Search results for: health belief model.

Authors: Hilal Al Shamsi, Abdullah Almutairi

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Introduction: As primary healthcare centres play an important role in implementing Saudi Arabia’s health strategy, this paper offers a review of publications on the quality of the country’s primary health care. With the aim of deciding on solutions for improvement, it provides an overview of healthcare quality in this context and indicates barriers to quality. Method: Using two databases, ProQuest and Scopus, data extracted from published articles were systematically analysed for determining the care quality in Saudi primary health centres and obstacles to achieving higher quality. Results: Twenty-six articles met the criteria for inclusion in this review. The components of healthcare quality were examined in terms of the access to and effectiveness of interpersonal and clinical care. Good access and effective care were identified in such areas as maternal health care and the control of epidemic diseases, whereas poor access and effectiveness of care were shown for chronic disease management programmes, referral patterns (in terms of referral letters and feedback reports), health education and interpersonal care (in terms of language barriers). Several factors were identified as barriers to high-quality care. These included problems with evidence-based practice implementation, professional development, the use of referrals to secondary care and organisational culture. Successful improvements have been implemented by other systems, such as mobile medical units, electronic referrals, online translation tools and mobile devices and their applications; these can be implemented in Saudi Arabia for improving the quality of the primary healthcare system in this country. Conclusion: The quality of primary health care in Saudi Arabia varies among the different services. To improve quality, management programmes and organisational culture must be promoted in primary health care. Professional development strategies are also needed for improving the skills and knowledge of healthcare professionals. Potential improvements can be implemented to improve the quality of the primary health system.

Keywords: quality, primary health care, Saudi Arabia, health centres, general medical

Procedia PDF Downloads 193

Authors: S. K. Ashiquer Rahman

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Recently, oil has become more influential in almost every economic sector as a key material. As can be seen from the news, when there are some changes in an oil price or OPEC announces a new strategy, its effect spreads to every part of the economy directly and indirectly. That’s a reason why people always observe the oil price and try to forecast the changes of it. The most important factor affecting the price is its supply which is determined by the number of wildcats drilled. Therefore, a study about the number of wellheads and other economic variables may give us some understanding of the mechanism indicated by the amount of oil supplies. In this paper, we will consider a relationship between the number of wellheads and three key factors: the price of the wellhead, domestic output, and GNP constant dollars. We also add trend variables in the models because the consumption of oil varies from time to time. Moreover, this paper will use an econometrics method to estimate parameters in the model, apply some tests to verify the result we acquire, and then conclude the model.

Keywords: price, domestic output, GNP, trend variable, wildcat activity

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Authors: Elisabete Alberdi, Irantzu Álvarez, Nerea Astigarraga, Heber Hernández

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The purpose of this research work is to study the emissions of certain substances that contribute to air pollution and, as far as possible, to try to eliminate or reduce them, to avoid damage to both health and the environment. This work focuses on analyzing and comparing air quality in 2019 and 2020 in the Autonomous Community of the Basque Country, especially near the UPV/EHU campuses. We use Geostatistics to develop a spatial model and to analyse the levels of pollutants in those areas where the scope of the monitoring stations is limited. Finally, different more sustainable transport alternatives for users have been proposed.

Keywords: air quality, pollutants, monitoring stations, environment, geostatistics

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Authors: A. Dėdelė, A. Miškinytė

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The assessment of air pollution exposure in different microenvironments is important for better understanding the relationship between health effects caused by air pollution. The recent researches revealed that the level of air pollution in transport microenvironment contributes considerably to the total exposure of air pollution. The aim of the study was to determine air pollution of nitrogen dioxide and to assess the exposure of NO2 dependence on the chosen commuting mode using a global positioning system (GPS). The same travel destination was chosen and 30 rides in three different commuting modes: cycling, walking, and public transport were made. Every different mean of transport is associated with different route. GPS device and travel diary data were used to track all routes of different commuting modes. Air pollution of nitrogen dioxide was determined using the ADMS-Urban dispersion model. The average annual concentration of nitrogen dioxide was modeled for 2011 year in Kaunas city. The geographical information systems were used to visualize the travel routes, to create maps indicating the route of different commuting modes and to combine modelled nitrogen dioxide data. The results showed that there is a significant difference between the selected commuting mode and the exposure of nitrogen dioxide. The concentrations in the microenvironments were 22.4 μg/m3, 21.4 μg/m3, and 25.9 μg/m3 for cycling, walking and public transport respectively. Of all the modes of commuting, the highest average exposure of nitrogen dioxide was found travelling by public transport, while the lowest average concentration of NO2 was determined by walking.

