Search results for: healthcare networks

Authors: Sai Shruthi Sridhar, A. Madhumidha, Kreethika Guru, Priyanka Sekar, Ananthi Malayappan

Abstract:

Numerous changes in technology and its recent development are structuring long withstanding effect to our world, one among them is the emergence of “Internet of Things” (IoT). Similar to Technology world, one industry stands out in everyday life–healthcare. Attention to “quality of health care” is an increasingly important issue in a global economy and for every individual. As per WHO (World Health Organization) it is estimated to be less than 50% adhere to the medication provided and only about 20% get their medicine on time. Medication adherence is one of the top problems in healthcare which is fixable by use of technology. In recent past, there were minor provisions for elderly and specially-skilled to get motivated and to adhere medicines prescribed. This paper proposes a novel solution that uses IOT based RFID Medication Reminder Solution to provide personal health care services. This employs real time tracking which offer quick counter measures. The proposed solution builds on the recent digital advances in sensor technologies, smart phones and cloud services. This novel solution is easily adoptable and can benefit millions of people with a direct impact on the nation’s health care expenditure with innovative scenarios and pervasive connectivity.

Keywords: cloud services, IoT, RFID, sensors

Procedia PDF Downloads 335

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

Procedia PDF Downloads 36

Authors: Hasan G. Elmazoghi, Aisha I. Alzayani, Lubna S. Bentaher

Abstract:

Rainfall is a highly non-linear phenomena, which requires application of powerful supervised data mining techniques for its accurate prediction. In this study the Artificial Neural Network (ANN) technique is used to predict the mean monthly historical rainfall data collected from BENINA station in Benghazi for 31 years, the period of “1977-2006” and the results are compared against the observed values. The specific objective to achieve this goal was to determine the best combination of weather variables to be used as inputs for the ANN model. Several statistical parameters were calculated and an uncertainty analysis for the results is also presented. The best ANN model is then applied to the data of one year (2007) as a case study in order to evaluate the performance of the model. Simulation results reveal that application of ANN technique is promising and can provide reliable estimates of rainfall.

Keywords: neural networks, rainfall, prediction, climatic variables

Procedia PDF Downloads 480

Authors: Namrata Mishra

Abstract:

Health of a person plays a vital role in the collective health of his community and hence the well-being of the society as a whole. But, in today’s fast paced technology driven world, health issues are increasingly being associated with human behaviors – their lifestyle. Social networks have tremendous impact on the health behavior of individuals. Many researchers have used social network analysis to understand human behavior that implicates their social and economic environments. It would be interesting to use a similar analysis to understand human behaviors that have health implications. This paper focuses on concepts of those behavioural analyses that have health implications using social networks analysis and provides possible algorithmic approaches. The results of these approaches can be used by the governing authorities for rolling out health plans, benefits and take preventive measures, while the pharmaceutical companies can target specific markets, helping health insurance companies to better model their insurance plans.

Keywords: breadth first search, directed graph, health behaviors, social network analysis

Procedia PDF Downloads 467

Authors: Mohammad Julhas Sujan, Md. Ferdous Alam

Abstract:

Maternal, neonatal, and child health (MNCH) holds one of the prime focuses in Bangladesh’s national healthcare system. To save the lives of mothers and children, knowing the stock of MNCH medicines in different healthcare facilities and when to replenish them are essential. A robust information system not only facilitates efficient management of the essential MNCH medicines but also helps effective allocation of scarce resources. In Bangladesh, Supply chain management of the 25-essential life-saving medicines are currently tracked and monitored via an electronic logistics management information system (eLMIS). Our aim was to conduct a cross-sectional study with a year (2020) worth of data from 24 districts of Bangladesh to evaluate how eLMIS is helping the Government and other stakeholders in efficient supply chain management. Data were collected from 4711 healthcare facilities ranging from primary to secondary levels within a district. About 90% (4143) are community clinics which are considered primary health care facilities in Bangladesh. After eLMIS implementation, the average reporting rate across the districts has been increased (> 97%). The month of stock (MOS) of zinc is an average 6 months compared to Inj. Magnesium Sulphate which will take 2.5 years to consume according to the current average monthly consumption (AMC). Due to first approaching expiry, Tab. Misoprostol, 7.1% Chlorhexidine and Inj. Oxytocin may become unusable. Moreover, Inj. Oxytocin is temperature sensitive and may reduce its efficacy if it is stocked for a longer period. In contrast, Zinc should be sufficiently stocked to prevent sporadic stockouts. To understand how data are collected, transmitted, processed, and aggregated for MNCH medicines in a faster and timely manner, an electronic logistics management information system (eLMIS) is necessary. We recommend the use of such a system in developing countries like Bangladesh for efficient supply chain management of essential MNCH medicines.

