Search results for: nursing interventions classification

Authors: Panagodage Janaka Sampath Fernando

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Brain circulation has become a buzzword in the skilled migration literature. However, promoting brain circulation; returning of skilled migrants is challenging. Success stories in Asia, for instances, Taiwan, and China, are results of rigorous policy interventions of the respective governments. Nonetheless, the same policy mix has failed in other countries making it skeptical to attribute the success of brain circulation to the policy interventions per se. The paper seeks to answer whether the success of brain circulation within the Knowledge Services Industry (KSI) in Sri Lanka is a policy driven or a market driven process. Mixed method approach, which is a combination of case study and survey methods, was employed. Qualitative data derived from ten case studies of returned entrepreneurs whereas quantitative data generated from a self-administered survey of 205 returned skilled migrants (returned skilled employees and entrepreneurs) within KSI. The pull factors have driven the current flow of brain circulation within KSI but to a lesser extent, push factors also have influenced. The founding stone of the industry has been laid by a group of returned entrepreneurs, and the subsequent growth of the industry has attracted returning skilled employees. Sri Lankan government has not actively implemented the reverse brain drain model, however, has played a passive role by creating a peaceful and healthy environment for the industry. Therefore, in contrast to the other stories, brain circulation within KSI has emerged as a market driven process with minimal government interventions. Entrepreneurs play the main role in a market-driven process of brain circulation, and it is free from the inherent limitations of the reverse brain drain model such as discriminating non-migrants and generating a sudden flow of low-skilled migrants. Thus, to experience a successful brain circulation, developing countries should promote returned entrepreneurs by creating opportunities in knowledge-based industries.

Keywords: brain circulation, knowledge services industry, return migration, Sri Lanka

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Authors: Sarinra Thongmee, Krittaporn Prakobsaeng, Adithep Mingsuan, Chanyapak Polkhet, Supattra Wongsuk

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The purposes of this research and developmentwasto develop and evaluation of the clinical nursingpractice guideline (CNPG) for the prevention infection during intubation in patient with suspected or confirmedCOVID-19 patient. This study was developed by using the evidencebased practice model of Soukup (2000) asa conceptual framework. The study consisted of 4 steps: 1) situational analysis of intubation service in patientswith confirmed COVID-19; 2) development of the CNPG; 3) apply the NPG to trial; and 4) evaluation of the CNPG. The sample consisted of 52 nurse anesthetists and 25 infected or suspected COVID-19 patients. The research instrument consisted of 1) the CNPG, which was developed by the researchers; 2) the nurses anesthetist opinion questionnaire to the guideline; 3) the evaluation practice form; and 4) the nurse anesthetist knowledge test on nursing care of patients infected with COVID-19. Data were analyzed by using descriptive statistics, and Wilcoxon matched-pairs signed rank test. The results revealed this developed CNPG consists of 4 sections: 1)the CNPG for airborne precautions2) the preparation of anesthetic and intubation equipments3) the roles and duties of the intubation team, 4) the guidelines for intubation in suspected or confirmed COVID-19 patients. The results of CNPG use found that 1)the provider: using NPG in providers revealed that nurse anesthetist had a higher mean of knowledge scores than before using CNPG statistically significant at the 0.05 level (p<0.01) and able to follow the NPG 100% inall activities. The anesthetic team was not infected with COVID-19 from intubation outside the operating room. 2)the client: the patient was safe, with no complications from intubation. Summary CNPG to prevent infection during reintubation of suspected or confirmedCOVID-19patient was appropriate and applicable to practice.

Keywords: clinical nursing practice guideline, prevention of infection, endotracheal intubation, COVID-19

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Authors: Amanda T. Badaró, Douglas F. Barbin, Sofia T. Garcia, Maria Teresa P. S. Clerici, Amanda R. Ferreira

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Food fortification is the intentional addition of a nutrient in a food matrix and has been widely used to overcome the lack of nutrients in the diet or increasing the nutritional value of food. Fortified food must meet the demand of the population, taking into account their habits and risks that these foods may cause. Wheat and its by-products, such as semolina, has been strongly indicated to be used as a food vehicle since it is widely consumed and used in the production of other foods. These products have been strategically used to add some nutrients, such as fibers. Methods of analysis and quantification of these kinds of components are destructive and require lengthy sample preparation and analysis. Therefore, the industry has searched for faster and less invasive methods, such as Near-Infrared Spectroscopy (NIR). NIR is a rapid and cost-effective method, however, it is based on indirect measurements, yielding high amount of data. Therefore, NIR spectroscopy requires calibration with mathematical and statistical tools (Chemometrics) to extract analytical information from the corresponding spectra, as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA is well suited for NIR, once it can handle many spectra at a time and be used for non-supervised classification. Advantages of the PCA, which is also a data reduction technique, is that it reduces the data spectra to a smaller number of latent variables for further interpretation. On the other hand, LDA is a supervised method that searches the Canonical Variables (CV) with the maximum separation among different categories. In LDA, the first CV is the direction of maximum ratio between inter and intra-class variances. The present work used a portable infrared spectrometer (NIR) for identification and classification of pure and fiber-fortified semolina samples. The fiber was added to semolina in two different concentrations, and after the spectra acquisition, the data was used for PCA and LDA to identify and discriminate the samples. The results showed that NIR spectroscopy associate to PCA was very effective in identifying pure and fiber-fortified semolina. Additionally, the classification range of the samples using LDA was between 78.3% and 95% for calibration and 75% and 95% for cross-validation. Thus, after the multivariate analysis such as PCA and LDA, it was possible to verify that NIR associated to chemometric methods is able to identify and classify the different samples in a fast and non-destructive way.