Keywords: nitrogen dioxide, dispersion model, commuting mode, GPS

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Authors: Yi-Fung Lin, Yuan-Mei Liao

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Background: Pain is common among patients with cancer and is also one of the most disturbing symptoms. As this suffering is subjective, if patients proactively participate in their pain self-management, pain could be alleviated effectively. However, not everyone can carry out self-management very well because human behavior is a product of the cognition process. In this process, we can see that "self-efficacy" plays an essential role in affecting human behaviors. Methods: We used the eight steps of concept analysis proposed by Walker and Avant to clarify the concept of “self-efficacy for cancer pain management.” A comprehensive literature review was conducted for relevant publications that were published during the period of 1977 to 2021. We used several keywords, including self-efficacy, self-management, concept analysis, conceptual framework, and cancer pain, to search the following databases: PubMed, CINAHL, Web of Science, and Embase. Results: We identified three defining attributes for the concept of self-efficacy for cancer pain management, including pain management abilities, confidence, and continuous pain monitoring, and recognized six skills related to pain management abilities: problem-solving, decision-making, resource utilization, forming partnerships between medical professionals and patients, planning actions, and self-regulation. Five antecedents for the concept of self-efficacy for cancer pain management were identified: pain experience, existing cancer pain, pain-related knowledge, a belief in pain management, and physical/mental state. Consequences related to self-efficacy for cancer pain management were achievement of pain self-management, well pain control, satisfying quality of life, and containing motivation. Conclusions: This analysis provides researchers with a clearer understanding of the concept of “self-efficacy for cancer pain management.” The findings presented here provide a foundation for future research and nursing interventions to enhance self-efficacy for cancer pain management.

Keywords: cancer pain, concept analysis, self-efficacy, self-management

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Authors: Deinzel R. Uezono, Vivien Fe F. Fadrilan-Camacho, Bianca Margarita L. Medina, Antonio Domingo R. Reario, Trisha M. Salcedo, Luke Wesley P. Borromeo

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The environment of health care workers is considered one of the most hazardous settings due to the nature of their work. In developing countries especially, the Philippines, this continues to be overlooked in terms of programs and services on occupational health and safety (OHS). One possible reason for this is the existing information gap on OHS which limits data comparability and impairs effective monitoring and assessment of interventions. To address this gap, there is a need to determine the current conditions of Filipino health care workers in their workplace. This descriptive cross-sectional study assessed the occupational health and safety conditions of health care workers in barangay health centers in a selected city in Metro Manila, Philippines by: (1) determining the hazards present in the workplace; (2) determining the most common self-reported medical problems; and (3) describing the elements of an OHS system based on the six building blocks of health system. Assessment was done through walkthrough survey, self-administered questionnaire, and key informant interview. Data analysis was done using Epi Info 7 and NVivo 11. Results revealed different health hazards present in the workplace particularly biological hazards (exposure to sick patients and infectious specimens), physical hazards (inadequate space and/or lighting), chemical hazards (toxic reagents and flammable chemicals), and ergonomic hazards (activities requiring repetitive motion and awkward posture). Additionally, safety hazards (improper capping of syringe and lack of fire safety provisions) were also observed. Meanwhile, the most commonly self-reported chronic diseases among health care workers (N=336) were hypertension (20.24%, n=68) and diabetes (12.50%, n=42). Top commonly self-reported symptoms were colds (66.07%, n=222), coughs (63.10%, n=212), headache (55.65%, n=187), and muscle pain (50.60%, n=170) while other diseases were influenza (16.96%, n=57) and UTI (15.48%, n=52). In terms of the elements of the OHS system, a general policy on occupational health and safety was found to be lacking and in effect, an absence of health and safety committee overseeing the implementing and monitoring of the policy. No separate budget specific for OHS programs and services was also found to be a limitation. As a result, no OHS personnel and trainings/seminar were identified. No established information system for OHS was in place. In conclusion, health and safety hazards were observed to be present across the barangay health centers visited in a selected city in Metro Manila. Medical conditions identified as most commonly self-reported were hypertension and diabetes for chronic diseases; colds, coughs, headache, and muscle pain for medical symptoms; and influenza and UTI for other diseases. As for the elements of the occupational health and safety system, there was a lack in the general components of the six building blocks of the health system.

Keywords: health hazards, occupational health and safety, occupational health and safety system, safety hazards

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Authors: Tina Wu, Andrew Martin

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The expansion of the Internet of Things (IoT) brings a new level of threat. Attacks on IoT are already being used by criminals to form botnets, launch Distributed Denial of Service (DDoS) and distribute malware. This opens a whole new digital forensic arena to develop forensic methodologies in order to have the capability to investigate IoT related crimes. However, existing proposed IoT forensic models are still premature requiring further improvement and validation, many lack details on the acquisition and analysis phase. This paper proposes an enhanced theoretical IoT digital forensic model focused on identifying and acquiring the main sources of evidence in a methodical way. In addition, this paper presents a theoretical acquisition framework of the different stages required in order to be capable of acquiring evidence from IoT devices.

Keywords: acquisition, Internet of Things, model, zoning

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Authors: Julio Franco

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Water is used, directly and indirectly, in all activities of the construction productive chain, making it a subject of worldwide relevance for sustainable development. The ongoing expansion of urban areas leads to a high demand for natural resources, which in turn cause significant environmental impacts. The present work proposes the application of BIM tools to assist the measurement of the water footprint (WF) of civil construction supplies. Data was inserted into the model as element properties, allowing them to be analyzed by element or in the whole model. The WF calculation was automated using parameterization in Autodesk Revit software. Parameterization was associated to the materials of each element in the model so that any changes in these elements directly alter the results of WF calculations. As a case study, we applied into a building project model to test the parameterized calculus of WF. Results show that the proposed parameterization successfully automated WF calculations according to design changes. We envision this tool to assist the measurement and rationalization of the environmental impact in terms of WF of construction projects.

