines

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Authors: Eda Yakit Ak, Ergül Aslan

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The study was conducted using the quasi-experimental design to determine the influence of the nursing support program prepared according to the Health Belief Model on reproductive health behaviors of orthopedically disabled women in the physical therapy and rehabilitation clinic at a university hospital between August 2019-October, 2020. The research sample included 50 women (35 in the control group and 15 in the experimental group with orthopedic disability). A 3-week nursing support program was applied to the experimental group of women. To collect the data, Introductory Information Form and Scale for Determining the Protective Attitudes of Married Women towards Reproductive Health (SDPAMW) were applied. The evaluation was made with a follow-up form for four months. In the first evaluation, the total SDPAMW scores were 119.93±20.59 for the experimental group and 122.20±16.71 for the control group. In the final evaluation, the total SDPAMW scores were 144.27±11.95 for the experimental group and 118.00±16.43 for the control group. The difference between the groups regarding the first and final evaluations for the total SDPAMW scores was statistically significant (p<0.01). In the experimental group, between the first and final evaluations regarding the sub-dimensions of SDPAMW, an increase was found in the behavior of seeing the doctor on reproductive health issues, protection from reproductive organ and breast cancer, general health behaviors to protect reproductive health, and protection from genital tract infections (p<0.05). Consequently, the nursing support program based on the Health Belief Model applied to orthopedically disabled women positively affected reproductive health behaviors.

Keywords: orthopedically disabled, woman, reproductive health, nursing support program, health belief model

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Authors: Abdullah Bal

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This paper presents a one-class classification (OCC) technique based on Fukunaga-Koontz Transform (FKT) for binary classification problems. The FKT is originally a powerful tool to feature selection and ordering for two-class problems. To utilize the standard FKT for data domain description problem (i.e., one-class classification), in this paper, a set of non-class samples which exist outside of positive class (target class) describing boundary formed with limited training data has been constructed synthetically. The tunnel-like decision boundary around upper and lower border of target class samples has been designed using statistical properties of feature vectors belonging to the training data. To capture higher order of statistics of data and increase discrimination ability, the proposed method, termed one-class FKT (OC-FKT), has been extended to its nonlinear version via kernel machines and referred as OC-KFKT for short. Multiple kernel learning (MKL) is a favorable family of machine learning such that tries to find an optimal combination of a set of sub-kernels to achieve a better result. However, the discriminative ability of some of the base kernels may be low and the OC-KFKT designed by this type of kernels leads to unsatisfactory classification performance. To address this problem, the quality of sub-kernels should be evaluated, and the weak kernels must be discarded before the final decision making process. MKL/OC-FKT and selective MKL/OC-FKT frameworks have been designed stimulated by ensemble learning (EL) to weight and then select the sub-classifiers using the discriminability and diversities measured by eigenvalue ratios. The eigenvalue ratios have been assessed based on their regions on the FKT subspaces. The comparative experiments, performed on various low and high dimensional data, against state-of-the-art algorithms confirm the effectiveness of our techniques, especially in case of small sample size (SSS) conditions.

Keywords: ensemble methods, fukunaga-koontz transform, kernel-based methods, multiple kernel learning, one-class classification

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Authors: Steffy Maria Joseph, Abdu Rahiman V, Abdul Hameed K. M.

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Online handwritten character recognition is a challenging field in Artificial Intelligence. The classification success rate of current techniques decreases when the dataset involves similarity and complexity in stroke styles, number of strokes and stroke characteristics variations. Malayalam is a complex south indian language spoken by about 35 million people especially in Kerala and Lakshadweep islands. In this paper, we consider the significant feature extraction for the similar stroke styles of Malayalam. This extracted feature set are suitable for the recognition of other handwritten south indian languages like Tamil, Telugu and Kannada. A classification scheme based on support vector machines (SVM) is proposed to improve the accuracy in classification and recognition of online malayalam handwritten characters. SVM Classifiers are the best for real world applications. The contribution of various features towards the accuracy in recognition is analysed. Performance for different kernels of SVM are also studied. A graphical user interface has developed for reading and displaying the character. Different writing styles are taken for each of the 44 alphabets. Various features are extracted and used for classification after the preprocessing of input data samples. Highest recognition accuracy of 97% is obtained experimentally at the best feature combination with polynomial kernel in SVM.