Keywords: Chemometrics, fiber, linear discriminant analysis, near-infrared spectroscopy, principal component analysis, semolina

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Authors: Valeria Cotza

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School drop-out is a multifactorial phenomenon that in Italy concerns all those underage students who, at different school stages (up to 16 years old) or training (up to 18 years old), manifest educational difficulties from dropping out of compulsory education without obtaining a qualification to repetition rates and absenteeism. From the 1980s to the 2000s, there was a progressive attenuation of the economic and social model towards a multifactorial reading of the phenomenon, and the European Commission noted the importance of learning about the phenomenon through approaches able to integrate large-scale quantitative surveys with qualitative analyses. It is not a matter of identifying the contextual factors affecting the phenomenon but problematising them by means of systemic and comprehensive in-depth analysis. So, a privileged point of observation and field of intervention are those schools that propose alternative models of teaching and learning to the traditional ones, such as popular and second-chance schools. Alternative schools and interventions grew in these years in Europe as well as in the US and Latin America, working in the direction of greater equity to create the conditions (often absent in conventional schools) for everyone to achieve educational goals. Against extensive Anglo-Saxon and US literature on this topic, there is yet no unambiguous definition of alternative education, especially in Europe, where second-chance education has been most studied. There is little literature on a second chance in Italy and almost none on alternative education (with the exception of method schools, to which in Italy the concept of “alternative” is linked). This research aims to fill the gap by systematically surveying the alternative interventions in the area and beginning to explore some models of popular and second-chance schools and experiences through a mixed methods approach. So, the main research objectives concern the spread of alternative education in the Lombardy region, the main characteristics of these schools and interventions, and their effectiveness in terms of students’ well-being and school results. This paper seeks to answer the first point by presenting the preliminary results of the first phase of the project dedicated to mapping. Through the Google Forms platform, a questionnaire is being distributed to all schools in Lombardy and some schools in the rest of Italy to map the presence of alternative schools and interventions and their main characteristics. The distribution is also taking place thanks to the support of the Milan Territorial and Lombardy Regional School Offices. Moreover, other social realities outside the school system (such as cooperatives and cultural associations) can be questioned. The schools and other realities to be questioned outside Lombardy will also be identified with the support of INDIRE (Istituto Nazionale per Documentazione, Innovazione e Ricerca Educativa, “National Institute for Documentation, Innovation and Educational Research”) and based on existing literature and the indicators of “Futura” Plan of the PNRR (Piano Nazionale di Ripresa e Resilienza, “National Recovery and Resilience Plan”). Mapping will be crucial and functional for the subsequent qualitative and quantitative phase, which will make use of statistical analysis and constructivist grounded theory.

Keywords: school drop-out, alternative education, popular and second-chance schools, map

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Authors: Anum Ali, Kashaf ad Dooja, Asif Saleem

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The future smart cities trend will be towards Internet of Things (IoT); IoT creates dynamic connections in a ubiquitous manner. Smart cities offer ease and flexibility for daily life matters. By using small devices that are connected to cloud servers based on IoT, network traffic between these devices is growing exponentially, whose security is a concerned issue, since ratio of cyber attack may make the network traffic vulnerable. This paper discusses the latest machine learning approaches in related work further to tackle the increasing rate of cyber attacks, machine learning algorithm is applied to IoT-based network traffic data. The proposed algorithm train itself on data and identify different sections of devices interaction by using supervised learning which is considered as a classifier related to a specific IoT device class. The simulation results clearly identify the attacks and produce fewer false detections.

Keywords: IoT, traffic security, deep learning, classification

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Authors: Ali Ulvi Uzer

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Data reduction is an important topic in the field of pattern recognition applications. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. The Principal Component Analysis (PCA) method is frequently used for data reduction. The Support Vector Machine (SVM) method is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, two boreholes taken from the soil sample was used. The classification accuracies for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising.

Keywords: feature selection, sequential forward selection, support vector machines, soil sample

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Authors: Tatiana Tunon, Gottfried Pestal

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Government agencies responsible for the management of fisheries resources publish many types of grey literature, and these materials are increasingly accessible to the public on agency websites. However, scope and quality vary considerably, and end-users need meta-data about the report series when deciding whether to use the information (e.g. apply the methods, include the results in a systematic review), or when prioritizing materials for archiving (e.g. library holdings, reference databases). A proposed taxonomy for these report series was developed based on a review of 41 report series from 6 government agencies in 4 countries (Canada, New Zealand, Scotland, and United States). Each report series was categorized according to multiple criteria describing peer-review process, content, and purpose. A robust classification tree was then fitted to these descriptions, and the resulting taxonomic groups were used to compare agency output from 4 countries using reports available in their online repositories.