Keywords: adaption, eLMIS, MNCH, live-saving medicines

Procedia PDF Downloads 158

Authors: Balimu Mwiya, Mundia Muya, Chabota Kaliba, Peter Mukalula

Abstract:

Factors affecting construction unit cost vary depending on a country’s political, economic, social and technological inclinations. Factors affecting construction costs have been studied from various perspectives. Analysis of cost factors requires an appreciation of a country’s practices. Identified cost factors provide an indication of a country’s construction economic strata. The purpose of this paper is to identify the essential factors that affect unit cost estimation and their breakdown using artificial neural networks. Twenty-five (25) identified cost factors in road construction were subjected to a questionnaire survey and employing SPSS factor analysis the factors were reduced to eight. The 8 factors were analysed using the neural network (NN) to determine the proportionate breakdown of the cost factors in a given construction unit rate. NN predicted that political environment accounted 44% of the unit rate followed by contractor capacity at 22% and financial delays, project feasibility, overhead and profit each at 11%. Project location, material availability and corruption perception index had minimal impact on the unit cost from the training data provided. Quantified cost factors can be incorporated in unit cost estimation models (UCEM) to produce more accurate estimates. This can create improvements in the cost estimation of infrastructure projects and establish a benchmark standard to assist the process of alignment of work practises and training of new staff, permitting the on-going development of best practises in cost estimation to become more effective.

Keywords: construction cost factors, neural networks, roadworks, Zambian construction industry

Procedia PDF Downloads 355

Authors: Mubarak Jan Beebee Zeba Mahetaab, Sumera Bibi Keenoo

Abstract:

Background: Covid-19 was first reported in Wuhan. On 13th March 2020, WHO declared Covid-19 as a pandemic disease with 140,936 cases globally. The outbreak of covid-19 occurred in over 184 countries, and it created a lot of medical and mental burdens. Aside from the physical problems, the mental health of the medical staff has been of critical concern. Aims and Objectives: To determine the prevalence of burnout among HCW dealing with COVID-19, identify the risk factors and find measures to support their mental health while dealing with the current and future pandemic. Methodology: A cross-sectional study was conducted among the HCW who fought against COVID-19 in SSRN Hospital in Mauritius. The HCWs were recruited using the snowballing sampling technique. Age, gender, job category, income, duration of vacation, working environment and importance of mental health were measured. Results: The prevalence of burnout was highest among HCA. Age had no significant association with pandemic-related burnout. In Mauritius, burnout during the pandemic is linked with lower income and having less vacation days. Conclusion: Burnout is prevalent among healthcare workers working during the Covid-19 Pandemic. Interventions such as psychological counselling, yoga and financial increments need to be implemented to help the healthcare workers.

Keywords: burnout, Covid-19, health care professionals, pandemic

Procedia PDF Downloads 76

Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

Abstract:

Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image

Procedia PDF Downloads 470

Authors: Tugce Talay, Kadir Erkan

Abstract:

In this study, the necessary steps for the design of axial flow permanent magnet motors are shown. The design and analysis of the engine were carried out based on ANSYS Maxwell program. The design parameters of the ANSYS Maxwell program and the artificial neural network system were established in MATLAB and the most efficient design parameters were found with the trained neural network. The results of the Maxwell program and the results of the artificial neural networks are compared and optimal working design parameters are found. The most efficient design parameters were submitted to the ANSYS Maxwell 3D design and the cogging torque was examined and design studies were carried out to reduce the cogging torque.