Keywords: building information modeling, BIM, sustainable development, water footprint

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Authors: M. Duk, L. Grabowski, P. Magryta

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Today's very important element relating to air transport is the environment impact issues. Nowadays there are no emissions standards for turbine and piston engines used in air transport. However, it should be noticed that the environmental effect in the form of exhaust gases from aircraft engines should be as small as possible. For this purpose, R&D centers often use special software to simulate and to estimate the negative effect of engine working process. For cooperation between the Lublin University of Technology and the Polish aviation company WSK "PZL-KALISZ" S.A., to achieve more effective operation of the ASz62IR engine, one of such tools have been used. The AVL Boost software allows to perform 1D simulations of combustion process of piston engines. ASz62IR is a nine-cylinder aircraft engine in a radial configuration. In order to analyze the impact of its working process on the environment, the mathematical model in the AVL Boost software have been made. This model contains, among others, model of the operation cycle of the cylinders. This model was based on a volume change in combustion chamber according to the reciprocating movement of a piston. The simplifications that all of the pistons move identically was assumed. The changes in cylinder volume during an operating cycle were specified. Those changes were important to determine the energy balance of a cylinder in an internal combustion engine which is fundamental for a model of the operating cycle. The calculations for cylinder thermodynamic state were based on the first law of thermodynamics. The change in the mass in the cylinder was calculated from the sum of inflowing and outflowing masses including: cylinder internal energy, heat from the fuel, heat losses, mass in cylinder, cylinder pressure and volume, blowdown enthalpy, evaporation heat etc. The model assumed that the amount of heat released in combustion process was calculated from the pace of combustion, using Vibe model. For gas exchange, it was also important to consider heat transfer in inlet and outlet channels because of much higher values there than for flow in a straight pipe. This results from high values of heat exchange coefficients and temperature coefficients near valves and valve seats. A Zapf modified model of heat exchange was used. To use the model with the flight scenarios, the impact of flight altitude on engine performance has been analyze. It was assumed that the pressure and temperature at the inlet and outlet correspond to the values resulting from the model for International Standard Atmosphere (ISA). Comparing this model of operation cycle with the others submodels of the ASz62IR engine, it could be noticed, that a full analysis of the performance of the engine, according to the ISA conditions, can be made. This work has been financed by the Polish National Centre for Research and Development, INNOLOT, under

Keywords: aviation propulsion, AVL Boost, engine model, operation cycle, aircraft engine

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Authors: Abdmohammad Mousavi, Mohsen Shams, Mehdi Akbartabar Toori, Ali Mousavizadeh, Mohammad Ali Morowatisharifabad

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Introduction: The effectiveness of Transtheoretical Model constructs on dietary behavior change has been subject to questions by some studies. The objective of this study was to determine the relationship between self–efficacy and decisional balance as mediator variables and transfer obese and overweight women among the stages of behavior change of calorie balanced dietary intake. Method: In this cross-sectional study, 448 obese and overweight 20-44 years old women were selected from three health centers in Yasuj, a city in south west of Iran. Anthropometric data were measured using standard techniques. Demographic, stages of change, self-efficacy and decisional balance data were collected by questionnaires and analyzed using One–Way ANOVA and Generalized Linear Models tests. Results: Demographic and anthropometric variables were not different significantly in different stages of change related to calorie intake except the pre-high school level of education (P=.047, OR=502, 95% CI= .255 ~ .990). Mean scores of Self-efficacy ( F(4.425)= 27.09, P= .000), decisional balance (F(4.394), P= .004), and pros (F(4.430)=5.33, P=000) were different significantly in five stages of change. However, the cons did not show a significant change in this regard (F(4.400)=1.83, P=.123). Discussion: Women movement through the stages of changes for calorie intake behavior can be predicted by self efficacy, decisional balance and pros.

Keywords: transtheoretical model, stages of change, self efficacy, decisional balance, calorie intake, women

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Authors: M. R. Ghasemi, R. Ghiasi, H. Varaee

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Model updating method has received increasing attention in damage detection structures based on measured modal parameters. Therefore, a probability-based damage detection (PBDD) procedure based on a model updating procedure is presented in this paper, in which a one-stage model-based damage identification technique based on the dynamic features of a structure is investigated. The presented framework uses a finite element updating method with a Monte Carlo simulation that considers the uncertainty caused by measurement noise. Enhanced ideal gas molecular movement (EIGMM) is used as the main algorithm for model updating. Ideal gas molecular movement (IGMM) is a multiagent algorithm based on the ideal gas molecular movement. Ideal gas molecules disperse rapidly in different directions and cover all the space inside. This is embedded in the high speed of molecules, collisions between them and with the surrounding barriers. In IGMM algorithm to accomplish the optimal solutions, the initial population of gas molecules is randomly generated and the governing equations related to the velocity of gas molecules and collisions between those are utilized. In this paper, an enhanced version of IGMM, which removes unchanged variables after specified iterations, is developed. The proposed method is implemented on two numerical examples in the field of structural damage detection. The results show that the proposed method can perform well and competitive in PBDD of structures.

