Search results for: Healthcare Multi-Agent System

Authors: James Ladzekpo

Abstract:

Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: Ouassila Bekkal Brikci Benhabib, Zahia Boucherit Otmani, Kebir Boucherit, A. Seghir

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The establishment of intravenous catheters in hospitalized patient is an act common in many clinical situations. These therapeutic tools, from their insertion in the body, represent gateways including fungal germs prone. The latter can generate the growth of biofilms, which can be the cause of fungal infection. Faced with this problem, we conducted a study at the University Hospital of Tlemcen in the neurosurgery unit and aims to isolate and identify Candida yeasts from intravenous catheters. Then test their ability to form biofilms. Materials and methods: 256 patient hospitalized in surgery of the hospital in west Algeria were submitted to this study. All samples were taken from peripheral venous catheters implanted for 72 hours or more days. A total of 31 isolates of Candida species were isolated. MIC and SMIC are determined at 80% inhibition by the test XTT tetrazolium measured at 490 nm. The final concentrations of antifungal agent being between 0.03 and 16 mg / ml for amphotericin B and from 0.015 to 8 mg / mL caspofungin. Results: 31 Candida species isolates from catheters including 14 Candida albicans and 17 Candida non albicans . 21 strains of all the isolates were able to form biofilms. In their form of Planktonic cells, all isolates are 100% susceptible to antifungal agents tested. However, in their state of biofilms, more isolates have become tolerant to the tested antifungals. Conclusion: Candida yeasts isolated from intravascular catheters are considered an important virulence factor in the pathogenesis of infections. Their involvement in catheter-related infections can be disastrous for their potential to generate biofilms. They survive high concentrations of antifungal where treatment failure. Pending the development of a therapeutic approach antibiofilm related to catheters, their mastery is going through: -The risk of infection prevention based on the training and awareness of medical staff, -Strict hygiene and maximum asepsis, and -The choice of material limiting microbial colonization.

Keywords: candida, biofilm, hospital, infection, amphotericin B, caspofungin

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Authors: Maria Karidakis, Barbara Kelly

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The challenges precipitated by the advent of COVID-19 have brought to the fore the task governments and key stakeholders are faced with; ensuring public health communication is readily accessible to vulnerable populations. COVID-19 has presented challenges for the provision and reception of timely, accessible, and accurate health information pertaining to vaccine health messaging to Aboriginal and Torres Strait Islander peoples. The aim of this qualitative study was to explore strategies used by Aboriginal-led organisations to improve communication about COVID-19 and vaccination for their communities and to explore how these mediation and outreach strategies were received by community members. We interviewed 6 Aboriginal-led organisations and 15 community members from several states across Australian, and these interviews were analysed thematically. The findings suggest that effective public health communication is enhanced when aFirst nations-led response defines the governance that happens in First Nations communities. Pro-active and self-determining Aboriginal leadership and decision-making helps drive the response to counter a growing trend towards vaccine hesitancy. Other strategies include establishing partnerships with government departments and relevant non-governmental organisations to ensure services are implemented and culturally appropriate. The outcomes of this research will afford policymakers, stakeholders in healthcare, and cultural mediators the capacity to identify strengths and potential problems associated with pandemic health information and to subsequently implement creative and culturally specific solutions that go beyond the provision of written documentation via translation or interpreting. It will also enable governing bodies to adjust multilingual polices and to adopt mediation strategies that will improve information delivery and intercultural services on a national and international level.

Keywords: intercultural communication, qualitative, public health communication, COVID-19, pandemic, mediated communication, first nations people

Procedia PDF Downloads 160

Authors: Loreine Makki, Marc Anthony Mannah, Christophe Batard, Nicolas Ginot, Julien Weckbrodt

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Planar transformers are extensively utilized in high-frequency, high power density power electronic converters. The breakthrough of wide-bandgap technology compelled power electronic system miniaturization while inducing pivotal effects on system modeling and manufacturing within the power electronics industry. A significant consideration to simulate and model the unanticipated parasitic parameters emerges with the requirement to mitigate electromagnetic disturbances. This paper will present an equivalent circuit model of a shielded pulse planar transformer quantifying leakage inductance and resistance in addition to the interwinding capacitance of the primary and secondary windings. ANSYS Q3D Extractor was utilized to model and simulate the transformer, intending to study the immunity of the simulated equivalent model to high dv/dt occurrences. A convenient correlation between simulation and experimental results is presented.