Keywords: classification tree, fisheries, government, grey literature

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Authors: Ping Tan, Xiaomeng Su, Yi Shen

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The motion intention in the motor imagery braincomputer interface is identified by classifying the event-related desynchronization (ERD) and event-related synchronization ERS characteristics of sensorimotor rhythm (SMR) in EEG signals. When the subject imagines different limbs or different parts moving, the rhythm components and bandwidth will change, which varies from person to person. How to find the effective sensorimotor frequency band of subjects is directly related to the classification accuracy of brain-computer interface. To solve this problem, this paper proposes a Minimum Distance to Riemannian Mean Classification method based on Non-Uniform Filter Banks. During the training phase, the EEG signals are decomposed into multiple different bandwidt signals by using multiple band-pass filters firstly; Then the spatial covariance characteristics of each frequency band signal are computered to be as the feature vectors. these feature vectors will be classified by the MDRM (Minimum Distance to Riemannian Mean) method, and cross validation is employed to obtain the effective sensorimotor frequency bands. During the test phase, the test signals are filtered by the bandpass filter of the effective sensorimotor frequency bands, and the extracted spatial covariance feature vectors will be classified by using the MDRM. Experiments on the BCI competition IV 2a dataset show that the proposed method is superior to other classification methods.

Keywords: non-uniform filter banks, motor imagery, brain-computer interface, minimum distance to Riemannian mean

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Authors: Sepase K. Ivande

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Morality and frugality are two of the strongest motivations for pro-environmental behavior. However, formulating interventions based on these motivations requires knowledge of who is likely to be motivated by morality and who by frugality. This study investigated which personality traits make someone predisposed to morality motivation and which to frugality motivation for pro-environmental behavior. Results from a series of multiple regression analyses indicated that openness and agreeableness had a positive association with morality motivation, while conscientiousness had a positive association with frugality motivation. The link of agreeableness to morality motivation was stronger when the individuals were also higher on openness. Furthermore, a pair of Wilcoxon signed-rank tests revealed that individuals high on openness and agreeableness but low on conscientiousness scored higher on morality than frugality motivation. On the other hand, individuals low on openness and agreeableness but high on conscientiousness scored higher on frugality than morality motivation. The results of this study could inform the formulation of personalized interventions based on people’s personal predisposition to morality and frugality motivation for pro-environmental behavior, which could be more effective in getting them to be pro-environmental.

Keywords: agreeableness, conscientiousness, frugality, higher order motivations, morality, openness to experience, personality traits, pro-environmental behavior

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Authors: Kailin K. Zhang, Anne W. Thompson

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Electronic learning for continuing professional education (CPE) and interprofessional education (IPE) in healthcare have been shown to improve learners’ satisfaction, attitudes, and performance. Yet, their impact on behavior change in healthcare professionals through continuing interprofessional education (CIPE) is less known. A scoping review of 32 articles from 2010 to 2020 was conducted using the Arksey and O’Malley framework across all healthcare settings. It focused on evaluating the effectiveness of CIPE on behavior change of healthcare professionals, as well as identifying course features of electronic CIPE programs facilitating behavior change. Eight different types of electronic learning methods, including online programs, tele-education, and social media, were identified as interventions. More than 35,542 healthcare professionals participated in the interventions. Electronic learning for CIPE led to positive behavior outcomes in 30 out of 32 studies, especially through a change in patient care practices. The most successful programs provided interactive and authentic learning experiences tailored to learners’ needs while promoting the direct application of what was learned in their clinical settings. Future research should include monitoring of sustained behavior changes and their resultant patient outcomes.

Keywords: behavior change, continuing interprofessional education, distance learning, electronic learning

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Authors: Gizem Eser Erdek

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This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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Authors: Martins Onyekwelu Onuorah, Jnr Dahiru Usman

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Introduction: In the realm of public health, the threat posed by Monkeypox continues to elicit concern, prompting rigorous studies to understand its dynamics and devise effective containment strategies. Particularly significant is its recurrence in variable populations, such as the observed outbreak in Nigeria in 2022. In light of this, our study undertakes a meticulous analysis, employing a data-driven approach to explore, validate, and propose optimized intervention strategies tailored to the distinct dynamics of Monkeypox within varying demographic structures. Utilizing a deterministic mathematical model, we delved into the intricate dynamics of Monkeypox, with a particular focus on a variable population context. Our qualitative analysis provided insights into the disease-free equilibrium, revealing its stability when R0 is less than one and discounting the possibility of backward bifurcation, as substantiated by the presence of a single stable endemic equilibrium. The model was rigorously validated using real-time data from the Nigerian 2022 recorded cases for Epi weeks 1 – 52. Transitioning from qualitative to quantitative, we augmented our deterministic model with optimal control, introducing three time-dependent interventions to scrutinize their efficacy and influence on the epidemic's trajectory. Numerical simulations unveiled a pronounced impact of the interventions, offering a data-supported blueprint for informed decision-making in containing the disease. A comprehensive cost-effectiveness analysis employing the Infection Averted Ratio (IAR), Average Cost-Effectiveness Ratio (ACER), and Incremental Cost-Effectiveness Ratio (ICER) facilitated a balanced evaluation of the interventions’ economic and health impacts. In essence, our study epitomizes a holistic approach to understanding and mitigating Monkeypox, intertwining rigorous mathematical modeling, empirical validation, and economic evaluation. The insights derived not only bolster our comprehension of Monkeypox's intricate dynamics but also unveil optimized, cost-effective interventions. This integration of methodologies and findings underscores a pivotal stride towards aligning public health imperatives with economic sustainability, marking a significant contribution to global efforts in combating infectious diseases.