Keywords: enhanced ideal gas molecular movement (EIGMM), ideal gas molecular movement (IGMM), model updating method, probability-based damage detection (PBDD), uncertainty quantification

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Authors: Mustapha Babatunde, Bassam Tawabini, Ole John Nielson

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Air dispersion (AD) models such as AERMOD are important tools for estimating the environmental impacts of air pollutant emissions into the atmosphere from anthropogenic sources. The outcome of these models is significantly linked to the climate condition like air temperature, which is expected to differ in the future due to the global warming phenomenon. With projections from scientific sources of impending changes to the future climate of Saudi Arabia, especially anticipated temperature rise, there is a potential direct impact on the dispersion patterns of air pollutants results from AD models. To our knowledge, no similar studies were carried out in Saudi Arabia to investigate such impact. Therefore, this research investigates the effects of climate temperature change on air quality in the Dammam Metropolitan area, Saudi Arabia, using AERMOD coupled with Station data using Sulphur dioxide (SO2) – as a model air pollutant. The research uses AERMOD model to predict the SO2 dispersion trends on the surrounding area. Emissions from five (5) industrial stacks, on twenty-eight (28) receptors in the study area were considered for the climate period (2010-2019) and future period of mid-century (2040-2060) under different scenarios of elevated temperature profiles (+1oC, + 3oC and + 5oC) across averaging time periods of 1hr, 4hr and 8hr. Results showed that levels of SO2 at the receiving sites under current and simulated future climactic condition fall within the allowable limit of WHO and KSA air quality standards. Results also revealed that the projected rise in temperature would only have mild increment on the SO2 concentration levels. The average increase of SO2 levels were 0.04%, 0.14%, and 0.23% due to the temperature increase of 1, 3, and 5 degrees respectively. In conclusion, the outcome of this work elucidates the degree of the effects of global warming and climate changes phenomena on air quality and can help the policymakers in their decision-making, given the significant health challenges associated with ambient air pollution in Saudi Arabia.

Keywords: air quality, sulphur dioxide, global warming, air dispersion model

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Authors: R. Nandhini, Gaurab Mudbhari

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Ranging from the field of health care to self-driving cars, machine learning and deep learning algorithms have revolutionized the field with the proper utilization of images and visual-oriented data. Segmentation, regression, classification, clustering, dimensionality reduction, etc., are some of the Machine Learning tasks that helped Machine Learning and Deep Learning models to become state-of-the-art models for the field where images are key datasets. Among these tasks, classification and clustering are essential but difficult because of the intricate and high-dimensional characteristics of image data. This finding examines and assesses advanced techniques in supervised classification and unsupervised clustering for image datasets, emphasizing the relative efficiency of Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Deep Embedded Clustering (DEC), and self-supervised learning approaches. Due to the distinctive structural attributes present in images, conventional methods often fail to effectively capture spatial patterns, resulting in the development of models that utilize more advanced architectures and attention mechanisms. In image classification, we investigated both CNNs and ViTs. One of the most promising models, which is very much known for its ability to detect spatial hierarchies, is CNN, and it serves as a core model in our study. On the other hand, ViT is another model that also serves as a core model, reflecting a modern classification method that uses a self-attention mechanism which makes them more robust as this self-attention mechanism allows them to lean global dependencies in images without relying on convolutional layers. This paper evaluates the performance of these two architectures based on accuracy, precision, recall, and F1-score across different image datasets, analyzing their appropriateness for various categories of images. In the domain of clustering, we assess DEC, Variational Autoencoders (VAEs), and conventional clustering techniques like k-means, which are used on embeddings derived from CNN models. DEC, a prominent model in the field of clustering, has gained the attention of many ML engineers because of its ability to combine feature learning and clustering into a single framework and its main goal is to improve clustering quality through better feature representation. VAEs, on the other hand, are pretty well known for using latent embeddings for grouping similar images without requiring for prior label by utilizing the probabilistic clustering method.

Keywords: machine learning, deep learning, image classification, image clustering

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Authors: Margarida Porfírio Tomás, Paula Pereira, José Palma Oliveira

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Robotic grasping is advancing rapidly, but transferring techniques from rigid to deformable objects remains a challenge. Deformable and flexible items, such as food containers, demand nuanced handling due to their changing shapes. Bridging this gap is crucial for applications in food processing, surgical robotics, and household assistance. AGILEHAND, a Horizon project, focuses on developing advanced technologies for sorting, handling, and packaging soft and deformable products autonomously. These technologies serve as strategic tools to enhance flexibility, agility, and reconfigurability within the production and logistics systems of European manufacturing companies. Key components include intelligent detection, self-adaptive handling, efficient sorting, and agile, rapid reconfiguration. The overarching goal is to optimize work environments and equipment, ensuring both efficiency and safety. As new technologies emerge in the food industry, there will be some implications, such as labour force, safety problems and acceptance of the new technologies. To overcome these implications, AGILEHAND emphasizes the integration of social sciences and humanities, for example, the application of the Technology Acceptance Model (TAM). The project aims to create a change management manual, that will outline strategies for developing digital skills and managing health and safety transitions. It will also provide best practices and models for organizational change. Additionally, AGILEHAND will design effective training programs to enhance employee skills and knowledge. This information will be obtained through a combination of case studies, structured interviews, questionnaires, and a comprehensive literature review. The project will explore how organizations adapt during periods of change and identify factors influencing employee motivation and job satisfaction. This project received funding from European Union’s Horizon 2020/Horizon Europe research and innovation program under grant agreement No101092043 (AGILEHAND).