Keywords: AFPM, ANSYS Maxwell, cogging torque, design optimisation, efficiency, NNTOOL

Procedia PDF Downloads 212

Authors: Koray U. Erbas

Abstract:

Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

Procedia PDF Downloads 263

Authors: Zeina Merabi, Arij Dao

Abstract:

The temporal order of information processing in the brain is an important code in many acoustic signals, including speech, music, and animal vocalizations. Despite its significance, surprisingly little is known about its underlying cellular mechanisms and network manifestations. In the songbird telencephalic nucleus HVC, a subset of neurons shows temporal combination sensitivity (TCS). These neurons show a high temporal specificity, responding differently to distinct patterns of spectral elements and their combinations. HVC neuron types include basal-ganglia-projecting HVCX, forebrain-projecting HVCRA, and interneurons (HVC¬INT), each exhibiting distinct cellular, electrophysiological and functional properties. In this work, we develop conductance-based neural network models connecting the different classes of HVC neurons via different wiring scenarios, aiming to explore possible neural mechanisms that orchestrate the combination sensitivity property exhibited by HVCX, as well as replicating in vivo firing patterns observed when TCS neurons are presented with various auditory stimuli. The ionic and synaptic currents for each class of neurons that are presented in our networks and are based on pharmacological studies, rendering our networks biologically plausible. We present for the first time several realistic scenarios in which the different types of HVC neurons can interact to produce this behavior. The different networks highlight neural mechanisms that could potentially help to explain some aspects of combination sensitivity, including 1) interplay between inhibitory interneurons’ activity and the post inhibitory firing of the HVCX neurons enabled by T-type Ca2+ and H currents, 2) temporal summation of synaptic inputs at the TCS site of opposing signals that are time-and frequency- dependent, and 3) reciprocal inhibitory and excitatory loops as a potent mechanism to encode information over many milliseconds. The result is a plausible network model characterizing auditory processing in HVC. Our next step is to test the predictions of the model.

Keywords: combination sensitivity, songbirds, neural networks, spatiotemporal integration

Procedia PDF Downloads 60

Authors: Khadiza Akter, Tammy Greer, Raegan Bishop

Abstract:

An in-depth study of the psycho-social effects of gynecological health disparities among immigrant women in the USA is the goal of this integrative review paper. Immigrant women frequently encounter unique obstacles that have severe psycho-social repercussions when it comes to receiving high-quality gynecological treatment. The review investigates the interaction of psychological, sociological, and health factors that affect the psycho-social effects that immigrant women experience in recognition of the significance of addressing these imbalances. The difficulties that immigrant women face in providing high-quality gynecological treatment in the USA are examined in this study. These difficulties are caused by a variety of psychological issues, including acculturation stress and stigma, as well as by social problems like prejudice, language hurdles, and cultural norms. Additionally, variations in healthcare access and affordability have a role. This study highlights the particular challenges that immigrant women have in receiving high-quality gynecological treatment in the United States. These difficulties are caused by both social problems like language obstacles, cultural norms, and biases, as well as psychological ones like acculturation stress and stigma. Additionally, variations in gynecological care for immigrant women are greatly influenced by variances in healthcare availability and price. To find pertinent research looking at the psychological effects of gynecological health disparities among immigrant women in the USA, a thorough search of numerous databases was done. Numerous approaches, including mixed, quantitative, and qualitative ones, were used in the studies. The important findings from various investigations were extracted and synthesized after they underwent a careful evaluation. In order to lessen these discrepancies and enhance the overall well-being of immigrant women, healthcare professionals, legislators, and researchers must collaborate to create specialized treatments, regulations, and health system reforms.