Keywords: SVM, matlab, malayalam, South Indian scripts, onlinehandwritten character recognition

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Authors: Jeremy Bekker, Guy Salazar

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Introduction: Nature-exposure interventions on university campuses may serve as an effective addition to overburdened counseling and student support centers. Nature-exposure interventions can work as a preventative well-being enhancement measure on campuses, which can be used adjacently with existing health resources. Specifically, this paper analyzes how spending time in nature impacts psychological well-being, cognitive functioning, and physical health. The poster covers the core findings and recommendations of this paper, which has been previously published in the BYU undergraduate psychology journal Intuition. Research Goals and Method: The goal of this paper was to outline the potential benefits of nature exposure for students’ physical health, mental well-being, and academic success. Another objective of this paper was to outline potential research-based interventions that use campus green spaces to improve student outcomes. Given that the core objective of this paper was to identify and establish research-based nature exposure interventions that could be used on college campuses, a broad literature review focused on these areas. Specifically, the databases Scopus and PsycINFO were used to screen for research focused on psychological well-being, physical health, cognitive functioning, and nature exposure interventions. Outcomes: Nature exposure has been shown to help increase positive affect, life satisfaction, happiness, coping ability and subjective well-being. Further, nature exposure has been shown to decrease negative affect, lower mental distress, reduce cognitive load, and decrease negative psychological symptoms. Finally, nature exposure has been shown to lead to better physical health. Findings and Recommendations: Potential interventions include adding green space to university buildings and grounds, dedicating already natural environments as nature restoration areas, and providing means for outdoor excursions. Potential limitations and suggested areas for future research are also addressed. Many campuses already contain green spaces, defined as any part of an environment that is predominately made of natural elements, and these green spaces comprise an untapped resource that is relatively cheap and simple.

Keywords: nature exposure, preventative care, undergraduate mental health, well-being intervention

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Authors: Shelby Simpson, William Stanley, Namir Naba, Xiaodi Wang

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Both t-SNE and UMAP are brand new state of art tools to predominantly preserve the local structure that is to group neighboring data points together, which indeed provides a very informative visualization of heterogeneity in our data. In this research, we develop a t-SNE and UMAP base neural network image classification algorithm to embed the original dataset to a corresponding low dimensional dataset as a preprocessing step, then use this embedded database as input to our specially designed neural network classifier for image classification. We use the fashion MNIST data set, which is a labeled data set of images of clothing objects in our experiments. t-SNE and UMAP are used for dimensionality reduction of the data set and thus produce low dimensional embeddings. Furthermore, we use the embeddings from t-SNE and UMAP to feed into two neural networks. The accuracy of the models from the two neural networks is then compared to a dense neural network that does not use embedding as an input to show which model can classify the images of clothing objects more accurately.

Keywords: t-SNE, UMAP, fashion MNIST, neural networks

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Authors: Richard Vuijk, Arnoud Arntz

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Rationale: To our knowledge treatment of personality disorder comorbidity in adults with autism spectrum disorder (ASD) is understudied and is still in its infancy: We do not know if treatment of personality disorders may be applicable to adults with ASD. In particular, it is unknown whether patients with ASD benefit from experiential techniques that are part of schema therapy developed for the treatment of personality disorders. Objective: The aim of the study is to investigate the efficacy of a schema mode focused treatment with adult clients with ASD and comorbid personality pathology (i.e. at least one personality disorder). Specifically, we investigate if they can benefit from both cognitive-behavioral, and experiential interventions. Study design: A multiple baseline case series study. Study population: Adult individuals (age > 21 years) with ASD and at least one personality disorder. Participants will be recruited from Sarr expertise center for autism in Rotterdam. The study requires 12 participants. Intervention: The treatment protocol consists of 35 weekly offered sessions, followed by 10 monthly booster sessions. A multiple baseline design will be used with baseline varying from 5 to 10 weeks, with weekly supportive sessions. After baseline, a 5-week exploration phase follows with weekly sessions during which current and past functioning, psychological symptoms, schema modes are explored, and information about the treatment will be given. Then 15 weekly sessions with cognitive-behavioral interventions and 15 weekly sessions with experiential interventions will be given. Finally, there will be a 10-month follow-up phase with monthly booster sessions. Participants are randomly assigned to baseline length, and respond weekly during treatment and monthly at follow-up on Belief Strength of negative core beliefs (by VAS), and fill out SMI, SCL-90 and SRS-A 7 times during screening procedure (i.e. before baseline), after baseline, after exploration, after cognitive and behavioral interventions, after experiential interventions, and after 5- and 10- month follow-up. The SCID-II will be administered during screening procedure (i.e. before baseline), at 5- and at 10-month follow-up. Main study parameters: The primary study parameter is negative core beliefs. Secondary study parameters include schema modes, personality disorder manifestations, psychological symptoms, and social interaction and communication. Discussion: To the best of author’s knowledge so far no study has been published on the application of schema mode focused interventions in adult patients with ASD and comorbid PD(s). This study offers the first systematic test of application of schema therapy for adults with ASD. The results of this study will provide initial evidence for the effectiveness of schema therapy in treating adults with both ASD and PD(s). The study intends to provide valuable information for future development and implementation of therapeutic interventions for adults with both ASD and PD(s).