Keywords: change management, technology acceptance model, organizational change, health and safety

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Authors: M. Roberts

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We Are Undefeatable - is a mass marketing behaviour change campaign that aims to support the least active people living with long term health conditions to be more active. This is an important issue to address because people with long term conditions are an historically underserved community for the sport and physical activity sector and the least active of those with long term conditions have the most to gain in health and wellbeing benefits. The campaign has generated a significant change in the way physical activity is communicated and people with long term conditions are represented in the media and marketing. The goal is to create a social norm around being active. The campaign is led by a unique partnership of organisations: the Richmond Group of Charities (made up of Age UK, Alzheimer’s Society, Asthma + Lung UK, Breast Cancer Now, British Heart Foundation, British Red Cross, Diabetes UK, Macmillan Cancer Support, Rethink Mental Illness, Royal Voluntary Service, Stroke Association, Versus Arthritis) along with Mind, MS Society, Parkinson’s UK and Sport England, with National Lottery Funding. It is underpinned by the COM-B model of behaviour change. It draws on the lived experience of people with multiple long term conditions to shape the look and feel of the campaign and all the resources available. People with long term conditions are the campaign messengers, central to the ethos of the campaign by telling their individual stories of overcoming barriers to be active with their health conditions. The central messaging is about finding a way to be active that works for the individual. We Are Undefeatable is evaluated through a multi-modal approach, including regular qualitative focus groups and a quantitative evaluation tracker undertaken three times a year. The campaign has highlighted the significant barriers to physical activity for people with long term conditions. This has changed the way our partnership talks about physical activity but has also had an impact on the wider sport and physical activity sector, prompting an increasing departure from traditional messaging and marketing approaches for this audience of people with long term conditions. The campaign has reached millions of people since its launch in 2019, through multiple marketing and partnership channels including primetime TV advertising and promotion through health professionals and in health settings. Its diverse storytellers make it relatable to its target audience and the achievable activities highlighted and inclusive messaging inspire our audience to take action as a result of seeing the campaign. The We Are Undefeatable campaign is a blueprint for physical activity campaigns; it not only addresses individual behaviour change but plays a role in addressing systemic barriers to physical activity by sharing the lived experience insight to shape policy and professional practice.

Keywords: behaviour change, long term conditions, partnership, relatable

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Authors: Kien-Trinh Thi Bui, Cuong Manh Nguyen

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In order to simulate and reproduce the operational characteristics of a dam visually, it is necessary to capture the displacement at different measurement points and analyze the observed movement data promptly to forecast the dam safety. The accuracy of forecasts is further improved by applying machine learning methods to data analysis progress. In this study, the horizontal displacement monitoring data of the Ialy hydroelectric dam was applied to machine learning algorithms: Gaussian processes, multi-layer perceptron neural networks, and the M5-rules algorithm for modelling and forecasting of horizontal displacement of the Ialy hydropower dam (Vietnam), respectively, for analysing. The database which used in this research was built by collecting time series of data from 2006 to 2021 and divided into two parts: training dataset and validating dataset. The final results show all three algorithms have high performance for both training and model validation, but the MLPs is the best model. The usability of them are further investigated by comparison with a benchmark models created by multi-linear regression. The result show the performance which obtained from all the GP model, the MLPs model and the M5-Rules model are much better, therefore these three models should be used to analyze and predict the horizontal displacement of the dam.

Keywords: Gaussian processes, horizontal displacement, hydropower dam, Ialy dam, M5-Rules, multi-layer perception neural networks

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Authors: Jaehwan Jung, Sung-Ah Kim

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Building Information Modeling (BIM) provides project stakeholders with various information about property and geometry of entire component as a 3D object-based parametric building model. BIM represents a set of Information and solutions that are expected to improve collaborative work process and quality of the building design. To improve collaboration among project participants, the BIM model should provide the necessary information to remote participants in real time and manage the information in the process. The purpose of this paper is to propose a process model that can apply effective architectural design collaborative work process in architectural design education in BIM-Cloud environment.

Keywords: BIM, cloud computing, collaborative design, digital design education

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Authors: Aqeel Zeid, Hasna Anees, Mohamed Adheeb, Mohamed Rifan, Kalpani Manathunga

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To achieve constructive alignment in a higher education program, a clear set of learning outcomes must be defined. Traditional learning outcome definition techniques such as Bloom’s taxonomy are not written to be utilized by the student. This might be disadvantageous for students in student-centric learning settings where the students are expected to formulate their own learning strategies. To solve the problem, we propose the learning outcome relation and aggregation (LORA) model. To achieve alignment, we developed learning outcome, assessment, and resource authoring tools which help teachers to tag learning outcomes during creation. A pilot study was conducted with an expert panel consisting of experienced professionals in the education domain to evaluate whether the LORA model and tools present an improvement over the traditional methods. The panel unanimously agreed that the model and tools are beneficial and effective. Moreover, it helped them model learning outcomes in a more student centric and descriptive way.