Keywords: Planar transformers, wide-band gap, equivalent circuit model, shielded, ANSYS Q3D Extractor, dv/dt

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Authors: Yasith Mindula Saipath Wickramasinghe

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Augmented reality applications require a high processing power to load, render and live stream high-definition AR models and virtual scenes; it also requires device sensors to work excessively to coordinate with internal hardware, OS and give the expected outcome in advance features like object detection, real time tracking, as well as voice and text recognition. Excessive thermal generation due to these advanced functionalities has become a major research problem as it is unbearable for smaller mobile devices to manage such heat increment and battery drainage as it causes physical harm to the devices in the long term. Therefore, effective thermal management is one of the major requirements in Augmented Reality application development. As this paper discusses major causes for this issue, it also provides possible solutions in the means of operating system adaptations as well as further research on best coding practises to optimize the application performance that reduces thermal excessive thermal generation.

Keywords: augmented reality, device thermal management, GPU, operating systems, device I/O, overheating

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Authors: G. Barański, P. Kacejko, M. Wendeker

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The impact of the ignition advance control algorithms of the ASz-62IR-16X aircraft piston engine on a combustion process has been presented in this paper. This aircraft engine is a nine-cylinder 1000 hp engine with a special electronic control ignition system. This engine has two spark plugs per cylinder with an ignition advance angle dependent on load and the rotational speed of the crankshaft. Accordingly, in most cases, these angles are not optimal for power generated. The scope of this paper is focused on developing algorithms to control the ignition advance angle in an electronic ignition control system of an engine. For this type of engine, i.e. radial engine, an ignition advance angle should be controlled independently for each cylinder because of the design of such an engine and its crankshaft system. The ignition advance angle is controlled in an open-loop way, which means that the control signal (i.e. ignition advance angle) is determined according to the previously developed maps, i.e. recorded tables of the correlation between the ignition advance angle and engine speed and load. Load can be measured by engine crankshaft speed or intake manifold pressure. Due to a limited memory of a controller, the impact of other independent variables (such as cylinder head temperature or knock) on the ignition advance angle is given as a series of one-dimensional arrays known as corrective characteristics. The value of the ignition advance angle specified combines the value calculated from the primary characteristics and several correction factors calculated from correction characteristics. Individual cylinder control can proceed in line with certain indicators determined from pressure registered in a combustion chamber. Control is assumed to be based on the following indicators: maximum pressure, maximum pressure angle, indicated mean effective pressure. Additionally, a knocking combustion indicator was defined. Individual control can be applied to a single set of spark plugs only, which results from two fundamental ideas behind designing a control system. Independent operation of two ignition control systems – if two control systems operate simultaneously. It is assumed that the entire individual control should be performed for a front spark plug only and a rear spark plug shall be controlled with a fixed (or specific) offset relative to the front one or from a reference map. The developed algorithms will be verified by simulation and engine test sand experiments. This work has been financed by the Polish National Centre for Research and Development, INNOLOT, under Grant Agreement No. INNOLOT/I/1/NCBR/2013.

Keywords: algorithm, combustion process, radial engine, spark plug

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Authors: Elena Valeryevna Pozhidaeva

Abstract:

This linguistic case study is based on an interaction between the social position and foodways. In every culture there is a social hierarchical system in which there can be means to express and to identify the social status of a person. Food serves as a class indicator. The British being a verbal nation use the words as a preferred medium for signalling and recognising the social status. The linguistic analysis reflects a symbolic hierarchy determined by social groups in the UK. The linguistic class indicators of a British hierarchical system are detectable directly – in speech acts. They are articulated in every aspect of a national identity’s life from preferences of the food and the choice to call it to the names of the meals. The linguistic class indicators can as well be detected indirectly – through symbolic meaning or via the choice of the mealtime, its class (e.g the classes of tea or marmalade), the place to buy food (the class of the supermarket) and consume it (the places for eating out and the frequency of such practices). Under analysis of this study are not only food items and their names but also such categories as cutlery as a class indicator and the act of eating together as a practice of social significance and a class indicator. Current social changes and economic developments are considered and their influence on the class indicators appearance and transformation.

