Search results for: nursing interventions classification

Authors: Burçin Saltık, Levent Genç

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In this study, it was aimed to determine a route for identification of rice cultivation areas within Thrace and Marmara regions of Turkey using remote sensing and GIS. Landsat 8 (OLI-TIRS) imageries acquired in production season of 2013 with 181/32 Path/Row number were used. Four different seasonal images were generated utilizing original bands and different transformation techniques. All images were classified individually using supervised classification techniques and Land Use Land Cover Maps (LULC) were generated with 8 classes. Areas (ha, %) of each classes were calculated. In addition, district-based rice distribution maps were developed and results of these maps were compared with Turkish Statistical Institute (TurkSTAT; TSI)’s actual rice cultivation area records. Accuracy assessments were conducted, and most accurate map was selected depending on accuracy assessment and coherency with TSI results. Additionally, rice areas on over 4° slope values were considered as mis-classified pixels and they eliminated using slope map and GIS tools. Finally, randomized rice zones were selected to obtain maximum-minimum value ranges of each date (May, June, July, August, September images separately) NDVI, LSWI, and LST images to test whether they may be used for rice area determination via raster calculator tool of ArcGIS. The most accurate classification for rice determination was obtained from seasonal LSWI LULC map, and considering TSI data and accuracy assessment results and mis-classified pixels were eliminated from this map. According to results, 83151.5 ha of rice areas exist within study area. However, this result is higher than TSI records with an area of 12702.3 ha. Use of maximum-minimum range of rice area NDVI, LSWI, and LST was tested in Meric district. It was seen that using the value ranges obtained from July imagery, gave the closest results to TSI records, and the difference was only 206.4 ha. This difference is normal due to relatively low resolution of images. Thus, employment of images with higher spectral, spatial, temporal and radiometric resolutions may provide more reliable results.

Keywords: landsat 8 (OLI-TIRS), LST, LSWI, LULC, NDVI, rice

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Authors: Grace Adebo

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Rural Farmers-herdsmen conflicts have resulted in a large number of causalities in many parts of Nigeria. Herds of cattle have died, while farmers recorded inestimable losses of their crops and harvests. The overall consequences have impacted negatively on food security across the country. There are divided opinions by scholars, agricultural experts and conflict analysts on the root causes of the conflicts and why traditional institutional interventions are ineffective in resolving the crisis. The study, therefore, aims to investigate the fundamentality of the conflicts’ causes in Southwest Nigeria and the correlates between traditional institutional authorities’ intervention and farmers-herdsmen conflicts in Southwest Nigeria. A structured interview schedule and focus group discussion were employed to elicit information from 180 farmers and 48 herdsmen selected through a multistage sampling procedure from the conflict zones in Southwest Nigeria. Collected data were analyzed using frequency counts, percentages, means and the Relative Importance Index (RII). The study found that climate change effects, farmland encroachment, crop damage, theft, and competition for land and water resources and pollution were the root causes of the violent herders-rural farmer’s clashes. The quest for wealth acquisition by some traditional rulers and some notable individuals in the conflict neighborhoods, occasioned tribal-mix herds possession and, thus undermining local institutional interventions and perverting justice through weak conflict resolution strategies, therefore, fueling further conflicts. Most farmers in the conflict zones have abandoned their farms for fear of death. This coupled with physical, social, economic and psychological consequences have deepened food insecurity and impaired the economic conditions of the herdsmen and the farmers. Currently, there are no mutually established mediation mechanisms as most states are opposed to the enactment of grazing laws to protect territorial encroachments of lands and subsequent multiplication of the herdsmen. It is suggested that government and Non-Governmental Organisation (NGOs) should encourage a functional stakeholder's forum for sustainable conflict resolution and establish a compensation scheme for losses incurred while extension agents are equipped with knowledge on conflict management strategies for peace attainment with the envisioned goal of achieving sustainable livelihoods and food security in Southwest Nigeria.

Keywords: conflict resolution, food security, herdsmen-farmers conflict, sustainable livelihoods, traditional institutions

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Bissonnette Jean-François, Dossa Fabrice

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The project focuses on analyzing the spatial and temporal evolution of mangrove forest ecosystems near the city of Douala, Cameroon, in response to increasing human and environmental pressures. The selected study area, located in the Wouri River estuary, has a unique combination of economic importance, and ecological prominence. The study included valuable insights by conducting semi-structured interviews with resource operators and local officials. The thorough analysis of socio-economic data, farmer surveys, and satellite-derived information was carried out utilizing quantitative approaches in Excel and SPSS. Simultaneously, qualitative data was subjected to rigorous classification and correlation with other sources. The use of ArcGIS and CorelDraw facilitated the visual representation of the gradual changes seen in various land cover classifications. The research reveals complex processes that characterize mangrove ecosystems on Manoka and Cape Cameroon Islands. The lack of regulations in urbanization and the continuous growth of infrastructure have led to a significant increase in land conversion, causing negative impacts on natural landscapes and forests. The repeated instances of flooding and coastal erosion have further shaped landscape alterations, fostering the proliferation of water and mudflat areas. The unregulated use of mangrove resources is a significant factor in the degradation of these ecosystems. Activities including the use of wood for smoking and fishing, together with the coastal pollution resulting from the absence of waste collection, have had a significant influence. In addition, forest operators contribute to the degradation of vegetation, hence exacerbating the harmful impact of invasive species on the ecosystem. Strategic interventions are necessary to guarantee the sustainable management of these ecosystems. The proposals include advocating for sustainable wood exploitation techniques, using appropriate techniques, along with regeneration, and enforcing rules to prevent wood overexploitation. By implementing these measures, the ecological balance can be preserved, safeguarding the long-term viability of these precious ecosystems. On a conceptual level, this paper uses the framework developed by Elinor Ostrom and her colleagues to investigate the consequences of open access resources, where local actors have not been able to enforce measures to prevent overexploitation of mangrove wood resources. Governmental authorities have demonstrated limited capacity to enforce sustainable management of wood resources and have not been able to establish effective relationships with local fishing communities and with communities involved in the purchase of wood. As a result, wood resources in the mangrove areas remain largely accessible, while authorities do not monitor wood volumes extracted nor methods of exploitation. There have only been limited and punctual attempts at forest restoration with no significant consequence on mangrove forests dynamics.