Keywords: cultural barriers, gynecological health disparities, health care access, immigrant women, mental health, psycho-social consequences, social stigma

Procedia PDF Downloads 76

Authors: Mamouni Mohammed Dhiya Eddine

Abstract:

This work aims at studying the exploitation of high-speed networks of clusters for distributed storage. Parallel applications running on clusters require both high-performance communications between nodes and efficient access to the storage system. Many studies on network technologies led to the design of dedicated architectures for clusters with very fast communications between computing nodes. Efficient distributed storage in clusters has been essentially developed by adding parallelization mechanisms so that the server(s) may sustain an increased workload. In this work, we propose to improve the performance of distributed storage systems in clusters by efficiently using the underlying high-performance network to access distant storage systems. The main question we are addressing is: do high-speed networks of clusters fit the requirements of a transparent, efficient and high-performance access to remote storage? We show that storage requirements are very different from those of parallel computation. High-speed networks of clusters were designed to optimize communications between different nodes of a parallel application. We study their utilization in a very different context, storage in clusters, where client-server models are generally used to access remote storage (for instance NFS, PVFS or LUSTRE). Our experimental study based on the usage of the GM programming interface of MYRINET high-speed networks for distributed storage raised several interesting problems. Firstly, the specific memory utilization in the storage access system layers does not easily fit the traditional memory model of high-speed networks. Secondly, client-server models that are used for distributed storage have specific requirements on message control and event processing, which are not handled by existing interfaces. We propose different solutions to solve communication control problems at the filesystem level. We show that a modification of the network programming interface is required. Data transfer issues need an adaptation of the operating system. We detail several propositions for network programming interfaces which make their utilization easier in the context of distributed storage. The integration of a flexible processing of data transfer in the new programming interface MYRINET/MX is finally presented. Performance evaluations show that its usage in the context of both storage and other types of applications is easy and efficient.

Keywords: distributed storage, remote file access, cluster, high-speed network, MYRINET, zero-copy, memory registration, communication control, event notification, application programming interface

Procedia PDF Downloads 213

Authors: Van-Thanh Ho, Hyoungsoon Lee, Jaiyoung Ryu

Abstract:

Efficient pressure distribution within soft robotic systems, specifically to the pneumatic artificial muscle (PAM) regions, is essential to minimize energy consumption. This optimization involves adjusting reservoir pressure, pipe diameter, and branching network layout to reduce flow speed and pressure drop while enhancing flow efficiency. The outcome of this optimization is a lightweight power source and reduced mechanical impedance, enabling extended wear and movement. To achieve this, a branching network system was created by combining pipe components and intricate cross-sectional area variations, employing the principle of minimal work based on a complete virtual human exosuit. The results indicate that modifying the cross-sectional area of the branching network, gradually decreasing it, reduces velocity and enhances momentum compensation, preventing flow disturbances at separation regions. These optimized designs achieve uniform velocity distribution (uniformity index > 94%) prior to entering the connection pipe, with a pressure drop of less than 5%. The design must also consider the length-to-diameter ratio for fluid dynamic performance and production cost. This approach can be utilized to create a comprehensive PAM system, integrating well-designed tube networks and complex pneumatic models.

Keywords: pneumatic artificial muscles, pipe networks, pressure drop, compressible turbulent flow, uniformity flow, murray's law

Procedia PDF Downloads 69

Authors: Aderonke Oluseun Akinwumi, Olusola A. Komolaf

Abstract:

This paper gives information about the wide array of challenges facing both the electric utilities and consumers in the distribution system in developing countries, using Obafemi Awolowo University, Ile-Ife Nigeria as a case study. It also proffers cost-effective solution through remote monitoring, diagnostic and operation of distribution networks without compromising the system reliability. As utilities move from manned and unintelligent networks to completely unmanned smart grids, switching activities at substations and feeders will be managed and controlled remotely by dedicated systems hence this design. The Substation Remote Diagnostic and Operation Solution (sRDOs) would remotely monitor the load on Medium Voltage (MV) and Low Voltage (LV) feeders as well as distribution transformers and allow the utility disconnect non-paying customers with absolutely no extra resource deployment and without interrupting supply to paying customers. The aftermath of the implementation of this design improved the lifetime of key distribution infrastructure by automatically isolating feeders during overload conditions and more importantly erring consumers. This increased the ratio of revenue generated on electricity bills to total network load.