Keywords: linguistic, class, social indicator, English, food class

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Authors: Salah Al-Ali

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The role of technology transfer in technical and vocational education is significant since lecturers, trainers, and students can obtain the updated knowledge, skills, and attitudes that are currently being practiced by local and international businesses and industries. Technology transfer can indeed close the gap between what is being learned and practiced in technical and vocational institutions and the world of work. However, the success of technology transfer in technical and vocational education perspectives would depend entirely on the quality of management. It is their responsibility when signing an agreement with internal or external providers of technology, to include calluses that enable academic staff in related specialty to interact positively and freely with the supplier of technology. In other terms, ensuring no clear or hidden restriction is imposed by the supplier of technology to acquire the know-how and know-why that are embedded in the agreement. In this paper, I present some of the empirical results and observations which describe the interactions between the supplier of technology (Electrical Engineering System) and the recipient of the technology (PAAE&T) in the field of technology transfer. In another word, whether the PAAE&T have taken the opportunity while building its new headquarter, the transfer of technology from the supplier of an electrical engineering system to its academic staff in its various Electrical Engineering Academic Departments at the PAAE&T colleges and institutions. The paper argues that, for effective and efficient transfer of technology, the recipient (PAAE&T) must ensure that the agreement with the supplier of the Electrical Engineering System must include calluses that would allow the PAAE&T academic staff in its various Electrical Engineering Academic Departments in its various colleges and institutions to acquire the technology embedded in the agreement. The paper concludes that the transfer of technology and the building of a local scientific and technical infrastructure must be viewed by Kuwaiti decision-makers as complementary to one another. Thus, reducing, to great extent, the level of dependence on expatriates, particularly in the essential sectors of the economy.

Keywords: vocational and technical education, technology transfer, enhancing indigenous capabilities, Kuwait

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Zdravko Eškinja, Lovre Miljanić, Ognjen Kuljača

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This paper is an overview of simulation tools used to model specific thermal dynamics that occurs while controlling double skin façade. Research has been conducted on simplified construction with single zone where one side is glazed. Heat flow and temperature responses are simulated in three different simulation tools: IDA-ICE, EnergyPlus and HAMBASE. The excitation of observed system, used in all simulations, was a temperature step of exterior environment. Air infiltration, insulation and other disturbances are excluded from this research. Although such isolated behaviour is not possible in reality, experiments are carried out to gain novel information about heat flow transients which are not observable under regular conditions. Results revealed new possibilities for adapting the parameters of the neural network regulator. Along numerical simulations, the same set-up has been also tested in a real-time experiment with a 1:18 scaled model and thermal chamber. The comparison analysis brings out interesting conclusion about simulation accuracy in this particular case.

Keywords: double skin façade, experimental tests, heat control, heat flow, simulated tests, simulation tools

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Authors: Duchel Jeanedvi Kinouani Kinavouidi, Christian Aimé Kayath, Etienne Nguimbi

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Biosurfactants are amphipathic molecules produced by many microorganisms, usually bacteria, fungi, and yeasts. +ey possesses the property of reducing the tension of the membrane interfaces. No studies have been conducted on Shigella species showing the role of biosurfactant-like molecules (BLM) in pathogenicity. +e aim of this study is to assess the ability of Shigella environmental and clinical strains to produce BLM and investigate the involvement of biosurfactants in pathogenicity. Our study has shown that BLM is secreted in the extracellular medium with EI24 ranging from 80% to 100%. +e secretion depends on the type III secretion system (T3SS). Moreover, our results have shown that S. flexneri, S. boydii, and S. sonnei are able to interact with hydrophobic areas with 17.64%, 21.42%, and 22.22% hydrophobicity, respectively. BLM secretion is totally prevented due to the inhibition of T3SS by 100 mM benzoic and 1.5 mg/ml salicylic acids. P. aeruginosa harboring T3SS is able to produce 100% of BLM in the presence or in the absence of both T3SS inhibitors. +e secreted BLM are extractable with an organic solvent such as chloroform, and this could entirely be considered a lipopeptide or polypeptide compound. Secretion of BLM allows some Shigella strains to induce multicellular phenomena like ‘swarming.’

Keywords: shigella flexneri, biosurfactant, T3SS, Lipopeptide

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Authors: Sengul Celik, Alper Ketenci

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In Turkish Constitution about health care in Article 56, it is said that: everyone has the right to live in a healthy and balanced environment. It is the duty of the state and citizens to improve the environment, protect environmental health, and prevent environmental pollution. The state ensures that everyone lives their lives in physical and mental health; it organizes the planning and service of health institutions from a single source in order to realize cooperation by increasing savings and efficiency in human and substance power. The state fulfills this task by utilizing and supervising health and social institutions in the public and private sectors. General health insurance can be established by law for the widespread delivery of health services. To have health care is one of the basic rights of patients. After the coupe attempt in July 2016, the Government of Turkey has announced a state of emergency and issued lots of emergency decrees. By these emergency decrees, lots of people were dismissed from their jobs and lost their some basic social rights. The violations occur in social life. One of the most common observations is the discrimination by government in health care system. This study aims to put forward the violation of human rights in health care system in Turkey due to their discriminated position by an emergency decree. The study is a case study that is based on nine interviews with the people or relatives of people who lost their jobs by an emergency decree in Turkey. In this study, no personally identifiable information was obtained for the safety of individuals. Also no distinctive questions regarding the identity of individuals were asked. The interviews are obtained through internet call applications. The data were analyzed through the requirements of regular health care system in Turkey. The interviews expose that the people or the relatives of people lost their right to have regular health care. They have to pay extra amount both in clinical services and in medication treatment. The patient right to quality medical care without prejudice is violated. It was assessed that the people who are involved in emergency decree and their relatives are discriminated by government and deprived of regular medical care and supervision. Although international legal arrangements and legal responsibilities of the state have been put forward by Article 56, they are violated in practice. To prevent these kinds of violations, some measures should be taken against the deprivation in health care system especially towards the discriminated people by an emergency decree.