Keywords: Mangroves, forest management, governance, open access resources, Cameroon

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Authors: Anna Bak-Klimek, Emily Spencer, Siew Lee, Karen Campbell, Wendy McInally

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Background & Significance: Over half of Teenagers and Young Adults (TYAs) say that they experience psychological distress after cancer diagnosis and TYAs with cancer are at higher risk of developing distress compared to other age groups. Despite this there are no age-appropriate interventions to help TYAs manage distress and there is a lack of conceptual understanding of what causes distress in this population group. This makes it difficult to design a targeted, developmentally appropriate intervention. This review aims to identify the key determinants of distress in TYAs affected by cancer and to propose an integrative model of cancer-related distress for TYAs. Method: A literature search was performed in Cochrane Database of Systematic Reviews, MEDLINE, PsycINFO, CINAHL, EMBASE and PsycArticles in May-June, 2022. Quantitative literature was systematically reviewed on the relationship between psychological distress experienced by TYAs affected by cancer and a wide range of factors i.e. individual (demographic, psychological, developmental, and clinical factors) and contextual (social/environmental) factors. Evidence was synthesized and correlates were categorized using the Biopsychosocial Model. The full protocol is available from PROSPERO (CRD42022322069) Results: Thirty eligible quantitative studies met criteria for the review. A total of twenty-six studies were cross-sectional, three were longitudinal and one study was a case control study. The evidence on the relationship between the socio-demographic, illness and treatment-related factors and psychological distress is inconsistent and unclear. There is however consistent evidence on the link between psychological factors and psychological distress. For instance, the use of cognitive and defence coping, negative meta-cognitive beliefs, less optimism, a lack of sense of meaning and lower resilience levels were significantly associated with higher psychological distress. Furthermore, developmental factors such as poor self-image, identity issues and perceived conflict were strongly associated with higher distress levels. Conclusions: The current review suggests that psychological and developmental factors such as ineffective coping strategies, poor self-image and identity issues may play a key role in the development of psychological distress in TYAs affected by cancer. The review proposes a Positive Developmental Psychology Model of Distress for Teenagers and Young Adults affected by cancer. The review highlights that implementation of psychological interventions that foster optimism, improve resilience and address self-image may result in reduced distress in TYA’s with cancer.

Keywords: cancer, determinant, psychological distress, teenager and young adult, theoretical model

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Authors: Mireille Elhajj, Washington Ochieng, Deeph Chana

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The challenges of the rapid growth in the demand for transport has traditionally been seen within the context of the problems of congestion, air quality, climate change, safety, and affordability. However, there are increasing threats including those related to crime such as cyber-attacks that threaten the security of the transport of people and goods. To the best of the authors’ knowledge, this paper presents for the first time, a comprehensive framework for the assessment of the current and future security issues of multi-modal transport systems. The approach or method proposed is based on a structured framework starting with a detailed specification of the transport asset map (transport system architecture), followed by the identification of vulnerabilities. The asset map and vulnerabilities are used to identify the various approaches for exploitation of the vulnerabilities, leading to the creation of a set of threat scenarios. The threat scenarios are then transformed into risks and their categories, and include insights for their mitigation. The consideration of the mitigation space is holistic and includes the formulation of appropriate policies and tactics and/or technical interventions. The quality of the framework is ensured through a structured and logical process that identifies the stakeholders, reviews the relevant documents including policies and identifies gaps, incorporates targeted surveys to augment the reviews, and uses subject matter experts for validation. The approach to categorising security risks is an extension of the current methods that are typically employed. Specifically, the partitioning of risks into either physical or cyber categories is too limited for developing mitigation policies and tactics/interventions for transport systems where an interplay between physical and cyber processes is very often the norm. This interplay is rapidly taking on increasing significance for security as the emergence of cyber-physical technologies, are shaping the future of all transport modes. Examples include: Connected Autonomous Vehicles (CAVs) in road transport; the European Rail Traffic Management System (ERTMS) in rail transport; Automatic Identification System (AIS) in maritime transport; advanced Communications, Navigation and Surveillance (CNS) technologies in air transport; and the Internet of Things (IoT). The framework adopts a risk categorisation scheme that considers risks as falling within the following threat→impact relationships: Physical→Physical, Cyber→Cyber, Cyber→Physical, and Physical→Cyber). Thus the framework enables a more complete risk picture to be developed for today’s transport systems and, more importantly, is readily extendable to account for emerging trends in the sector that will define future transport systems. The framework facilitates the audit and retro-fitting of mitigations in current transport operations and the analysis of security management options for the next generation of Transport enabling strategic aspirations such as systems with security-by-design and co-design of safety and security to be achieved. An initial application of the framework to transport systems has shown that intra-modal consideration of security measures is sub-optimal and that a holistic and multi-modal approach that also addresses the intersections/transition points of such networks is required as their vulnerability is high. This is in-line with traveler-centric transport service provision, widely accepted as the future of mobility services. In summary, a risk-based framework is proposed for use by the stakeholders to comprehensively and holistically assess the security of transport systems. It requires a detailed understanding of the transport architecture to enable a detailed vulnerabilities analysis to be undertaken, creates threat scenarios and transforms them into risks which form the basis for the formulation of interventions.