Keywords: electric utility, consumers, remote monitoring, diagnostic, system reliability, manned and unintelligent networks, unmanned smart grids, switching activities, medium voltage, low voltage, distribution transformer

Procedia PDF Downloads 123

Authors: Purva Joshi, Rohit Thanki, Omar Hanif

Abstract:

Unmanned aerial vehicles (UAVs) are becoming increasingly important in various industrial applications and sectors. Nowadays, a multi UAV network is used for specific types of communication (e.g., military) and monitoring purposes. Therefore, it is critical to reducing propagation delay during communication between UAVs, which is essential in a multi UAV network. This paper presents how the propagation delay between the base station (BS) and the UAVs is reduced using a searching algorithm. Furthermore, the iterative-based K-nearest neighbor (k-NN) algorithm and Travelling Salesmen Problem (TSP) algorthm were utilized to optimize the distance between BS and individual UAV to overcome the problem of propagation delay in multi UAV networks. The simulation results show that this proposed method reduced complexity, improved reliability, and reduced propagation delay in multi UAV networks.

Keywords: multi UAV network, optimal distance, propagation delay, K - nearest neighbor, traveling salesmen problem

Procedia PDF Downloads 190

Authors: Won-Seok Kim, Hyung-Ik Shin

Abstract:

Self-rated health (SRH) is a subjective assessment of individual health and has been identified as a strong predictor for mortality and morbidity. However few studies have been directed to the factors associated with SRH in persons with disabilities (PWD). We used data of 7th Korean national survey for 5307 PWD in 2008. Multiple logistic regression analysis was performed to find out independent risk factors for poor SRH in PWD. As a result, indicators of physical condition (poor instrumental ADL), socioeconomic disadvantages (poor education, economically inactive, low self-rated social class, medicaid in health insurance, presence of unmet need for hospital use) and social participation and networks (no use of internet service) were selected as independent risk factors for poor SRH in final model. Findings in the present study would be helpful in making a program to promote the health and narrow the gap of health status between the PWD.

Keywords: disabilities, risk factors, self-rated health, socioeconomic disadvantages, social networks

Procedia PDF Downloads 390

Authors: Chaitanya Chawla, Divya Panwar, Gurneesh Singh Anand, M. P. S Bhatia

Abstract:

This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.

Keywords: image forensics, computer graphics, classification, deep learning, convolutional neural networks

Procedia PDF Downloads 328

Authors: Osuagwu Justine Ugochukwu

Abstract:

Investigating the effects of transformational and transactional leadership styles on the performance of healthcare employees at the University Teaching Hospital (UNTH) in Enugu, Nigeria, was the goal of the research. The respondents were asked to fill out a structured questionnaire. The respondents were chosen using a straightforward random sampling technique and consisted of 370 health workers at the hospital. The result of the analysis revealed that transactional and transformational leadership style has a positive while ambidextrous leadership has a negative effect on healthcare workers' performance in UNTH, Enugu. Therefore, the management of public hospitals that have the capacity to change their top management approach to leadership styles will gain substantial support from their employees’ thereby increasing organizational commitment and performance among health workers. This will have remarkable social implications, one of which is a change in the work culture and attitude of medical personnel from the seemingly anti-community of patients to friendly engagement and treatment of patients leading to a harmonious coexistence among these individuals in society. Investigating ambidextrous leadership and the use of nonparametric analysis is unique and has brought brand-new knowledge to leadership literature.