Keywords: monkeypox, equilibrium states, stability, bifurcation, optimal control, cost-effectiveness

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Authors: Igor Perechuda, Wojciech Cieśliński

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The paper identifies the features of Polish sports clubs in the particular organizational forms: profit and nonprofit. Identification and description of these features is carried out in terms of financial efficiency of the given organizational form. Under the terms of the efficiency the research allows you to specify the advantages of particular organizational sports club form and the following limitations. Paper considers features of sports clubs in range of Polish conditions as legal regulations. The sources of the functioning efficiency of sports clubs may lie in the organizational forms in which they operate. Each of the available forms can be considered either a for-profit or nonprofit enterprise. Depending on this classification there are different capabilities of increasing organizational and financial efficiency of a given sports club. Authors start with general classification and difference between for-profit and non-profit sport clubs. Next identifies specific financial and organizational conditions of both organizational form and then show examples of mixed activity forms and their efficiency effect.

Keywords: financial efficiency, for-profit, non-profit, sports club

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Authors: Sibel Kurt

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In this study, with the use of Goffee and Jones’s corporate culture classification and the scale of this classification, there aimed to analyze a company’s corporate culture which does not have a public relations or communication department. First of all, the type of corporate culture in the company had been determined. Then it questioned if there are subcultures which formed according to demographics or the department of work. In the survey questionnaire, there are 53 questions total. 6 of these questions are about demographics, and 47 of them are about corporate culture. 152 personnel of the company had answered the survey, and the data have been evaluated according to frequency, descriptive, and compare means tests. The type of corporate culture of the company was determined as the 'communal' from the typology of Goffee and Jones in the positive form. There are no subcultures in the company which bases on the demographics, but only one subculture has determined according to the department of work. As a result, the absence of public relations department, personnel’s low level of awareness about corporate culture, and the lack of information between management and employees has been revealed.

Keywords: corporate culture, subculture, public relations, organizational communication

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Authors: T. Garlan, I. Gabelotaud, S. Lucas, E. Marchès

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Production of a global sedimentological seabed map has been initiated in 1995 to provide the necessary tool for searches of aircraft and boats lost at sea, to give sedimentary information for nautical charts, and to provide input data for acoustic propagation modelling. This original approach had already been initiated one century ago when the French hydrographic service and the University of Nancy had produced maps of the distribution of marine sediments of the French coasts and then sediment maps of the continental shelves of Europe and North America. The current map of the sediment of oceans presented was initiated with a UNESCO's general map of the deep ocean floor. This map was adapted using a unique sediment classification to present all types of sediments: from beaches to the deep seabed and from glacial deposits to tropical sediments. In order to allow good visualization and to be adapted to the different applications, only the granularity of sediments is represented. The published seabed maps are studied, if they present an interest, the nature of the seabed is extracted from them, the sediment classification is transcribed and the resulted map is integrated in the world map. Data come also from interpretations of Multibeam Echo Sounder (MES) imagery of large hydrographic surveys of deep-ocean. These allow a very high-quality mapping of areas that until then were represented as homogeneous. The third and principal source of data comes from the integration of regional maps produced specifically for this project. These regional maps are carried out using all the bathymetric and sedimentary data of a region. This step makes it possible to produce a regional synthesis map, with the realization of generalizations in the case of over-precise data. 86 regional maps of the Atlantic Ocean, the Mediterranean Sea, and the Indian Ocean have been produced and integrated into the world sedimentary map. This work is permanent and permits a digital version every two years, with the integration of some new maps. This article describes the choices made in terms of sediment classification, the scale of source data and the zonation of the variability of the quality. This map is the final step in a system comprising the Shom Sedimentary Database, enriched by more than one million punctual and surface items of data, and four series of coastal seabed maps at 1:10,000, 1:50,000, 1:200,000 and 1:1,000,000. This step by step approach makes it possible to take into account the progresses in knowledge made in the field of seabed characterization during the last decades. Thus, the arrival of new classification systems for seafloor has improved the recent seabed maps, and the compilation of these new maps with those previously published allows a gradual enrichment of the world sedimentary map. But there is still a lot of work to enhance some regions, which are still based on data acquired more than half a century ago.

Keywords: marine sedimentology, seabed map, sediment classification, world ocean

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Authors: M. G. Dirodi, G. Gugliotta, C. Leonardi

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The member states shall establish zones and agglomerations throughout their territory to assess and manage air quality in order to comply with European directives. In Italy decree 155/2010, transposing Directive 2008/50/EC on ambient air quality and cleaner air for Europe, merged into a single act the previous provisions on ambient air quality assessment and management, including those resulting from the implementation of Directive 2004/107/EC relating to arsenic, cadmium, nickel, mercury, and polycyclic aromatic hydrocarbons in ambient air. Decree 155/2010 introduced stricter rules for identifying zones on the basis of the characteristics of the territory in spite of considering pollution levels, as it was in the past. The implementation of such new criteria has reduced the great variability of the previous zoning, leading to a significant reduction of the total number of zones and to a complete and uniform ambient air quality assessment and management throughout the Country. The present document is related to the new zones definition in Italy according to Decree 155/2010. In particular, the paper contains the description and the analysis of the outcome of zoning and classification.