Keywords: mitigations, risk, transport, security, vulnerabilities

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Authors: James Beauchemin

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Introduction: The incarceration of people with mental illness and substance use disorders is a major public health issue, with social, clinical, and economic implications. Yoga participation has been associated with numerous psychological benefits; however, there is a paucity of research examining impacts of yoga with incarcerated populations. The purpose of this study was to evaluate effectiveness of a six-week yoga intervention on several mental health-related variables, including emotion regulation, body awareness, and warnings of substance relapse among incarcerated females. Methods: This study utilized a pre-post, three-arm design, with participants assigned to intervention, therapeutic community, or general population groups. A between-groups analysis of covariance (ANCOVA) was conducted across groups to assess intervention effectiveness using the Difficulties in Emotion Regulation Scale (DERS), Scale of Body Connection (SBC), and Warnings of Relapse (AWARE) Questionnaire. Results: ANCOVA results for warnings of relapse (AWARE) revealed significant between-group differences F(2, 80) = 7.15, p = .001; np2 = .152), with significant pairwise comparisons between the intervention group and both the therapeutic community (p = .001) and the general population (p = .005) groups. Similarly, significant differences were found for emotional regulation (DERS) F(2, 83) = 10.521, p = .000; np2 = .278). Pairwise comparisons indicated a significant difference between the intervention and general population (p = .01). Finally, significant differences between the intervention and control groups were found for body awareness (SBC) F(2, 84) = 3.69, p = .029; np2 = .081). Between-group differences were clarified via pairwise comparisons, indicating significant differences between the intervention group and both the therapeutic community (p = .028) and general population groups (p = .020). Implications: Study results suggest that yoga may be an effective addition to integrative mental health and substance use treatment for incarcerated women, and contributes to increasing evidence that holistic interventions may be an important component for treatment with this population. Specifically, given the prevalence of mental health and substance use disorders, findings revealed that changes in body awareness and emotion regulation may be particularly beneficial for incarcerated populations with substance use challenges as a result of yoga participation. From a systemic perspective, this proactive approach may have long-term implications for both physical and psychological well-being for the incarcerated population as a whole, thereby decreasing the need for traditional treatment. By integrating a more holistic, salutogenic model that emphasizes prevention, interventions like yoga may work to improve the wellness of this population, while providing an alternative or complementary treatment option for those with current symptoms.

Keywords: yoga, mental health, incarceration, wellness

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Authors: Kenneth Larsen, Astrid Aasland, Synnve Schjølberg, Trond Diseth

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Autism Spectrum Disorders (ASD) are neurodevelopmental disorders emerging in early development characterized by impairment in social communication skills and a restricted, repetitive and stereotyped patterns of behavior and interests. Early identification and interventions potentially improve development and quality of life of children with ASD. Symptoms of ASD are apparent through the second year of life, yet diagnostic age are still around 4 years of age. This study explored whether symptoms associated with ASD are possible to identify in typical Norwegian day-care centers in the second year of life. Results of this study clearly indicates that most described symptoms also are identifiable by day-care staff, and that a short observation list of 5 symptoms clearly identify children with ASD from a sample of normal developing peers.

Keywords: autism, early identification, day-care, screening

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Ahmet Karagoz, Irfan Karagoz

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Synthetic Aperture Radar (SAR) is a radar mechanism that can be integrated into manned and unmanned aerial vehicles to create high-resolution images in all weather conditions, regardless of day and night. In this study, SAR images of military vehicles with different azimuth and descent angles are pre-processed at the first stage. The main purpose here is to reduce the high speckle noise found in SAR images. For this, the Wiener adaptive filter, the mean filter, and the median filters are used to reduce the amount of speckle noise in the images without causing loss of data. During the image segmentation phase, pixel values are ordered so that the target vehicle region is separated from other regions containing unnecessary information. The target image is parsed with the brightest 20% pixel value of 255 and the other pixel values of 0. In addition, by using appropriate parameters of statistical region merging algorithm, segmentation comparison is performed. In the step of feature extraction, the feature vectors belonging to the vehicles are obtained by using Gabor filters with different orientation, frequency and angle values. A number of Gabor filters are created by changing the orientation, frequency and angle parameters of the Gabor filters to extract important features of the images that form the distinctive parts. Finally, images are classified by sparse representation method. In the study, l₁ norm analysis of sparse representation is used. A joint database of the feature vectors generated by the target images of military vehicle types is obtained side by side and this database is transformed into the matrix form. In order to classify the vehicles in a similar way, the test images of each vehicle is converted to the vector form and l₁ norm analysis of the sparse representation method is applied through the existing database matrix form. As a result, correct recognition has been performed by matching the target images of military vehicles with the test images by means of the sparse representation method. 97% classification success of SAR images of different military vehicle types is obtained.