Keywords: learning design, constructive alignment, Bloom’s taxonomy, learning outcome modelling

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Authors: Sunghoon Park, Hank Kim, Jinwon An, Sungzoon Cho

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Internet of Things allows one to collect data from facilities which are then used to monitor them and even predict malfunctions in advance. Conventional quality control methods focus on setting a normal range on a sensor value defined between a lower control limit and an upper control limit, and declaring as an anomaly anything falling outside it. However, interactions among sensor values are ignored, thus leading to suboptimal performance. We propose a multivariate approach which takes into account many sensor values at the same time. In particular Gaussian Mixture Model is used which is trained to maximize likelihood value using Expectation-Maximization algorithm. The number of Gaussian component distributions is determined by Bayesian Information Criterion. The negative Log likelihood value is used as an anomaly score. The actual usage scenario goes like a following. For each instance of sensor values from a facility, an anomaly score is computed. If it is larger than a threshold, an alarm will go off and a human expert intervenes and checks the system. A real world data from Building energy system was used to test the model.

Keywords: facility anomaly detection, gaussian mixture model, anomaly score, expectation maximization algorithm

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Authors: Waleed A. Basuliman, Khalid S. AlSaleh, Mohamed Z. Ramadan

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Applying the anthropometric dimensions is considered one of the important factors when designing any human-machine system. In this study, the estimation of anthropometric dimensions has been improved by developing artificial neural network that aims to predict the anthropometric measurements of the male in Saudi Arabia. A total of 1427 Saudi males from age 6 to 60 participated in measuring twenty anthropometric dimensions. These anthropometric measurements are important for designing the majority of work and life applications in Saudi Arabia. The data were collected during 8 months from different locations in Riyadh City. Five of these dimensions were used as predictors variables (inputs) of the model, and the remaining fifteen dimensions were set to be the measured variables (outcomes). The hidden layers have been varied during the structuring stage, and the best performance was achieved with the network structure 6-25-15. The results showed that the developed Neural Network model was significantly able to predict the body dimensions for the population of Saudi Arabia. The network mean absolute percentage error (MAPE) and the root mean squared error (RMSE) were found 0.0348 and 3.225 respectively. The accuracy of the developed neural network was evaluated by compare the predicted outcomes with a multiple regression model. The ANN model performed better and resulted excellent correlation coefficients between the predicted and actual dimensions.

Keywords: artificial neural network, anthropometric measurements, backpropagation, real anthropometric database

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Authors: P.-W. Tsai, C.-Y. Chen, C.-W. Chen

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In this paper, the stability analysis of a GA-Based adaptive fuzzy sliding model controller for a nonlinear system is discussed. First, a nonlinear plant is well-approximated and described with a reference model and a fuzzy model, both involving FLC rules. Then, FLC rules and the consequent parameter are decided on via an Evolved Bat Algorithm (EBA). After this, we guarantee a new tracking performance inequality for the control system. The tracking problem is characterized to solve an eigenvalue problem (EVP). Next, an adaptive fuzzy sliding model controller (AFSMC) is proposed to stabilize the system so as to achieve good control performance. Lyapunov’s direct method can be used to ensure the stability of the nonlinear system. It is shown that the stability analysis can reduce nonlinear systems into a linear matrix inequality (LMI) problem. Finally, a numerical simulation is provided to demonstrate the control methodology.

Keywords: adaptive fuzzy sliding mode control, Lyapunov direct method, swarm intelligence, evolved bat algorithm

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Authors: Maha Almumaiz, Harry Evdorides

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Change in land value typically occurs when a new interurban road construction causes an increase in accessibility; this change in the adjacent lands values differs according to land characteristics such as geographic location, land use type, land area and sale time (appraisal time). A multiple regression model is obtained to predict the percent change in land value (CLV) based on four independent variables namely land distance from the constructed road, area of land, nature of land use and time from the works completion of the road. The random values of percent change in land value were generated using Microsoft Excel with a range of up to 35%. The trend of change in land value with the four independent variables was determined from the literature references. The statistical analysis and model building process has been made by using the IBM SPSS V23 software. The Regression model suggests, for lands that are located within 3 miles as the straight distance from the road, the percent CLV is between (0-35%) which is depending on many factors including distance from the constructed road, land use, land area and time from works completion of the new road.

Keywords: interurban road, land use types, new road construction, percent CLV, regression model

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Authors: Haga Abdelrahman Elimam

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This paper investigates the connotation, and some of the realistic implications, of the economic reform of health sector in under developed countries. The paper investigates the issues that economic reforms have to address, and the policy targets they are considered to accomplish. The work argues that the development of economic reform is not connected only with understanding the priorities and refining them, furthermore with reformation and restructuring the organizations through which health policies are employed. Considering various organizational values, that are likely to be regular to all economic reform programs, a regulatory approach to institutional reform is unsuitable. The paper further investigates the selection of economic reform that may as well influence via technical suggestions and analysis, but the verdict to continue, and the consequent success of execution, eventually depends on the progressive political sustainability. The paper concludes by giving examples of institutional reforms from various underdeveloped countries and includes recommendation of the responsibility and control of donor organizations.