Keywords: zones, agglomerations, air quality assessment, classification

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Authors: D. I. Yani, S. Isaramalai, C. Kritpracha

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Adherence to health behaviors is essential to achieve successful TB treatment. This study aimed to examine the effect of a family-based DOTS support program on adherence to health behaviors in patients with pulmonary TB. Sixty TB patients and their families were selected using cluster randomization of community health centers. The subjects were assigned into a control group, who received the routine care, and an experimental group, who received both routine care and care from the family-based DOTS support program. Paired t-test and the independent t-test were applied. The total score of adherence to health behaviors in the experimental group was significantly higher after receiving care from the family-based DOTS support program than the pretest score (t = -10.34, p < .001). Suggestions were made to expand the application of this program in various contexts and to extend knowledge for nursing practices and research.

Keywords: self-care deficit nursing theory, family-based DOTS program, pulmonary tuberculosis, adherence, health behaviors

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Authors: Almahd Alshereef, Ahmed Alkilany, Hammad Said, Azuraliza Abu Bakar

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From time to time, electrical power grid is directed by the National Electricity Operator to conduct load shedding, which involves hours' power outages on the area of this study, Southern Electrical Grid of Libya (SEGL). Load shedding is conducted in order to alleviate pressure on the National Electricity Grid at times of peak demand. This approach has chosen a set of categories to study load-shedding problem considering the effect of the demand priorities on the operation of the power system during emergencies. Classification of category region for load shedding problem is solved by a new algorithm (the harmony algorithm) based on the "random generation list of category region", which is a possible solution with a proximity degree to the optimum. The obtained results prove additional enhancements compared to other heuristic approaches. The case studies are carried out on SEGL.

Keywords: optimization, harmony algorithm, load shedding, classification

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Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

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Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder

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Authors: Hamzeh Awad

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Disability is considered to be a worldwide complex phenomenon which rising at a phenomenal rate and caused by many different factors. Chronic diseases such as cardiovascular disease and diabetes can lead to mobility disability in particular and disability in general. The ICF is an integrative bio-psycho-social model of functioning and disability and considered by the World Health Organization (WHO) to be a reference for disability classification using its categories and core set to classify disorder’s functional limitations. Specialist programs at Sultan Bin Abdul Aziz Humanitarian City (SBAHC) are providing both inpatient and outpatient services have started to implement the ICF and use it as a problem solving tool in Rehab. Diabetes is leading contributing factor for disability and considered epidemic in several Gulf countries including the Kingdom of Saudi Arabia (KSA), where its prevalence continues to increase dramatically. Metabolic disorders, mainly diabetes are not well covered in Rehab field. The purpose of this study is present to research and clinical rehabilitation field of DREAM and ICF as a framework in clinical and research setting in Rehab service. Also, shed the light on using the ICF as problem solving tool at SBAHC. There are synergies between disability causes and wider public health priorities in relation to both chronic disease and disability prevention. Therefore, there is a need for strong advocacy and understanding of the role of ICF as a reference in Rehab settings in Middle East if we wish to seize the opportunity to reverse current trends of acquired disability in the region.

Keywords: international classification of functioning, disability and health (ICF), prototype, rehabilitation and diabetes

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Authors: Daphne Alroy-Thiberge

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Trauma-impacted individuals present unique treatment challenges that include high reactivity, hyper-and hypo-arousal, poor adherence to therapy, as well as powerful transference and counter-transference experiences in therapy. This work provides an overview of the clinical tenets most often encountered in trauma-impacted individuals. Further, it provides readily applicable clinical techniques to optimize therapeutic rapport and facilitate accelerated positive mental health outcomes. Finally, integrated neuroscience and clinical evidence-based data are discussed to shed new light on crisis states in trauma-impacted individuals. This knowledge is utilized to provide effective and concrete interventions towards rapid and successful de-escalation of the impacted individual. A highly interactive, adult-learning-principles-based modality is utilized to provide an organic learning experience for participants. The information and techniques learned aim to increase clinical effectiveness, reduce staff injuries and burnout, and significantly enhance positive mental health outcomes and self-determination for the trauma-impacted individuals treated.