Keywords: automatic target recognition, sparse representation, image classification, SAR images

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Authors: H. Jung, N. Kim, B. Kang, J. Choe

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History matching is a crucial procedure for predicting reservoir performances and making future decisions. However, it is difficult due to uncertainties of initial reservoir models. Therefore, it is important to have reliable initial models for successful history matching of highly heterogeneous reservoirs such as channel reservoirs. In this paper, we proposed a novel scheme for regenerating geological models using support vector machine (SVM) and principal component analysis (PCA). First, we perform PCA for figuring out main geological characteristics of models. Through the procedure, permeability values of each model are transformed to new parameters by principal components, which have eigenvalues of large magnitude. Secondly, the parameters are projected into two-dimensional plane by multi-dimensional scaling (MDS) based on Euclidean distances. Finally, we train an SVM classifier using 20% models which show the most similar or dissimilar well oil production rates (WOPR) with the true values (10% for each). Then, the other 80% models are classified by trained SVM. We select models on side of low WOPR errors. One hundred channel reservoir models are initially generated by single normal equation simulation. By repeating the classification process, we can select models which have similar geological trend with the true reservoir model. The average field of the selected models is utilized as a probability map for regeneration. Newly generated models can preserve correct channel features and exclude wrong geological properties maintaining suitable uncertainty ranges. History matching with the initial models cannot provide trustworthy results. It fails to find out correct geological features of the true model. However, history matching with the regenerated ensemble offers reliable characterization results by figuring out proper channel trend. Furthermore, it gives dependable prediction of future performances with reduced uncertainties. We propose a novel classification scheme which integrates PCA, MDS, and SVM for regenerating reservoir models. The scheme can easily sort out reliable models which have similar channel trend with the reference in lowered dimension space.

Keywords: history matching, principal component analysis, reservoir modelling, support vector machine

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Authors: A. Tamimi, S. Greatrex-White, A. Narayanasamy

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Background: Spirituality has been increasingly acknowledged in the nursing literature as an important element of holistic patient care. To date there have been numerous studies investigating the meaning of spirituality in Western cultures. Spirituality in Middle Eastern countries however remains under-researched. We will present a study which aimed to address this gap. Aim: The study aimed to explore how spirituality manifests in the lives of Jordanian End Stage Renal Failure (ESRF) patients. Methodology and Method: A hermeneutic phenomenological approach was adopted informed by the philosophy of Martin Heidegger. Participants (n=27) were recruited from four different dialysis units: in a public hospital, a private hospital, an educational hospital and a refugee’s hospital in Jordan. Data was collected through in-depth unstructured interviews. Data Analysis: Analysis was guided by the tenets of hermeneutic phenomenology namely: gaining immediate sense of what was said both during and after each interview, transcribing data verbatim, translating interviews into the English language, intensive reading and re-reading, seeking meaning units by line to line coding, developing situated structures (how spirituality was manifest in each text), developing a general structure from the individual situated structures (how the phenomenon ‘spirituality’ comes into being). Findings: Three major themes emerged from analysis: Religion, Relationships and Desperation. We will argue that a ‘secular’ concept of spirituality had no meaning for the participants in the study. Spirituality is fundamentally part of religion and vice versa. Discussion: The findings may have consequences for the use of spirituality in multi-cultural settings in Western countries. Additionally, findings highlighted an important emphasis on the practice of spirituality, often underestimated in previous literature for Arab-Muslim Jordanian patients. Conclusion: The study findings contribute to the existing gap in knowledge regarding how Arab-Muslim Jordanian ESRF patients experience spirituality during their illness. It provides valuable insights into the importance of spirituality for this patient group and suggests how nurses, educators and policy makers might help address ESRF patients’ spiritual needs and provide appropriate spiritual care. We suggest the findings may have relevance beyond the Jordanian context in educating nurses’ on the importance of appreciating the religious dimension of spirituality.

Keywords: spirituality, nursing, muslim, Jordan

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Authors: Sašo Pečnik, Borut Žalik

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This paper presents a real-time visualization technique and filtering of classified LiDAR point clouds. The visualization is capable of displaying filtered information organized in layers by the classification attribute saved within LiDAR data sets. We explain the used data structure and data management, which enables real-time presentation of layered LiDAR data. Real-time visualization is achieved with LOD optimization based on the distance from the observer without loss of quality. The filtering process is done in two steps and is entirely executed on the GPU and implemented using programmable shaders.

Keywords: filtering, graphics, level-of-details, LiDAR, real-time visualization

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Authors: Zeinab Zaremohzzabieh, Syasya Firzana Azmi, Haslinda Abdullah, Soh Kim Geok, Aini Azeqa Ma'rof, Hayrol Azril Mohammed Shaffril