Keywords: emergency decree in Turkey, health care, discriminated people, patients rights

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Authors: Stefanache Stefania, Ioana R. Podina

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Background: Embodied Conversational Agents (ECAs) are revolutionizing education and healthcare by offering cost-effective, adaptable, and portable solutions. Research on the Uncanny Valley effect (UVE) involves various embodied agents, including ECAs. Achieving the optimal level of anthropomorphism, no consensus on how to overcome the uncanniness problem. Objectives: This systematic review aims to identify the user characteristics, agent features, and context factors that influence the UVE. Additionally, this review provides recommendations for creating effective ECAs and conducting proper experimental studies. Methods: We conducted a systematic review following the PRISMA 2020 guidelines. We included quantitative, peer-reviewed studies that examined human-ECA interaction. We identified 17,122 relevant records from ACM Digital Library, IEE Explore, Scopus, ProQuest, and Web of Science. The quality of the predictors, mediators, and moderators adheres to the guidelines set by prior systematic reviews. Results: Based on the included studies, it can be concluded that females and younger people perceive the ECA as more attractive. However, inconsistent findings exist in the literature. ECAs characterized by extraversion, emotional stability, and agreeableness are considered more attractive. Facial expressions also play a role in the UVE, with some studies indicating that ECAs with more facial expressions are considered more attractive, although this effect is not consistent across all studies. Few studies have explored contextual factors, but they are nonetheless crucial. The interaction scenario and exposure time are important circumstances in human-ECA interaction. Conclusions: The findings highlight a growing interest in ECAs, which have seen significant developments in recent years. Given this evolving landscape, investigating the risk of the UVE can be a promising line of research.

Keywords: human-computer interaction, uncanny valley effect, embodied conversational agent, systematic review

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Authors: Ana Mafalda Silva, Rebeca Fontes, Ana Paula Vaz, Carla Carreira, Ana Corte-Real

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Decades of research on the topic of interpersonal violence against minors highlight five main conclusions: 1) it causes harmful effects on children's development and health; 2) it is prevalent; 3) it violates children's rights; 4) it can be prevented and 5) parents are the main aggressors. The child abuse scenario is identified through clinical observation, administrative data and self-reports. The most used instruments are self-reports; however, there are no valid and reliable self-report instruments for minors, which consist of a retrospective interpretation of the situation by the victim already in her adult phase and/or by her parents. Clinical observation and collection of information, namely from the orofacial region, are essential in the early identification of these situations. The management of medical data, such as personal data, must comply with the General Data Protection Regulation (GDPR), in Europe, and with the General Law of Data Protection (LGPD), in Brazil. This review aims to answer the question: In a situation of medical assistance to minors, in the suspicion of interpersonal violence, due to mistreatment, is it necessary for the guardians to provide consent in the registration and sharing of personal data, namely medical ones. A scoping review was carried out based on a search by the Web of Science and Pubmed search engines. Four papers and two documents from the grey literature were selected. As found, the process of identifying and signaling child abuse by the health professional, and the necessary early intervention in defense of the minor as a victim of abuse, comply with the guidelines expressed in the GDPR and LGPD. This way, the notification in maltreatment scenarios by health professionals should be a priority and there shouldn’t be the fear or anxiety of legal repercussions that stands in the way of collecting and treating the data necessary for the signaling procedure that safeguards and promotes the welfare of children living with abuse.

Keywords: child abuse, disease notifications, ethics, healthcare assistance

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Authors: Qiang Zhang, Xiaojian Hu1

Abstract:

Intersection is the place where vehicles and pedestrians must pass through, turn and evacuate. Obtaining the motion data of vehicles near the intersection is of great significance for transportation research. Since there are usually many targets and there are more conflicts between targets, this makes it difficult to obtain vehicle motion parameters in traffic videos of intersections. According to the characteristics of traffic videos, this paper applies video technology to realize the automated track, count and trajectory extraction of vehicles to collect traffic data by roadside surveillance cameras installed near the intersections. Based on the video recognition method, the vehicles in each lane near the intersection are tracked with extracting trajectory and counted respectively in various degrees of occlusion and visibility. The performances are compared with current recognized CPU-based algorithms of real-time tracking-by-detection. The speed of the presented system is higher than the others and the system has a better real-time performance. The accuracy of direction has reached about 94.99% on average, and the accuracy of classification and statistics has reached about 75.12% on average.