Keywords: workers performance, transformational leadership, transactional leadership, governance quality, ambidextrous leadership

Procedia PDF Downloads 81

Authors: Harriet Bothwell, Lowri Evans, Kevin Jones

Abstract:

Background: The University of Bristol provides all medical students the opportunity to undertake student selected components (SSCs) at multiple stages of the undergraduate programme. SSCs enable students to explore areas of interest that are not necessarily covered by the curriculum. Students are required to produce a written report and most use SSCs as an opportunity to undertake an audit or small research project. In 2013 Swindon Academy, based at the Great Western Hospital, offered eight students the opportunity of a global health SSC which included a two week trip to rural hospital in Uganda. This SSC has since expanded and in 2017 a total of 20 students had the opportunity to undertake small research projects at two hospitals in rural Uganda. 'Tomorrows Doctors' highlights the importance of understanding healthcare from a 'global perspective' and student feedback from previous SSCs suggests that self-assessed knowledge of global health increases as a result of this SSC. Through the most recent version of this SSC students had the opportunity to undertake projects in a wide range of specialties including paediatrics, palliative care, surgery and medical education. Methods: An anonymous online questionnaire was made available to students following the SSC. There was a response rate of 80% representing 16 out of the 20 students. This questionnaire surveyed students’ satisfaction and experience of the SSC including the level of academic, project and spiritual support provided as well as perceived challenges in completing the project and barriers to healthcare delivery in the low resource setting. This survey had multiple open questions allowing the collection of qualitative data. Further qualitative data was collected from the students’ project report. The suggested format included a reflection and all students completed these. All qualitative data underwent thematic analysis. Results: All respondents rated the overall experience of the SSC as 'good' or 'excellent'. Preliminary data suggest that students’ confidence in their knowledge of global health, diagnosis of tropical diseases and management of tropical diseases improved after completing this SSC. Thematic analysis of students' reflection is ongoing but suggests that students gain far more than improved knowledge of tropical diseases. Students reflect positively on having the opportunity to research in a low resource setting and feel that by completing these projects they will be 'useful' to the hospital. Several students reflect the stark contrast to healthcare delivery in the UK and recognise the 'privilege' of having a healthcare system that is free at the point of access. Some students noted the different approaches that clinicians in Uganda had to train in 'taking ownership' of their own learning. Conclusions: Students completing this SSC report increased knowledge of global health and tropical medicine. However, their reflections reveal much broader learning outcomes and demonstrate considerable insight in multiple topics including conducting research in the low resource setting, training and healthcare inequality.

Keywords: global health, medical education, student feedback, undergraduate

Procedia PDF Downloads 118

Authors: Charlotte Bach, Albrecht Jahn, Mahnaz Motamedi, Maryam Karimi-Ghahfarokhi

Abstract:

Background: Maternal and infant deaths are most prevalent in the first month after birth, emphasizing the critical need for quality healthcare services during this period. Immigrant women, who are more susceptible to adverse pregnancy outcomes, often face neglect in accessing proper healthcare. The lack of adequate postpartum care significantly contributes to mortality rates. Therefore, utilizing maternal health care services and implementing postpartum care is crucial in reducing maternal and child mortality. Aims: This study aims to evaluate the assessment of pre- and postnatal care among women with and without migration background. In addition, the study explores the impact of COVID-19 procedures on women's experiences during pregnancy, birth, and the postpartum period. Methods: This research employs a cross-sectional Mixed-Method design. Data collection was facilitated through structured questionnaires administered to participants, alongside the utilization of patient bases, including Maternity and child medical records. Following the assumption that the investigator aimed to gain comprehensive insights, qualitative sampling focused on individuals with substantial experiences related to COVID-19, regarded as rich cases. Results: our study highlighted the influence of educational level, marital status, and consensual partnerships on the likelihood of Cesarean deliveries. Regarding breastfeeding practices, migrant women exhibited higher rates of breastfeeding initiation and continuation. Contraception utilization revealed interesting patterns, with non-migrants displaying higher odds of contraceptive use. The qualitative component of our research adds depth to the exploration of women's experiences during the COVID-19 pandemic, revealing nuanced challenges related to anxiety, hospital restrictions, breastfeeding support, and postnatal ward routines. Conclusion: Dissimilarity among studies toward cesarean rate between migrants and non-migrants underscores the importance of targeted interventions considering the diverse needs of distinct population groups. It also acknowledges potential cultural, contextual, and healthcare system influences on the association between mode of delivery and infant feeding practices. Studies acknowledge the influence of contextual variables on contraceptive preferences among migrants and non-migrants, emphasizing the need for tailored healthcare policies. The findings contribute to existing research, highlighting the need for a nuanced understanding of the impact of birth preparation courses on maternal and infant outcomes. Furthermore, they emphasize the universality of certain maternity care experiences, regardless of pandemic contexts, reinforcing the importance of patient-centred approaches in healthcare delivery.