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The escalating prevalence of doping in sports, casting a shadow over both high-performance and recreational settings, has emerged as a formidable concern, particularly within the realm of young athletes. Doping, characterized by the surreptitious use of prohibited substances to gain a competitive edge, underscores the pressing need for comprehensive and efficacious preventive measures. This study aims to address a crucial void in current research by unraveling the motivations that drive clean adolescent athletes to steadfastly abstain from performance-enhancing substances. In navigating this intricate landscape, the study adopts a positive psychology perspective, investigating into the conditions and processes that contribute to the holistic well-being of individuals and communities. At the heart of this exploration lies the application of the PERMA model, a comprehensive positive psychology framework encapsulating positive emotion, engagement, relationships, meaning, and accomplishments. This model functions as a distinctive lens, dissecting intervention results to offer nuanced insights into the complex dynamics of clean sport behavior. The research is poised to usher in a paradigm shift from conventional anti-doping strategies, predominantly fixated on identifying deficits, towards an innovative approach firmly rooted in positive psychology. The objective of this study is to evaluate the efficacy of a positive education intervention program tailored to promote clean sport behavior among Malaysian adolescent athletes. Representing unexplored terrain within the landscape of anti-doping efforts, this initiative endeavors to reshape the focus from deficiencies to strengths. The meticulously crafted pilot study engages thirty adolescent athletes, divided into a control group of 15 and an experimental group of 15. The pilot study serves as the crucible to assess the effectiveness of the prepared intervention package, providing indispensable insights that will meticulously guide the finalization of an all-encompassing intervention program for the main study. The main study adopts a pioneering two-arm randomized control trial methodology, actively involving adolescent athletes from diverse Malaysian high schools. This approach aims to address critical lacunae in anti-doping strategies, specifically calibrated to resonate with the unique context of Malaysian schools. The study, cognizant of the imperative to develop preventive measures harmonizing with the cultural and educational milieu of Malaysian adolescent athletes, aspires to cultivate a culture of clean sport. In conclusion, this research aspires to contribute unprecedented insights into the efficacy of positive education interventions firmly rooted in the PERMA model. By unraveling the intricacies of clean sport behavior, particularly within the context of Malaysian adolescent athletes, the study seeks to introduce transformative preventive methods. The adoption of positive psychology as an avant-garde anti-doping tool represents an innovative and promising approach, bridging a conspicuous gap in scholarly research and offering potential panaceas for the sporting community. As this study unfurls its chapters, it carries the promise not only to enrich our understanding of clean sport behavior but also to pave the way for positive metamorphosis within the realm of adolescent sports in Malaysia.

Keywords: positive education interventions, a pilot study, clean sport behavior, adolescent athletes, Malaysia

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Authors: Meenal Badki

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We address the issue of active feature determination, where the objective is to determine the set of examples on which additional data (such as lab tests) needs to be gathered, given a large number of examples with some features (such as demographics) and some examples with all the features (such as the complete Electronic Health Record). We note that certain features may be more costly, unique, or laborious to gather. Our proposal is a general active learning approach that is independent of classifiers and similarity metrics. It allows us to identify examples that differ from the full data set and obtain all the features for the examples that match. Our comprehensive evaluation shows the efficacy of this approach, which is driven by four authentic clinical tasks.

Keywords: feature determination, classification, active learning, sample-efficiency

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Authors: Fuad M. Alkoot

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The main component of a machine learning system is the classifier. Classifiers are mathematical models that can perform classification tasks for a specific application area. Additionally, many classifiers are combined using any of the available methods to reduce the classifier error rate. The benefits gained from the combination of multiple classifier designs has motivated the development of diverse approaches to multiple classifiers. We aim to investigate using fractal geometry to develop an improved classifier combiner. Initially we experiment with measuring the fractal dimension of data and use the results in the development of a combiner strategy.

Keywords: fractal geometry, machine learning, classifier, fractal dimension

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Authors: Dil Ware Alam, Faiza Zebeen, Sumaya Binte Masud

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COVID-19) has impacted the whole world, including Bangladesh. The epidemic has reduced access to health care, particularly for women, creating challenges for an increasingly disadvantaged population. Women's health and well-being in Bangladesh are susceptible to a rise in domestic violence and need to be addressed quickly. The planet has been greatly influenced by Coronavirus disease 2019 (COVID-19), and Bangladesh is no difference. The pandemic has resulted in a decline in the availability of health care, notably for women's health problems, leading to an increase in difficulties for an increasingly marginalized group. Maternity care, maternal health programs, medical interventions, nutritional counseling and mental health care, are not discussed, and women's health and well-being in Bangladesh is vulnerable with a spike in domestic violence and needs to be resolved urgently.

Keywords: Covid-19, mental health, reproductive health, Bangladesh

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Authors: Mohamed Mamdouh Al-Banna, Warda Youssef Mohamed Morsy, Hanaa Ali El-Feky, Ashraf Hussein Abdelmohsen

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Background: The mechanically ventilated patients need a special nursing care with continuous closed observation. The patients’ body mass index may affect their prognosis or outcomes. Aim of the study: to investigate the relationship between BMI and selected outcomes of critically ill mechanically ventilated patients. Research Design: A descriptive correlational research design was utilized Research questions: a) what is the BMI profile of mechanically ventilated patients admitted to critical care units over a period of six months? b) What is the relationship between body mass index and frequency of organ dysfunction, length of ICU stay, weaning from mechanical ventilation, and the mortality rate among adult critically ill mechanically ventilated patients? Setting: different intensive care units of Cairo University Hospitals. Sample: A convenience sample of 30 mechanically ventilated patients for at least 72 hours. Tools of data collection: Three tools were utilized to collect data pertinent to the current study: tool 1: patients’ sociodemographic and medical data sheet, tool 2: BURNS Wean Assessment Program (BWAP) checklist, tool 3: Sequential organ failure assessment (SOFA score) sheet. Results: The majority of the studied sample (77%) was males, and (26.7 %) of the studied sample were in the age group of 18-28 years old, and (26.7 %) were in the age group of 40-50 years old. Moreover, two thirds (66.7%) of the studied sample were within normal BMI. No significant statistical relationship between BMI category and ICU length of stay or the mortality rate among the studied sample, (X² = 11.31, P value = 0.79), (X² = 0.15, P value = 0.928) respectively. No significant statistical relationship between BMI category and the weaning trials from mechanical ventilation among the studied sample, (X² = 0.15, P value = 0.928). No significant statistical relationship was found between BMI category and the occurrence of organ dysfunction among the studied sample, (X² = 2.54, P value = 0.637). Conclusion: No relationship between the BMI categories and the selected patients’ outcomes (weaning from MV, length of ICU stay, occurrence of organ dysfunction, mortality rate). Recommendations: Replication of this study on a larger sample from different geographical locations in Arab Republic of Egypt, conducting farther studies to assess the effect of the quality of nursing care on the mechanically ventilated patients’ outcomes.