Keywords: clinical competencies, crisis interventions, psychotherapy techniques, trauma informed care

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Authors: Ariane Plaisance, Holly O. Witteman, Patrick Michel Archambault

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Upon admission to an intensive care unit (ICU), all patients should discuss their wishes concerning life-sustaining interventions (e.g., cardiopulmonary resuscitation (CPR)). Without such discussions, interventions that prolong life at the cost of decreasing its quality may be used without appropriate guidance from patients. We employed user-centered design to adapt an existing decision aid (DA) about CPR to create a novel wiki-based DA adapted to the context of a single ICU and tailored to individual patient’s risk factors. During Phase 1, we conducted three weeks of ethnography of the decision-making context in our ICU to identify clinician and patient needs for a decision aid. During this time, we observed five dyads of intensivists and patients discussing their wishes concerning life-sustaining interventions. We also conducted semi-structured interviews with the attending intensivists in this ICU. During Phase 2, we conducted three rounds of rapid prototyping involving 15 patients and 11 other allied health professionals. We recorded discussions between intensivists and patients and used a standardized observation grid to collect patients’ comments and sociodemographic data. We applied content analysis to field notes, verbatim transcripts and the completed observation grids. Each round of observations and rapid prototyping iteratively informed the design of the next prototype. We also used the programming architecture of a wiki platform to embed the GO-FAR prediction rule programming code that we linked to a risk graphics software to better illustrate outcome risks calculated. During Phase I, we identified the need to add a section in our DA concerning invasive mechanical ventilation in addition to CPR because both life-sustaining interventions were often discussed together by physicians. During Phase II, we produced a context-adapted decision aid about CPR and mechanical ventilation that includes a values clarification section, questions about the patient’s functional autonomy prior to admission to the ICU and the functional decline that they would judge acceptable upon hospital discharge, risks and benefits of CPR and invasive mechanical ventilation, population-level statistics about CPR, a synthesis section to help patients come to a final decision and an online calculator based on the GO-FAR prediction rule. Even though the three rounds of rapid prototyping led to simplifying the information in our DA, 60% (n= 3/5) of the patients involved in the last cycle still did not understand the purpose of the DA. We also identified gaps in the discussion and documentation of patients’ preferences concerning life-sustaining interventions (e.g.,. CPR, invasive mechanical ventilation). The final version of our DA and our online wiki-based GO-FAR risk calculator using the IconArray.com risk graphics software are available online at www.wikidecision.org and are ready to be adapted to other contexts. Our results inform producers of decision aids on the use of wikis and user-centered design to develop DAs that are better adapted to users’ needs. Further work is needed on the creation of a video version of our DA. Physicians will also need the training to use our DA and to develop shared decision-making skills about goals of care.

Keywords: ethnography, intensive care units, life-sustaining therapies, user-centered design

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Authors: Tsega Asresa Mengistu, Getahun Tigistu

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Computer vision is an artificial intelligence subfield that allows computers and systems to retrieve meaning from digital images. It is applied in various fields of study self-driving cars, video surveillance, agriculture, Quality control, Health care, construction, military, and everyday life. Aromatic and medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, and other natural health products for therapeutic and Aromatic culinary purposes. Herbal industries depend on these special plants. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs, and going to export not only industrial raw materials but also valuable foreign exchange. There is a lack of technologies for the classification and identification of Aromatic and medicinal plants in Ethiopia. The manual identification system of plants is a tedious, time-consuming, labor, and lengthy process. For farmers, industry personnel, academics, and pharmacists, it is still difficult to identify parts and usage of plants before ingredient extraction. In order to solve this problem, the researcher uses a deep learning approach for the efficient identification of aromatic and medicinal plants by using a convolutional neural network. The objective of the proposed study is to identify the aromatic and medicinal plant Parts and usages using computer vision technology. Therefore, this research initiated a model for the automatic classification of aromatic and medicinal plants by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides the root, flower and fruit, latex, and barks. The study was conducted on aromatic and medicinal plants available in the Ethiopian Institute of Agricultural Research center. An experimental research design is proposed for this study. This is conducted in Convolutional neural networks and Transfer learning. The Researcher employs sigmoid Activation as the last layer and Rectifier liner unit in the hidden layers. Finally, the researcher got a classification accuracy of 66.4 in convolutional neural networks and 67.3 in mobile networks, and 64 in the Visual Geometry Group.

Keywords: aromatic and medicinal plants, computer vision, deep convolutional neural network

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Authors: V. Pentland, S. Spilsbury, A. Biswas, M. F. Mottola, S. Paplinskie, M. S. Mitchell

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Postpartum depression (PPD) is a form of major depressive disorder that afflicts 10–22% of mothers worldwide. Rising demands for traditional PPD treatment options (e.g., psychiatry), especially in the context of the COVID-19 pandemic, are increasingly difficult to meet. More accessible treatment options (e.g., walking) are needed. The objective of this review is to determine the impact of walking on PPD severity. A structured search of seven electronic databases for randomised controlled trials published between 2000 and July 29, 2021, was completed. Studies were included if walking was the sole or primary aerobic exercise modality. A random-effects meta-analysis was conducted for studies reporting PPD symptoms measured using a clinically validated tool. A simple count of positive/null effect studies was undertaken as part of a narrative summary. Five studies involving 242 participants were included (mean age=~28.9 years; 100% with mild-to-moderate depression). Interventions were 12 (n=4) and 24 (n=1) weeks long. Each assessed PPD severity using the Edinburgh Postnatal Depression Scale (EPDS) and was included in the meta-analysis. The pooled effect estimate suggests that relative to controls, walking yielded clinically significant decreases in mean EPDS scores from baseline to intervention end (pooled MD=-4.01; 95% CI:-7.18 to -0.84, I2=86%). The narrative summary provides preliminary evidence that walking-only, supervised, and group-based interventions, including 90-120+ minutes/week of moderate-intensity walking, may produce greater EPDS reductions. While limited by a relatively small number of included studies, pooled effect estimates suggest walking may help mothers manage PPD. This is the first time walking as a treatment for PPD, an exercise modality that uniquely addresses many barriers faced by mothers has been summarized in a systematic way. Trial registration: PROSPERO (CRD42020197521) on August 16th, 2020