Keywords: tracking and statistics, vehicle, signalized intersection, motion parameter, trajectory

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Authors: Chongyang Ye, Rong Liu

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Elastic compression stockings (ECSs) have been widely applied in prophylaxis and treatment of chronic venous insufficiency of lower extremities. The medical function of ECS is to improve venous return and increase muscular pumping action to facilitate blood circulation, which is largely determined by the complex interaction between the ECS and lower limb tissues. Understanding the mechanical transmission of ECS along the skin surface, deeper tissues, and vascular system is essential to assess the effectiveness of the ECSs. In this study, a three-dimensional (3D) finite element (FE) model of the leg-ECS system integrated with a 3D fluid-solid interaction (FSI) model of the leg-vein system was constructed to analyze the biomechanical properties of veins and soft tissues under different ECS compression. The Magnetic Resonance Imaging (MRI) of the human leg was divided into three regions, including soft tissues, bones (tibia and fibula) and veins (peroneal vein, great saphenous vein, and small saphenous vein). The ECSs with pressure ranges from 15 to 26 mmHg (Classes I and II) were adopted in the developed FE-FSI model. The soft tissue was assumed as a Neo-Hookean hyperelastic model with the fixed bones, and the ECSs were regarded as an orthotropic elastic shell. The interfacial pressure and stress transmission were simulated by the FE model, and venous hemodynamics properties were simulated by the FSI model. The experimental validation indicated that the simulated interfacial pressure distributions were in accordance with the pressure measurement results. The developed model can be used to predict interfacial pressure, stress transmission, and venous hemodynamics exerted by ECSs and optimize the structure and materials properties of ECSs design, thus improving the efficiency of compression therapy.

Keywords: elastic compression stockings, fluid-solid interaction, tissue and vein properties, prediction

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Authors: Nadjet Brahmia

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The evolution of urban forms, the new aspects of mobility, the ways of life and economic models make public transport conventional collective low-performing on the majority of largest Algerian cities, particularly in the west of Algeria. On the other side, the information and communication technologies (ICT) open new eventualities to develop a new mode of transport which brings together both the tenders offered by the public service collective and those of the particular vehicle, suitable for urban requirements, social and environmental. Like the concrete examples made in the international countries in terms of on-demand transport systems (ODT) more particularly in the developed countries, this article has for objective the opportunity analysis to establish a service of ODT at the level of a few towns of Oran Wilaya, such a service will be subsequently spread on the totality of the Wilaya if not on the whole of Algeria. In this context, we show the different existing means of transport in the current network whose aim to illustrate the points of insufficiency accented in the present transport system, then we discuss the solutions that may exhibit a service of ODT to the problem studied all around the transport sector, to carry at the end to highlight the capabilities of ODT replying to the transformation of mobilities, this in the light of well-defined cases.

Keywords: mobility, on-demand transport, public transport collective, transport system

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Authors: Mohammadali Abedini Sanigy, Jiangang Fei

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In this paper, a mathematical optimization model was developed to maximize the profit in a vegetable production planning problem. It serves as a decision support system that assists farmers in land allocation to crops and harvest scheduling decisions. The developed model can handle different rotation rules in two consecutive cycles of production, which is a common practice in organic production system. Moreover, different production methods of the same crop were considered in the model formulation. The main strength of the model is that it is not restricted to predetermined production periods, which makes the planning more flexible. The model is classified as a mixed integer programming (MIP) model and formulated in PYOMO -a Python package to formulate optimization models- and solved via Gurobi and CPLEX optimizer packages. The model was tested with secondary data from 'Australian vegetable growing farms', and the results were obtained and discussed with the computational test runs. The results show that the model can successfully provide reliable solutions for real size problems.

Keywords: crop rotation, harvesting, mathematical model formulation, vegetable production

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Authors: Ambuj Kumar Gautam, V. Vasu

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This paper introduces a head movement based Human Machine Interface (HMI) that uses the right and left movements of head to control a robot motion. Here we present an approach for making an effective technique for real-time face orientation information system, to control a robot which can be efficiently used for Electrical Powered Wheelchair (EPW). Basically this project aims at application related to HMI. The system (machine) identifies the orientation of the face movement with respect to the pixel values of image in a certain areas. Initially we take an image and divide that whole image into three parts on the basis of its number of columns. On the basis of orientation of face, maximum pixel value of approximate same range of (R, G, and B value of a pixel) lie in one of divided parts of image. This information we transfer to the microcontroller through serial communication port and control the motion of robot like forward motion, left and right turn and stop in real time by using head movements.