Keywords: economic reform, health sector, underdeveloped countries, technical suggestions

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Authors: Priscilla Deede Hammond

Abstract:

Healthcare seeking behaviour has emerged as a tool to tackle perceived ill-health by taking remedial actions. And currently, efforts are being directed towards encouraging people (especially parents) to learn and use health-promoting behaviours in seeking their children’s healthcare. Regardless of these efforts, most parents encounter challenges with raising a child with a disability. The purpose of the study was to explore the healthcare-seeking behaviours of parents of children with disabilities. In order to achieve the purpose of the study, a case study design was employed where the researcher used a qualitative approach such as semi-structured interview to gather the required data. Data from participants were analysed using a thematic analysis approach. It was revealed from the findings of the study that, some of the parents after the first diagnosis by health professionals consulted a spiritualist or a herbalist for help. Also, some parents stated that their response to their children’s healthcare depended on the severity of the sickness. The study recommends the Ministry of Gender, Children and Social Protection and other social agencies such as the Social Welfare Department to provide health assessment and financial support to families of children with disabilities.

Keywords: healthcare, health, parents, disabilities

Procedia PDF Downloads 224

Authors: Denis Özdemir, Max Senges

Abstract:

There are many examples of AI-supported apps for better health and wellbeing. Generally, these applications help people to achieve their goals based on scientific research and input data. Still, they do not always explain how those three are related, e.g. by making implicit assumptions about goals that hold for many but not for all. We present a seven-step method for designing health and wellbeing AIs considering goal setting, measurable results, real-time indicators, analytics, visual representations, communication, and feedback. It can help engineers as guidance in developing apps, recommendation algorithms, and interfaces that support humans in their decision-making without patronization. To illustrate the method, we create a recommender AI for tiny wellbeing habits and run a small case study, including a survey. From the results, we infer how people perceive the relationship between them and the AI and to what extent it helps them to achieve their goals. We review our seven-step engineering method and suggest modifications for the next iteration.

Keywords: recommender systems, natural language processing, health apps, engineering methods

Procedia PDF Downloads 165

Authors: Jiahui Song

Abstract:

In order to predict or explain a complicated biological process, it is important first to construct mathematical models that can be used to yield analytical solutions. Through numerical simulation, mathematical model results can be used to test scenarios that might not be easily attained in a laboratory experiment, or to predict parameters or phenomena. High-intensity, nanosecond pulse electroporation has been a recent development in bioelectrics. The dynamic pore model can be achieved by including a dynamic aspect and a dependence on the pore population density into pore formation energy equation to analyze and predict such electroporation effects. For greater accuracy, with inclusion of atomistic details, molecular dynamics (MD) simulations were also carried out during this study. Besides inducing pores in cells, external voltages could also be used in principle to modulate action potential generation in nerves. This could have an application in electrically controlled ‘pain management’. Also a simple model-based rate equation treatment of the various cellular bio-chemical processes has been used to predict the pulse number dependent cell survival trends.

Keywords: model, high-intensity, nanosecond, bioelectrics

Procedia PDF Downloads 226

Authors: Lídia Chienda, Tchilissila A. Simoes

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Angola is known for the low quality of maternal health services, registering one of the highest maternal and child mortality of Africa. Working in these health facilities may be of great challenge for health professionals. In this study, we aimed to identify the presence of occupational stress in 76 nurses working in a maternity ward in Lubango, Southern Angola. The participants completed the Health Professional Stress Questionnaire and reported a moderate and high level of stress. To these individuals, 'receiving a low salary,' 'inadequate/insufficient salary,' 'overwork or very demanding work' and 'working long hours in a row' seemed to be the main indicators of occupational stress. Moreover, there was an influence of the work overload, the remuneration earned, the career, and family conflicts in the occupational stress index. These results contributed to a better understanding of the difficulties Angolan nurses are facing and the need to implement policies that envisage the wellbeing of this population.

Keywords: Africa, maternity wards, nursing, occupational stress

Procedia PDF Downloads 202

Authors: Shenlun Chen, Leonard Wee

Abstract:

Tumor grading is an essential reference for colorectal cancer (CRC) staging and survival prognostication. The widely used World Health Organization (WHO) grading system defines histological grade of CRC adenocarcinoma based on the density of glandular formation on whole slide images (WSI). Tumors are classified as well-, moderately-, poorly- or un-differentiated depending on the percentage of the tumor that is gland forming; >95%, 50-95%, 5-50% and <5%, respectively. However, manually grading WSIs is a time-consuming process and can cause observer error due to subjective judgment and unnoticed regions. Furthermore, pathologists’ grading is usually coarse while a finer and continuous differentiation grade may help to stratifying CRC patients better. In this study, a deep learning based automatic differentiation grading algorithm was developed and evaluated by survival analysis. Firstly, a gland segmentation model was developed for segmenting gland structures. Gland regions of WSIs were delineated and used for differentiation annotating. Tumor regions were annotated by experienced pathologists into high-, medium-, low-differentiation and normal tissue, which correspond to tumor with clear-, unclear-, no-gland structure and non-tumor, respectively. Then a differentiation prediction model was developed on these human annotations. Finally, all enrolled WSIs were processed by gland segmentation model and differentiation prediction model. The differentiation grade can be calculated by deep learning models’ prediction of tumor regions and tumor differentiation status according to WHO’s defines. If multiple WSIs were possessed by a patient, the highest differentiation grade was chosen. Additionally, the differentiation grade was normalized into scale between 0 to 1. The Cancer Genome Atlas, project COAD (TCGA-COAD) project was enrolled into this study. For the gland segmentation model, receiver operating characteristic (ROC) reached 0.981 and accuracy reached 0.932 in validation set. For the differentiation prediction model, ROC reached 0.983, 0.963, 0.963, 0.981 and accuracy reached 0.880, 0.923, 0.668, 0.881 for groups of low-, medium-, high-differentiation and normal tissue in validation set. Four hundred and one patients were selected after removing WSIs without gland regions and patients without follow up data. The concordance index reached to 0.609. Optimized cut off point of 51% was found by “Maxstat” method which was almost the same as WHO system’s cut off point of 50%. Both WHO system’s cut off point and optimized cut off point performed impressively in Kaplan-Meier curves and both p value of logrank test were below 0.005. In this study, gland structure of WSIs and differentiation status of tumor regions were proven to be predictable through deep leaning method. A finer and continuous differentiation grade can also be automatically calculated through above models. The differentiation grade was proven to stratify CAC patients well in survival analysis, whose optimized cut off point was almost the same as WHO tumor grading system. The tool of automatically calculating differentiation grade may show potential in field of therapy decision making and personalized treatment.

Keywords: colorectal cancer, differentiation, survival analysis, tumor grading

Procedia PDF Downloads 134

Authors: Pranav Gulati, Isha Sharma

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Hypertension and hypotension are known to have repercussions on the health of an individual, with hypertension contributing to an increased probability of risk to cardiovascular diseases and hypotension resulting in syncope. This prompts the development of a non-invasive, non-intrusive, continuous and cuff-less blood pressure monitoring system to detect blood pressure variations and to identify individuals with acute and chronic heart ailments, but due to the unavailability of such devices for practical daily use, it becomes difficult to screen and subsequently regulate blood pressure. The complexities which hamper the steady monitoring of blood pressure comprises of the variations in physical characteristics from individual to individual and the postural differences at the site of monitoring. We propose to develop a continuous, comprehensive cardio-analysis tool, based on reflective photoplethysmography (PPG). The proposed device, in the form of an eyewear captures the PPG signal and estimates the systolic and diastolic blood pressure using a sensor positioned near the temporal artery. This system relies on regression models which are based on extraction of key points from a pair of PPG wavelets. The proposed system provides an edge over the existing wearables considering that it allows for uniform contact and pressure with the temporal site, in addition to minimal disturbance by movement. Additionally, the feature extraction algorithms enhance the integrity and quality of the extracted features by reducing unreliable data sets. We tested the system with 12 subjects of which 6 served as the training dataset. For this, we measured the blood pressure using a cuff based BP monitor (Omron HEM-8712) and at the same time recorded the PPG signal from our cardio-analysis tool. The complete test was conducted by using the cuff based blood pressure monitor on the left arm while the PPG signal was acquired from the temporal site on the left side of the head. This acquisition served as the training input for the regression model on the selected features. The other 6 subjects were used to validate the model by conducting the same test on them. Results show that the developed prototype can robustly acquire the PPG signal and can therefore be used to reliably predict blood pressure levels.

Keywords: blood pressure, photoplethysmograph, eyewear, physiological monitoring

Procedia PDF Downloads 279

Authors: Othmane Zarhali, Cécilia Aubrun, Emmanuel Bacry, Jean-Philippe Bouchaud, Jean-François Muzy

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The Nested factor model was introduced by Bouchaud and al., where the asset return fluctuations are explained by common factors representing the market economic sectors and residuals (noises) sharing with the factors a common dominant volatility mode in addition to the idiosyncratic mode proper to each residual. This construction infers that the factors-residuals log volatilities are correlated. Here, we consider the case of a single factor where the only dominant common mode is a S-fbm process (introduced by Peng, Bacry and Muzy) with Hurst exponent H around 0.11 and the residuals having in addition to the previous common mode idiosyncratic components with Hurst exponents H around 0. The reason for considering this configuration is twofold: preserve the Nested factor model’s characteristics introduced by Bouchaud and al. and propose a framework through which the stylized fact reported by Peng and al. is reproduced, where it has been observed that the Hurst exponents of stock indices are large as compared to those of individual stocks. In this work, we show that the Log S-fbm Nested factor model’s construction leads to a Hurst exponent of single stocks being the ones of the idiosyncratic volatility modes and the Hurst exponent of the index being the one of the common volatility modes. Furthermore, we propose a statistical procedure to estimate the Hurst factor exponent from the stock returns dynamics together with theoretical guarantees, with good results in the limit where the number of stocks N goes to infinity. Last but not least, we show that the factor can be seen as an index constructed from the single stocks weighted by specific coefficients.

Keywords: hurst exponent, log S-fbm model, nested factor model, small intermittency approximation

Procedia PDF Downloads 51