Keywords: postpartum, exercise, depression, walking

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Authors: Heichia Wang, Yalan Chao

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Social inequality is a persistent problem. One of the ways to solve this problem is through education. At present, vulnerable groups are often less geographically accessible to educational resources. However, compared with educational resources, communication equipment is easier for vulnerable groups. Now that information and communication technology (ICT) has entered the field of education, today we can accept the convenience that ICT provides in education, and the mobility that it brings makes learning independent of time and place. With mobile learning, teachers and students can start discussions in an online chat room without the limitations of time or place. However, because liquidity learning is quite convenient, people tend to solve problems in short online texts with lack of detailed information in a lack of convenient online environment to express ideas. Therefore, the ICT education environment may cause misunderstanding between teachers and students. Therefore, in order to better understand each other's views between teachers and students, this study aims to clarify the essays of the analysts and classify the students into several types of learning questions to clarify the views of teachers and students. In addition, this study attempts to extend the description of possible omissions in short texts by using external resources prior to classification. In short, by applying a short text classification, this study can point out each student's learning problems and inform the instructor where the main focus of the future course is, thus improving the ICT education environment. In order to achieve the goals, this research uses convolutional neural network (CNN) method to analyze short discussion content between teachers and students in an ICT education environment. Divide students into several main types of learning problem groups to facilitate answering student problems. In addition, this study will further cluster sub-categories of each major learning type to indicate specific problems for each student. Unlike most neural network programs, this study attempts to extend short texts with external resources before classifying them to improve classification performance. In short, by applying the classification of short texts, we can point out the learning problems of each student and inform the instructors where the main focus of future courses will improve the ICT education environment. The data of the empirical process will be used to pre-process the chat records between teachers and students and the course materials. An action system will be set up to compare the most similar parts of the teaching material with each student's chat history to improve future classification performance. Later, the function of short text classification uses CNN to classify rich chat records into several major learning problems based on theory-driven titles. By applying these modules, this research hopes to clarify the main learning problems of students and inform teachers that they should focus on future teaching.

Keywords: ICT education improvement, social equality, short text analysis, convolutional neural network

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Authors: Yi-Fung Lin, Shiow-Ching Shun, Wen-Yu Hu

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Background: A skill mix model is regarded as one of the most effective methods of reducing nursing shortages, as well as easing nursing staff workloads and labor costs. Although this model shows several benefits for the health workforce, the relationship between the optimal model of skill mix and the patient mortality rate remains to be discovered. Objectives: This review aimed to explore the relationship between the skill mix model and patient mortality rate in acute care hospitals. Data Sources: A systematic search of the PubMed, Web of Science, Embase, and Cochrane Library databases and researchers retrieved studies published between January 1986 and March 2022. Review methods: Two independent reviewers screened the titles and abstracts based on selection criteria, extracted the data, and performed critical appraisals using the STROBE checklist of each included study. The studies focused on adult patients in acute care hospitals, and the skill mix model and patient mortality rate were included in the analysis. Results: Six included studies were conducted in the USA, Canada, Italy, Taiwan, and European countries (Belgium, England, Finland, Ireland, Spain, and Switzerland), including patients in medical, surgical, and intensive care units. There were both nurses and nursing assistants in their skill mix team. This main finding is that three studies (324,592 participants) show evidence of fewer mortality rates associated with hospitals with a higher percentage of registered nurse staff (range percentage of registered nurse staff 36.1%-100%), but three articles (1,122,270 participants) did not find the same result (range of percentage of registered nurse staff 46%-96%). However, based on appraisal findings, those showing a significant association all meet good quality standards, but only one-third of their counterparts. Conclusions: In light of the limited amount and quality of published research in this review, it is prudent to treat the findings with caution. Although the evidence is not insufficient certainty to draw conclusions about the relationship between nurse staffing level and patients' mortality, this review lights the direction of relevant studies in the future. The limitation of this article is the variation in skill mix models among countries and institutions, making it impossible to do a meta-analysis to compare them further.

Keywords: nurse staffing level, nursing assistants, mortality, skill mix

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Authors: Lavanya Annadi, Fahimeh Jafari

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Transportation brings immense benefits to society, but it also has its costs. Costs include such as the cost of infrastructure, personnel and equipment, but also the loss of life and property in traffic accidents on the road, delays in travel due to traffic congestion and various indirect costs in terms of air transport. More research has been done to identify the various factors that affect road accidents, such as road infrastructure, traffic, sociodemographic characteristics, land use, and the environment. The aim of this research is to predict the probabilistic crash prediction of vehicles using machine learning due to natural and structural reasons by excluding spontaneous reasons like overspeeding etc., in the United States. These factors range from weather factors, like weather conditions, precipitation, visibility, wind speed, wind direction, temperature, pressure, and humidity to human made structures like road structure factors like bump, roundabout, no exit, turning loop, give away, etc. Probabilities are dissected into ten different classes. All the predictions are based on multiclass classification techniques, which are supervised learning. This study considers all crashes that happened in all states collected by the US government. To calculate the probability, multinomial expected value was used and assigned a classification label as the crash probability. We applied three different classification models, including multiclass Logistic Regression, Random Forest and XGBoost. The numerical results show that XGBoost achieved a 75.2% accuracy rate which indicates the part that is being played by natural and structural reasons for the crash. The paper has provided in-deep insights through exploratory data analysis.