Keywords: electrical powered wheelchair (EPW), human machine interface (HMI), robotics, microcontroller

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Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

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Authors: Van Truong Pham

Abstract:

Background: Self-management was described as a potential strategy for blood pressure control in patients with hypertension. However, the effects of self-management interventions on blood pressure, self-efficacy, medication adherence, and body mass index (BMI) in older adults with hypertension have not been systematically evaluated. We evaluated the effects of self-management interventions on systolic blood pressure (SBP) and diastolic blood pressure (DBP), self-efficacy, medication adherence, and BMI in hypertensive older adults. Methods: We followed the recommended guidelines of preferred reporting items for systematic reviews and meta-analyses. Searches in electronic databases including CINAHL, Cochrane Library, Embase, Ovid-Medline, PubMed, Scopus, Web of Science, and other sources were performed to include all relevant studies up to April 2019. Studies selection, data extraction, and quality assessment were performed by two reviewers independently. We summarized intervention effects as Hedges' g values and 95% confidence intervals (CI) using a random-effects model. Data were analyzed using Comprehensive Meta-Analysis software 2.0. Results: Twelve randomized controlled trials met our inclusion criteria. The results revealed that self-management interventions significantly improved blood pressure control, self-efficacy, medication adherence, whereas the effect of self-management on BMI was not significant in older adult patients with hypertension. The following Hedges' g (effect size) values were obtained: SBP, -0.34 (95% CI, -0.51 to -0.17, p < 0.001); DBP, -0.18 (95% CI, -0.30 to -0.05, p < 0.001); self-efficacy, 0.93 (95%CI, 0.50 to 1.36, p < 0.001); medication adherence, 1.72 (95%CI, 0.44 to 3.00, p=0.008); and BMI, -0.57 (95%CI, -1.62 to 0.48, p = 0.286). Conclusions: Self-management interventions significantly improved blood pressure control, self-efficacy, and medication adherence. However, the effects of self-management on obesity control were not supported by the evidence. Healthcare providers should implement self-management interventions to strengthen patients' role in managing their health care.

Keywords: self-management, meta-analysis, blood pressure control, self-efficacy, medication adherence, body mass index

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Mansoor Soomro

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The evolution of power electronic equipment has been pivotal in making industrial processes productive, efficient and safe. Despite its attractive features, it has been due to nonlinear loads which make it vulnerable to power quality conditions. Harmonics is one of the power quality problem in which the harmonic frequency is integral multiple of supply frequency. Therefore, the supply voltage and supply frequency do not last within their tolerable limits. As a result, distorted current and voltage waveform may appear. Attributes of low power quality confirm that an electrical device or equipment is likely to malfunction, fail promptly or unable to operate under all applied conditions. The electrical power system is designed for delivering power reliably, namely maximizing power availability to customers. However, power quality events are largely untracked, and as a result, can take out a process as many as 20 to 30 times a year, costing utilities, customers and suppliers of load equipment, a loss of millions of dollars. The ill effects of current harmonics reduce system efficiency, cause overheating of connected equipment, result increase in electrical power and air conditioning costs. With the passage of time and the rapid growth of power electronic converters has highlighted the damages of current harmonics in the electrical power system. Therefore, it has become essential to address the bad influence of current harmonics while planning any suitable changes in the electrical installations. In this paper, an effort has been made to mitigate the effects of dominant 3rd order current harmonics. Passive filtering technique with six pulse multiplication converter has been employed to mitigate them. Since, the standards of power quality are to maintain the supply voltage and supply current within certain prescribed standard limits. For this purpose, the obtained results are validated as per specifications of IEEE 519-1992 and IEEE 519-2014 performance standards.