Keywords: mechanical ventilation, body mass index, outcomes of mechanically ventilated patient, organ failure

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Authors: Mohammed Arif, Abdessalam Kifouche

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Optical character recognition (OCR) has a main role in the present time. It's capable to solve many serious problems and simplify human activities. The OCR yields to 70's, since many solutions has been proposed, but unfortunately, it was supportive to nothing but Latin languages. This work proposes a system of recognition of an off-line Arabic handwriting. This system is based on a structural segmentation method and uses support vector machines (SVM) in the classification phase. We have presented a state of art of the characters segmentation methods, after that a view of the OCR area, also we will address the normalization problems we went through. After a comparison between the Arabic handwritten characters & the segmentation methods, we had introduced a contribution through a segmentation algorithm.

Keywords: OCR, segmentation, Arabic characters, PAW, post-processing, SVM

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Authors: Erin Lynne Plettenberg, Jeremy Vickery

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In an access-control situation, the role of a user determines whether a data request is appropriate. This paper combines vetted web mining and logic modeling to build a lightweight system for determining the role of a health care provider based only on their prior authorized requests. The model identifies provider roles with 100% recall from very little data. This shows the value of vetted web mining in AI systems, and suggests the impact of the ICD classification on medical practice.

Keywords: electronic medical records, information extraction, logic modeling, ontology, vetted web mining

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Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations of previous approaches, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with attention mechanism. In a previous work on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: transformers, generative ai, gene expression design, classification

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Authors: Maryam M. Bashir, Luai A. Ahmed, Meera R. Alshamsi, Sara Almahrooqi, Taif Alyammahi, Shooq A. Alshehhi, Waad I. Alhammadi, Fatima H. Alhammadi, Hind A. Alhosani, Rami H. Al-Rifai, Fatma Al-Maskari

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Obesity in the Middle East and North Africa (MENA) region has exponentially increased over the past five decades due to rapid urbanization and unhealthy lifestyle changes. It has been well established that overweight and obesity increase the risk of non-communicable diseases (NCDs) and are the leading cause of mortality and economic burden locally, and globally. In the United Arab Emirates (UAE), there is a growing epidemic of obesity and other chronic diseases like type 2 diabetes mellitus and cardiovascular diseases. Prevalence of overweight and obesity in UAE range up to 70% depending on the group being studied. Hence, there is a need to explore their patterns in the country for more targeted and responsive interventions. Our study aimed to explore the patterns of overweight and obesity and some self-reported chronic diseases among university students in Abu Dhabi, the capital city of UAE. A validated online self-administered questionnaire was used to collect data from UAE University (UAEU) students, 18years and above, from August to September 2021. Students’ characteristics were summarized using appropriate descriptive statistics. Overweight, obesity and self-reported chronic diseases were described and compared between male and female students using chi-square and t tests. Other associated factors were also explored in relation to overweight and obesity. All analyses were conducted using STATA statistical software version 16.1 (StataCorp LLC, College Station, TX, USA). 902 students participated in the study. 79.8% were females and mean age was 21.90 ± 5.19 years. Majority of the respondents were undergraduate students (80.71%). The prevalence of self-reported chronic diseases was 22.95%. Obesity (BMI≥30kg/m2), Diabetes Mellitus, and Asthma/Allergies were the commonest diseases (12.48%, 4.21% & 3.22%, respectively). Approximately 5% of the students reported more than one chronic disease. Out of the 833 participating students who had complete weight and height data, prevalence of overweight and obesity was 34.81% (22.33% and 12.48%, respectively). More than half of the male students (54.36%) were overweight or obese. This is significantly higher than in female students (30.56%, p=0.001). Overweight/obesity when compared to normal weight is associated with increasing mean age [23.40 vs 21.01, respectively (p=0.001)]. In addition to gender and age, being married [57.63% vs 31.05% (p=0.001)], being a postgraduate student [51.59% vs 30.92% (p=0.001)] and having two or more chronic diseases [65.85% vs 33.21% (p=0.001)] were also significantly associated with overweight/obesity. Our study showed that almost a quarter of the participating university students reported at least one chronic disease. Obesity was the commonest and more than 1 in 3 students were either overweight or obese. This shows the need for intensive health promotion and screening programs on obesity and other chronic diseases to meet the health needs of these students. This study is also a basis for further research, especially qualitative, to explore the relevant risk factors and risk groups for more targeted interventions.

Keywords: chronic disease, obesity, overweight, students, United Arab Emirates

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Authors: Muhammad Irfan Marwat, Sadaqat Jan, Syed Zafar Ali Shah

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Software architecture plays a key role in software development but absence of formal description of software architecture causes different impede in software development. To cope with these difficulties, ontology has been used as artifact. This paper proposes ontology for software architectural design based on IEEE model for architecture description and Kruchten 4+1 model for viewpoints classification. For categorization of style and views, ISO/IEC 42010 has been used. Corpus method has been used to evaluate ontology. The main aim of the proposed ontology is to classify and locate software architectural design information.