Keywords: adults, autism spectrum disorder, personality disorder, schema therapy

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Authors: Zdravko Trivic, John Chye Fung, Ruzica Bozovic-Stamenovic

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Within the context of rapidly ageing population in Singapore and the pressing demands on both caregivers and care providers, an integrated approach to ageing-friendly and ability-sensitive enabling environment becomes an imperative. This particularly applies to nursing home environments and their immediate surroundings, as they are becoming one of the main available options of long-term care for many senior adults who are unable to age at home. Yet, despite the considerable efforts to break the still predominant clinical approach to eldercare and to introduce more home-like design and person-centric care model, nursing homes keep being stigmatised and perceived as not so desirable environments to grow old in. The challenges are further emphasised by the associated physical, sensorial, psychological and cognitive declines that are the common consequences of ageing. Such declines have an immense impact on almost all aspects of older adults’ daily functioning, including problems with mobility and spatial orientation, difficulties in communication, withdrawal from social interaction, higher level of depression and decreased sense of independence and autonomy. However, typical nursing home designs tend to neglect the full capacities of balanced and carefully integrated multisensory stimuli as active component of care and ability building. This paper outlines part of a larger multi-disciplinary study of six nursing homes in Singapore, with overarching objectives to create new models of supportive nursing home environments that go beyond the clinical care model and encourage community integration with the nursing home settings. The paper focuses on the largely neglected aspects of sensorial comfort and multi-sensorial properties of nursing homes, including both indoor and immediate outdoor spaces (boundaries). The objective was to investigate the sensory rhythms and explore their role in nursing home users’ daily routine and therapeutic capacities. Socio-sensory rhythms were captured and analysed through a combination of on-site sensory recordings of “objective” quantitative sensory data (air temperature and humidity, sound level and luminance) using multi-function environment meter, perceived experienced data, spatial mapping, first-person observations of nursing home users’ activity patterns, and interviews. This was done in addition to employment of available assessment tools, such as Wisconsin Person Directed Care assessment tool, Dementia Quality of Life [DQoL] instrument, and Resident Environment Impact Scale [REIS], as these tools address the issues of sensorial experience insufficiently and selectively. Key findings indicate varied levels of sensory comfort, as well as diversity, intensity, and customisation of multi-sensory conditions within different nursing home spaces. Sensory stimulation is typically concentrated in communal living areas of the nursing homes or in the areas that often provide controlled or limited access, including specifically designed sensory rooms and outdoor green spaces (gardens and terraces). Opportunities for sensory stimulation are particularly limited for bed-bound senior residents and within more functional areas, such as corridors. This suggests that the capacities of nursing home designs to provide more diverse and better integrated pleasant sensory conditions as integrated “therapeutic devices” to build nursing home residents’ physical and mental abilities, encourage activity and improve wellbeing are far from exhausted.

Keywords: ageing-supportive environment, enabling design, multi-sensory assessment, nursing home environment

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Authors: Evmorfia Koukia, Polyxeni Mangoulia

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Workshops tend to be a vivid and productive way as an in vivo teaching method. Due to the pandemic, COVID-19 university courses were conducted through the internet. Method It was tried for the first time to integrate online art therapy workshops in a core course named “Special Themes of Mental Health Nursing” in a MSc Program in Mental Health. The duration of the course is 3-hours per week for 11 weeks in a single semester. The course has a main instructor, a professor of psychiatric nursing experienced in arts therapies workshops and visiting art therapists. All art therapists were given a certain topic to cover. Students were encouraged to keep a logbook that was evaluated at the end of the semester and was submitted as a part of the examination process of the course. An interview of 10 minutes was conducted with each student at the end of the course from an independent investigator (an assistant professor) Participants The students (sample) of the program were: nurses, psychologists, and social workers Results: All students who participated in the courses found that the learning process was vivid, encouraging participation and self-motivation, and there were no main differences from in vivo learning. The students identified their personal needs, and they felt a personal connection with the learning experience. The result of the personalized learning was that students discovered their strengths and weaknesses and developed skills like critical thinking. All students admitted that the workshops were the optimal way for them to comprehend the courses’ content, their capability to become therapists, as well as their obstacles and weaknesses while working with patients in mental health. Conclusion: There were no important differences between the views of students in online and in vivo teaching method of the workshops. The result has shown that workshops in mental health can contribute equally in the learning experience.