Keywords: road safety, crash prediction, exploratory analysis, machine learning

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Authors: C. Vázquez, L. M. Gigirey, C. P. del Oro, S. Seoane

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Poor vision is common among older people, and several studies show connections between visual impairment and cognitive function. 15 older adult live in Galician Government nursing homes, and cognitive decline is one of the main reasons of admission. Objectives: (1) To evaluate functional far and near vision of older people with cognitive impairment. (2) To determine connections between visual and cognitive state of “our” residents. Methodology: A total of 364 older adults (aged 65 years or more) underwent a visual and cognitive screening. We tested presenting visual acuity (binocular visual acuity with habitual correction if warn) for distance and near vision (E-Snellen, usual working distance for near vision). Binocular presenting visual acuity less than 0.3 was used as cut point for diagnosis of visual impairment. Exclusion criteria included immobilized residents unable to reach the USC Dual Sensory Loss Unit for visual screening. To screen cognition we employed the mini-mental examination test (Spanish version). Analysis of categorical variables was performed using chi-square tests. We utilized Pearson and Spearman correlation tests and the variance analysis to determine differences between groups of interest (SPSS 19.0 version). Results: the percentage of residents with cognitive decline reaches 32.2% Prevalence of visual impairment for distance and near vision increases among those subjects with cognitive impairment respect those with normal cognition. Shift correlation exists between distance visual acuity and mini-mental test (age and sex controlled), and moderate association was found in case of near vision (p<0.01). Conclusion: First results shows that people with cognitive impairment have poor functional distance and near vision than those with normal cognition. Next step will be to analyse the individual contribution of distance and near vision loss on cognition.

Keywords: visual impairment, cognition, aging, nursing homes

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Authors: Jiraporn Lao-Un, Dararat Khampusaen

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Lack of exposure to English language in the authentic English setting naturally leads to a lack of fluency in the language. As a result, Thai EFL learners are struggling in meeting with the communication 'can do' descriptors of the Common European Framework of References (CEFR) required by the Ministry of Education. This initial phase of the ongoing study, employing the e-portfolio to promote the English speaking ability, probed into the effects of the use of e-portfolio on Thai EFL nursing students' speaking ability. Also, their opinions towards the use of e-portfolio to enhance their speaking ability were investigated. The participants were 44 undergraduate nursing students at a Thai College of Nursing. The participants undertook four lessons to promote their communication skills according to the CEFR criteria. Throughout the semester, the participants videotaped themselves while completing the four speaking tasks. The videos were then uploaded onto the e-portfolio website where the researcher provided them with the feedbacks. The video records were analyzed by the speaking rubric designed according to the CEFR 'can do' descriptors. Also, students were required to record self-reflections in video format and upload onto the same URL Students' oral self-reflections were coded to find out the perceptions towards the use of the e-portfolio in promoting their speaking ability. The results from the two research instruments suggested the effectiveness of the tool on improving speaking ability, learner autonomy and media literacy skills. In addition, the oral reflection videos revealed positive opinion towards the tool. The discussion offers the current status of English speaking ability among Thai EFL students. This reveals the gaps between the EFL speaking ability and the CEFR ‘can do’ descriptors. In addition, the author raises the light on integration of the 21st century IT tool to enhance these students’ speaking ability. Lastly, the theoretical implications and recommendation for further study in integrating electronic tools to promote language skills in the EFL context are offered for further research.

Keywords: EFL communication, EFL speaking, English communication, E-learning, E-portfolio, speaking ability, Thai EFL learners

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Authors: Raeda F. Abualrub, Amani Abdulnabi

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Aim: The aims of this study were to (a) examine the levels of nurses' political efficacy and involvement in health policy; and (b) explore the relationships between political efficacy, involvement in health policy, and participants’ background variables. Background: Nurses as citizens and health care providers have the right to express their opinions and beliefs in regard to issues that are concerned with the health care system or the public health domain. Methods: A descriptive, cross-sectional design using was utilized. A self-administered questionnaire (Political Efficacy Scale & Involvement in Health Policy Scale) was completed by a convenience sample of 302 nurses. Results: The results of this study showed low levels of involvement in health policy and political efficacy and a positive weak correlation between political efficacy and involvement in health policy. The perceived level of political efficacy was associated positively with nurses’ age and experience. Conclusions: Nurse administrators and managers may empower, support, and encourage nurses to enhance their involvement in health policy. Implications for Nursing Management: The findings have implications for nursing leaders and administrators to design appropriate strategies to enhance nurses’ involvement in health policy development.

Keywords: health policy, Jordan, nurses, political efficacy

Procedia PDF Downloads 75