Keywords: current harmonics, power quality, passive filters, power electronic converters

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Authors: Iacopo Testi, Diego Pajarito, Nicoletta Roberto, Carmen Greco

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Today, a consistent segment of the world’s population lives in urban areas, and this proportion will vastly increase in the next decades. Therefore, understanding the key trends in urbanization, likely to unfold over the coming years, is crucial to the implementation of sustainable urban strategies. In parallel, the daily amount of digital data produced will be expanding at an exponential rate during the following years. The analysis of various types of data sets and its derived applications have incredible potential across different crucial sectors such as healthcare, housing, transportation, energy, and education. Nevertheless, in city development, architects and urban planners appear to rely mostly on traditional and analogical techniques of data collection. This paper investigates the prospective of the data science field, appearing to be a formidable resource to assist city managers in identifying strategies to enhance the social, economic, and environmental sustainability of our urban areas. The collection of different new layers of information would definitely enhance planners' capabilities to comprehend more in-depth urban phenomena such as gentrification, land use definition, mobility, or critical infrastructural issues. Specifically, the research results correlate economic, commercial, demographic, and housing data with the purpose of defining the youth economic discomfort index. The statistical composite index provides insights regarding the economic disadvantage of citizens aged between 18 years and 29 years, and results clearly display that central urban zones and more disadvantaged than peripheral ones. The experimental set up selected the city of Rome as the testing ground of the whole investigation. The methodology aims at applying statistical and spatial analysis to construct a composite index supporting informed data-driven decisions for urban planning.

Keywords: data science, spatial analysis, composite index, Rome, urban planning, youth economic discomfort index

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Authors: Tasawar Hayat, Madiha Rashid, Maria Imtiaz, Ahmed Alsaedi

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This problem is about the study of flow of viscous fluid due to rotating disk in nanofluid. Effects of magnetic field, slip boundary conditions and thermal radiations are encountered. An incompressible fluid soaked the porous medium. In this model, nanoparticles of Cu is considered with water as the base fluid. For Copper-water nanofluid, graphical results are presented to describe the influences of nanoparticles volume fraction (φ) on velocity and temperature fields for the slip boundary conditions. The governing differential equations are transformed to a system of nonlinear ordinary differential equations by suitable transformations. Convergent solution of the nonlinear system is developed. The obtained results are analyzed through graphical illustrations for different parameters. Moreover, the features of the flow and heat transfer characteristics are analyzed. It is found that the skin friction coefficient and heat transfer rate at the surface are highest in copper-water nanofluid.

Keywords: MHD nanofluid, porous medium, rotating disk, slip effect

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Authors: Eric Lee G. Escobedo-Wu, Nidhi Rohatgi, Fouzel Dhebar

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It is challenging for patients to navigate through healthcare systems after-hours. This leads to delays in care, patient/provider dissatisfaction, inappropriate resource utilization, readmissions, and higher costs. It is important to provide patients and providers with effective clinical decision-making tools to allow seamless connectivity and coordinated care. In August 2015, patient-centric Stanford Health Care established Clinical Advice Services (CAS) to provide clinical decision support after-hours. CAS is founded on key Lean principles: Value stream mapping, empathy mapping, waste walk, takt time calculations, standard work, plan-do-check-act cycles, and active daily management. At CAS, Clinical Assistants take the initial call and manage all non-clinical calls (e.g., appointments, directions, general information). If the patient has a clinical symptom, the CAS nurses take the call and utilize standardized clinical algorithms to triage the patient to home, clinic, urgent care, emergency department, or 911. Nurses may also contact the on-call physician based on the clinical algorithm for further direction and consultation. Since August 2015, CAS has managed 228,990 calls from 26 clinical specialties. Reporting is built into the electronic health record for analysis and data collection. 65.3% of the after-hours calls are clinically related. Average clinical algorithm adherence rate has been 92%. An average of 9% of calls was escalated by CAS nurses to the physician on call. An average of 5% of patients was triaged to the Emergency Department by CAS. Key learnings indicate that a seamless connectivity vision, cascading, multidisciplinary ownership of the problem, and synergistic enterprise improvements have contributed to this success while striving for continuous improvement.

Keywords: after hours phone calls, clinical advice services, nurse triage, Stanford Health Care

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Authors: Raymond K. Jonkers

Abstract:

The present study investigated approaches and techniques to enhance strategic management governance and decision making within the framework of a performance-based balanced scorecard. The review of best practices from strategic, program, process, and systems engineering management provided for a holistic approach toward effective outcome-based capability management. One technique, based on factorial experimental design methods, was used to develop an empirical model. This model predicted the degree of capability effectiveness and is dependent on controlled system input variables and their weightings. These variables represent business performance measures, captured within a strategic balanced scorecard. The weighting of these measures enhances the ability to quantify causal relationships within balanced scorecard strategy maps. The focus in this study was on the performance of tangible assets within the scorecard rather than the traditional approach of assessing performance of intangible assets such as knowledge and technology. Tangible assets are represented in this study as physical systems, which may be thought of as being aboard a ship or within a production facility. The measures assigned to these systems include project funding for upgrades against demand, system certifications achieved against those required, preventive maintenance to corrective maintenance ratios, and material support personnel capacity against that required for supporting respective systems. The resultant scorecard is viewed as complimentary to the traditional balanced scorecard for program and performance management. The benefits from these scorecards are realized through the quantified state of operational capabilities or outcomes. These capabilities are also weighted in terms of priority for each distinct system measure and aggregated and visualized in terms of overall state of capabilities achieved. This study proposes the use of interactive controls within the scorecard as a technique to enhance development of alternative solutions in decision making. These interactive controls include those for assigning capability priorities and for adjusting system performance measures, thus providing for what-if scenarios and options in strategic decision-making. In this holistic approach to capability management, several cross functional processes were highlighted as relevant amongst the different management disciplines. In terms of assessing an organization’s ability to adopt this approach, consideration was given to the P3M3 management maturity model.