Keywords: mental health, workshops, students, nursing

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Authors: Ali J. Ghandour, Houssam A. Krayem, Abedelkarim A. Jezzini

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High-resolution satellite images and remote sensing can provide global information in a fast way compared to traditional methods of data collection. Under such high resolution, a road is not a thin line anymore. Objects such as cars and trees are easily identifiable. Automatic vehicles enumeration can be considered one of the most important applications in traffic management. In this paper, autonomous vehicle detection and classification approach in highway environment is proposed. This approach consists mainly of three stages: (i) first, a set of preprocessing operations are applied including soil, vegetation, water suppression. (ii) Then, road networks detection and delineation is implemented using built-up area index, followed by several morphological operations. This step plays an important role in increasing the overall detection accuracy since vehicles candidates are objects contained within the road networks only. (iii) Multi-level Otsu segmentation is implemented in the last stage, resulting in vehicle detection and classification, where detected vehicles are classified into cars and trucks. Accuracy assessment analysis is conducted over different study areas to show the great efficiency of the proposed method, especially in highway environment.

Keywords: remote sensing, object identification, vehicle and road extraction, vehicle and road features-based classification

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Authors: Majid Habib Khan, Jinwei Zhao, Xinhong Hei, Liu Jiedong, Rana Shahzad Noor, Muhammad Imran

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Deep learning is increasingly employed in image classification, yet the scarcity and high cost of labeled data for training remain a challenge. Limited samples often lead to overfitting due to biased sample distribution. This paper introduces a dynamic distribution calibration method for few-shot learning. Initially, base and new class samples undergo normalization to mitigate disparate feature magnitudes. A pre-trained model then extracts feature vectors from both classes. The method dynamically selects distribution characteristics from base classes (both adjacent and remote) in the embedding space, using a threshold value approach for new class samples. Given the propensity of similar classes to share feature distributions like mean and variance, this research assumes a Gaussian distribution for feature vectors. Subsequently, distributional features of new class samples are calibrated using a corrected hyperparameter, derived from the distribution features of both adjacent and distant base classes. This calibration augments the new class sample set. The technique demonstrates significant improvements, with up to 4% accuracy gains in few-shot classification challenges, as evidenced by tests on miniImagenet and CUB datasets.

Keywords: deep learning, computer vision, image classification, few-shot learning, threshold

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Authors: Emily Adams, Eddie Donaghy, David Henderson, Lauren Ng, Caroline Sanders, Rowena Stewart, Maria Wolters, Stewart Mercer

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Ongoing review abstract: Older People Experiencing Homelessness (OPEH) can have mental and physical indicators of aging 10–20 years earlier than the general population and experience premature mortality due to age-related chronic conditions. Emerging literature suggests digital interventions could positively impact PEH’s well-being. However, the increased reliance on digital delivery may also perpetuate digital inequalities for socially excluded groups, including PEH. The potential triple disadvantage of being older, homeless, and digitally excluded creates a uniquely problematic situation that warrants further research. This scoping review aims to investigate and synthesise the range and type of digital interventions available to OPEH and the organisations that support OPEH. The following databases were searched on 28th July 2023: Medline, Scopus, International Bibliography of the Social Sciences (IBSS)‎, Applied Social Sciences Index & Abstracts (ASSIA)‎, Association for Computing Machinery Digital Library (ACMDL) and Policy commons. A search strategy was developed in collaboration with an academic librarian. The presentation will include: An introduction to OPEH and digital exclusion Overview of the results of this review: OPEH usage of digital platforms Current digital interventions available The role of support organisations Current gaps in the evidence, future research and recommendations for policy and practice

Keywords: homeless, digital exclusion, aging, technology

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Authors: Mekamı Hayet, Bounoua Nacer, Benabderrahmane Sidahmed, Taleb Ahmed

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Pose estimation is an important task in computer vision. Though the majority of the existing solutions provide good accuracy results, they are often overly complex and computationally expensive. In this perspective, we propose the use of dimensionality reduction techniques to address the problem of facial pose estimation. Firstly, a face image is converted into one-dimensional time series using Hilbert space filling curve, then the approach converts these time series data to a symbolic representation. Furthermore, a distance matrix is calculated between symbolic series of an input learning dataset of images, to generate classifiers of frontal vs. profile face pose. The proposed method is evaluated with three public datasets. Experimental results have shown that our approach is able to achieve a correct classification rate exceeding 97% with K-NN algorithm.

Keywords: machine learning, pattern recognition, facial pose classification, time series

Procedia PDF Downloads 350