Keywords: semantic-based software architecture, software architecture, ontology, software engineering

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Authors: Hyekyung Woo, Gwihyun Kim

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mHealth, which encompasses mobile health technologies and interventions, is rapidly evolving in various medical specialties, and its impact is evident in oncology. This review describes current trends in research addressing the integration of mHealth into the management of breast cancer by examining evaluations of mHealth and its contributions across the cancer care continuum. Mobile technologies are perceived as effective in prevention and as feasible for managing breast cancer, but the diagnostic accuracy of these tools remains in doubt. Not all phases of breast cancer treatment involve mHealth, and not all have been addressed by research. These drawbacks in the application of mHealth to breast cancer management call for intensified research to strengthen its role in breast cancer care.

Keywords: mobile application, breast cancer, content analysis, mHealth

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Authors: Kristina Sakalauskiene, Renaldas Raisutis, Gintare Linkeviciute, Skaidra Valiukeviciene

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Cutaneous melanoma is a melanocytic skin tumour, which has a very poor prognosis while is highly resistant to treatment and tends to metastasize. Thickness of melanoma is one of the most important biomarker for stage of disease, prognosis and surgery planning. In this study, we hypothesized that the automatic analysis of spectrophotometric images and high-frequency ultrasonic 2D data can improve differential diagnosis of cutaneous melanoma and provide additional information about tumour penetration depth. This paper presents the novel complex automatic system for non-invasive melanocytic skin tumour differential diagnosis and penetration depth evaluation. The system is composed of region of interest segmentation in spectrophotometric images and high-frequency ultrasound data, quantitative parameter evaluation, informative feature extraction and classification with linear regression classifier. The segmentation of melanocytic skin tumour region in ultrasound image is based on parametric integrated backscattering coefficient calculation. The segmentation of optical image is based on Otsu thresholding. In total 29 quantitative tissue characterization parameters were evaluated by using ultrasound data (11 acoustical, 4 shape and 15 textural parameters) and 55 quantitative features of dermatoscopic and spectrophotometric images (using total melanin, dermal melanin, blood and collagen SIAgraphs acquired using spectrophotometric imaging device SIAscope). In total 102 melanocytic skin lesions (including 43 cutaneous melanomas) were examined by using SIAscope and ultrasound system with 22 MHz center frequency single element transducer. The diagnosis and Breslow thickness (pT) of each MST were evaluated during routine histological examination after excision and used as a reference. The results of this study have shown that automatic analysis of spectrophotometric and high frequency ultrasound data can improve non-invasive classification accuracy of early-stage cutaneous melanoma and provide supplementary information about tumour penetration depth.

Keywords: cutaneous melanoma, differential diagnosis, high-frequency ultrasound, melanocytic skin tumours, spectrophotometric imaging

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Authors: Jung-Hun Cho, Tae-Heon Moon, Sun-Young Heo

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A lot of urban problems, caused by urbanization and industrialization, have occurred around the world. In particular, the creation of satellite towns, which was attributed to the explicit expansion of the city, has led to the traffic problems and the hollowization of old towns, raising the necessity of urban regeneration in old towns along with the aging of existing urban infrastructure. To select urban regeneration priority regions for the strategic execution of urban regeneration in Korea, the number of population, the number of businesses, and deterioration degree were chosen as standards. Existing standards had a limit in coping with solving urban problems fundamentally and rapidly changing reality. Therefore, it was necessary to add new indicators that can reflect the decline in relevant cities and conditions. In this regard, this study selected Busan Metropolitan City, Korea as the target area as a leading city, where urban regeneration such as an international port city has been activated like Yokohama, Japan. Prior to setting the urban regeneration priority region, the conditions of reality should be reflected because uniform and uncharacterized projects have been implemented without a quantitative analysis about population behavior within the region. For this reason, this study conducted a characterization analysis and type classification, based on the user behaviors by using representative floating population of the big data, which is a hot issue all over the society in recent days. The target areas were analyzed in this study. While 23 regions were classified as three types in existing Busan Metropolitan City urban regeneration priority region, 23 regions were classified as four types in existing Busan Metropolitan City urban regeneration priority region in terms of the type classification on the basis of user behaviors. Four types were classified as follows; type (Ⅰ) of young people - morning type, Type (Ⅱ) of the old and middle-aged- general type with sharp floating population, type (Ⅲ) of the old and middle aged-24hour-type, and type (Ⅳ) of the old and middle aged with less floating population. Characteristics were shown in each region of four types, and the study results of user behaviors were different from those of existing urban regeneration priority region. According to the results, in type (Ⅰ) young people were the majority around the existing old built-up area, where floating population at dawn is four times more than in other areas. In Type (Ⅱ), there were many old and middle-aged people around the existing built-up area and general neighborhoods, where the average floating population was more than in other areas due to commuting, while in type (Ⅲ), there was no change in the floating population throughout 24 hours, although there were many old and middle aged people in population around the existing general neighborhoods. Type (Ⅳ) includes existing economy-based type, central built-up area type, and general neighborhood type, where old and middle aged people were the majority as a general type of commuting with less floating population. Unlike existing urban regeneration priority region, these types were sub-divided according to types, and in this study, approach methods and basic orientations of urban regeneration were set to reflect the reality to a certain degree including the indicators of effective floating population to identify the dynamic activity of urban areas and existing regeneration priority areas in connection with urban regeneration projects by regions. Therefore, it is possible to make effective urban plans through offering the substantial ground by utilizing scientific and quantitative data. To induce more realistic and effective regeneration projects, the regeneration projects tailored to the present local conditions should be developed by reflecting the present conditions on the formulation of urban regeneration strategic plans.

Keywords: floating population, big data, urban regeneration, urban regeneration priority region, type classification

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

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The increasing use of deep learning applications in production is becoming a competitive advantage. Predictive quality enables the assurance of product quality by using data-driven forecasts via machine learning models as a basis for decisions on test results. The use of real Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the leakage of directional valves.