Keywords: management, systems, performance, scorecard

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Authors: Milad Keshvardoost, Amir Khanlari, Nader Khalesi

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Background: Any firm in the pharmaceutical industry requires efficient and effective management information systems (MIS) to support managerial functions. Purpose: The aim of this study is to investigate the impact of Task-Technology Fit on user effectiveness, efficiency, and creativity in Iranian pharmaceutical companies. Methodology: 345 reliable and validate questionnaires were distributed among selected samples, through the cluster method, to Information system users of eight leading Iranian pharmaceutical companies, based on the likert scale. The proposed model of the article is based on a model with Task technology fit, on user performance with the definition of efficiency, effectiveness, and creativity through mediation effects of perceived usefulness and ease of use. Results: This study confirmed that TTF with definitions of adequacy and compatibility has positive impacts on user performance Conclusion: We concluded that pharmaceutical users of IS, utilizing a system with a precise and intense observation of users' demands, may make facilitation for them to design an exclusive IS framework.

Keywords: information systems, user performance, pharmaceuticals, task technology fit

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Authors: Dev Ras Pandey, Bharat Mishra, S. K. Tripathi

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Cloud computing is ready to transform the structure of businesses and learning through supplying the real-time applications and provide an immediate help for small to medium sized businesses. The ability to run a hypervisor inside a virtual machine is important feature of virtualization and it is called nested virtualization. In today’s growing field of information technology, many of the virtualization models are available, that provide a convenient approach to implement, but decision for a single model selection is difficult. This paper explains the applications of operating system based virtualization in cloud computing with an appropriate/suitable model with their different specifications and user’s requirements. In the present paper, most popular models are selected, and the selection was based on container and hypervisor based virtualization. Selected models were compared with a wide range of user’s requirements as number of CPUs, memory size, nested virtualization supports, live migration and commercial supports, etc. and we identified a most suitable model of virtualization.

Keywords: virtualization, OS based virtualization, container based virtualization, hypervisor based virtualization

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Authors: Adam N. Bell, Sarina J. Ergas, Michael Nystrom, Nathan P. Brennan, Kevan L. Main

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Integrated Multi-Trophic Aquaculture systems (IMTA) have the potential to improve the sustainability of seafood production, generate organic fertilizer and feed, remove waste discharges and reduce energy use. IMTA can include periphyton biofilters where algae and microbes grow on surfaces, along with caught detritus and amphipods. Periphyton biofilters provide many advantages: nitrification, denitrification, primary production and ecological diversity. The goal of this study was to determine how biofilter hydraulic residence time (τ) effects periphyton biomass production, dissolved oxygen (DO) and nutrient removal. A pilot scale recirculating aquaculture system (RAS) was designed, constructed and operated at different hydraulic residence times (τ= 1, 2, 4, 6, 8 hours per tank). For each τ, a conservative tracer study was conducted to investigate system hydrodynamics. Data on periphyton weights, pH, nitrogen species, phosphorus, temperature and DO were collected. The tracer study for τ =1 hour revealed that the normalized time < τ, indicating short-circuiting. Periphyton biomass production rate was relatively unaffected by τ (R_e<1 for all τ). Average ammonia nitrogen removal was > 75% for all trials. Nitrate and nitrite did not accumulate in the RAS for τ≥4 hours due to enhanced denitrification in anoxic zones. For τ≥4 hours DO concentration was at a maximum of 4 mg L-1 after 14:00, and decreased to 0 mg L-1 during nighttime. At τ=1 hour, the RAS stayed > 2 mg L-1 and DO was more evenly distributed. For the validation trial, the culture tank was stocked with Centropomus undecimalis (common snook) and the system was operated at τ= 1 hr. Preliminary results showed that a RAS with an integrated periphyton biofilter could support fish health with low nutrient concentrations DO > 6 mg L-1.

Keywords: sustainable aquaculture, resource recovery, nitrogen, microalgae, hydrodynamics, integrated multi-trophic aquaculture

Procedia PDF Downloads 131