Keywords: artificial neural networks, classification, hydraulics, predictive quality, deep learning

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Authors: James D. Beauchemin

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Introduction: The incarceration of people with mental illness and substance use disorders is a major public health issue with social, clinical, and economic implications. Yoga participation has been associated with numerous psychological benefits; however, there is a paucity of research examining impacts of yoga with incarcerated populations. The purpose of this study was to evaluate effectiveness of a six-week yoga intervention on several mental health-related variables, including emotion regulation, body awareness, and warnings of substance relapse among incarcerated females. Methods: This study utilized a pre-post, three-arm design, with participants assigned to intervention, therapeutic community, or general population groups. A between-group analysis of covariance (ANCOVA) was conducted across groups to assess intervention effectiveness using the Difficulties in Emotion Regulation Scale (DERS), Scale of Body Connection (SBC), and Warnings of Relapse (AWARE) Questionnaire. Results: ANCOVA results for warnings of relapse (AWARE) revealed significant between-group differences F(2, 80) = 7.15, p = .001; np2 = .152), with significant pairwise comparisons between the intervention group and both the therapeutic community (p = .001) and the general population (p = .005) groups. Similarly, significant differences were found for emotional regulation (DERS) F(2, 83) = 10.521, p = .000; np2 = .278). Pairwise comparisons indicated a significant difference between the intervention and general population (p = .01). Finally, significant differences between the intervention and control groups were found for body awareness (SBC) F(2, 84) = 3.69, p = .029; np2 = .081). Between-group differences were clarified via pairwise comparisons, indicating significant differences between the intervention group and both the therapeutic community (p = .028) and general population groups (p = .020). Implications: Study results suggest that yoga may be an effective addition to integrative mental health and substance use treatment for incarcerated women and contributes to increasing evidence that holistic interventions may be an important component for treatment with this population. Specifically, given the prevalence of mental health and substance use disorders, findings revealed that changes in body awareness and emotion regulation might be particularly beneficial for incarcerated populations with substance use challenges as a result of yoga participation. From a systemic perspective, this proactive approach may have long-term implications for both physical and psychological well-being for the incarcerated population as a whole, thereby decreasing the need for traditional treatment. By integrating a more holistic, salutogenic model that emphasizes prevention, interventions like yoga may work to improve the wellness of this population while providing an alternative or complementary treatment option for those with current symptoms.

Keywords: wellness, solution-focused coaching, college students, prevention

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Authors: Shekoufeh Behdad, Amirhossein Yadegari, Leila Ghodrati, Saman Yadegari

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Context: Preoperative anxiety is a common psychological reaction experienced by all patients undergoing surgery. It can have negative effects on the patient's well-being and even impact surgical outcomes. Therefore, finding effective interventions to reduce preoperative anxiety is important in improving patient care. Research Aim: The aim of this study is to compare the effects of oral administration of zaleplon (5 mg) and alprazolam (0.5 mg) on preoperative anxiety levels in women undergoing gynecological abdominal surgery. Methodology: This study is a double-blind, randomized clinical trial conducted after receiving approval from the university's ethics committee and obtaining written informed consent from the patients. The night before the surgery, patients were randomly assigned to receive either 0.5 mg of alprazolam or 5 mg of zaleplon orally. Anxiety levels, measured using a 10-cm visual analog scale, and hemodynamic variables (blood pressure and heart rate) were assessed before drug administration and on the morning of the operation after the patient entered the pre-operation room. Findings: The study found that there were no significant differences in mean anxiety levels or hemodynamic variables before and after administration of either drug in both groups (P value > 0.05). This suggests that both 0.5 mg of alprazolam and 5 mg of zaleplon effectively reduce preoperative anxiety in women undergoing abdominal surgery without serious side effects. Theoretical Importance: This study contributes to the understanding of the effectiveness of alprazolam and zaleplon in reducing preoperative anxiety. It adds to the existing literature on pharmacological interventions for anxiety management, specifically in the context of gynecological abdominal surgery. Data Collection: Data for this study were collected through the assessment of anxiety levels using a visual analog scale and measuring hemodynamic variables, including systolic, diastolic, and mean arterial blood pressures, as well as heart rate. These measurements were taken before drug administration and on the morning of the surgery. Analysis Procedures: Statistical analysis was performed to compare the mean anxiety levels and hemodynamic variables before and after drug administration in the two groups. The significance of the differences was determined using appropriate statistical tests. Questions Addressed: This study aimed to answer the question of whether there are differences in the effects of alprazolam and zaleplon on preoperative anxiety levels in women undergoing gynecological abdominal surgery. Conclusion: The oral administration of both 0.5 mg of alprazolam and 5 mg of zaleplon the night before surgery effectively reduces preoperative anxiety in women undergoing abdominal surgery. These findings have important implications for the management of preoperative anxiety and can contribute to improving the overall surgical experience for patients.

Keywords: zaleplon, alprazolam, premedication, abdominal surgery

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Authors: Zeinabsadat Haghshenas

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Bloom’s Taxonomy has been changed during the years. The idea of this writing is about the revision that has happened in both facts and terms. It also contains case studies of using cognitive Bloom’s taxonomy in teaching geometric solids to the secondary school students, affective objectives in a creative workshop for adults and psychomotor objectives in fixing a malfunctioned refrigerator lamp. There is also pointed to the important role of classification objectives in adult education as a way to prevent memory loss.

Keywords: adult education, affective domain, cognitive domain, memory loss, psychomotor domain

Procedia PDF Downloads 447