Keywords: migration background, pregnancy outcome, covid-19, postpartum

Procedia PDF Downloads 47

Authors: Ulas Vural, M. Ergun Okay, E. Mesut Yildiz

Abstract:

Telecommunication service providers demand accurate and precise prediction of customer churn probabilities to increase the effectiveness of their customer relation services. The large amount of customer data owned by the service providers is suitable for analysis by machine learning methods. In this study, expenditure data of customers are analyzed by using an artificial neural network (ANN). The ANN model is applied to the data of customers with different billing duration. The proposed model successfully predicts the churn probabilities at 83% accuracy for only three months expenditure data and the prediction accuracy increases up to 89% when the nine month data is used. The experiments also show that the accuracy of ANN model increases on an extended feature set with information of the changes on the bill amounts.

Keywords: customer relationship management, churn prediction, telecom industry, deep learning, artificial neural networks

Procedia PDF Downloads 139

Authors: Richard Molyneux, Christopher Parrott, Kirill Horoshenkov

Abstract:

This paper aims to demonstrate how various algorithms can be implemented within swarms of autonomous robots to provide continuous inspection within underground pipeline networks. Current methods of fault detection within pipes are costly, time consuming and inefficient. As such, solutions tend toward a more reactive approach, repairing faults, as opposed to proactively seeking leaks and blockages. The paper presents an efficient inspection method, showing that autonomous swarm robotics is a viable way of monitoring underground infrastructure. Tailored adaptations of various Vehicle Routing Problems (VRP) and path-planning algorithms provide a customised inspection procedure for complicated networks of underground pipes. The performance of multiple algorithms is compared to determine their effectiveness and feasibility. Notable inspirations come from ant colonies and stigmergy, graph theory, the k-Chinese Postman Problem ( -CPP) and traffic theory. Unlike most swarm behaviours which rely on fast communication between agents, underground pipe networks are a highly challenging communication environment with extremely limited communication ranges. This is due to the extreme variability in the pipe conditions and relatively high attenuation of acoustic and radio waves with which robots would usually communicate. This paper illustrates how to optimise the inspection process and how to increase the frequency with which the robots pass each other, without compromising the routes they are able to take to cover the whole network.

Keywords: autonomous inspection, buried pipes, stigmergy, swarm intelligence, vehicle routing problem

Procedia PDF Downloads 162

Authors: Neda Seyyedi, Reza Berangi

Abstract:

Voice over IP (VOIP) network, also known as Internet telephony, is growing increasingly having occupied a large part of the communications market. With the growth of each technology, the related security issues become of particular importance. Taking advantage of this technology in different environments with numerous features put at our disposal, there arises an increasing need to address the security threats. Being IP-based and playing a signaling role in VOIP networks, Session Initiation Protocol (SIP) lets the invaders use weaknesses of the protocol to disable VOIP service. One of the most important threats is denial of service attack, a branch of which in this article we have discussed as flooding attacks. These attacks make server resources wasted and deprive it from delivering service to authorized users. Distributed denial of service attacks and attacks with a low rate can mislead many attack detection mechanisms. In this paper, we introduce a mechanism which not only detects distributed denial of service attacks and low rate attacks, but can also identify the attackers accurately. We detect and prevent flooding attacks in SIP protocol using Shannon (FDP-S), Renyi (FDP-R) and Tsallis (FDP-T) entropy. We conducted an experiment to compare the percentage of detection and rate of false alarm messages using any of the Shannon, Renyi and Tsallis entropy as a measure of disorder. Implementation results show that, according to the parametric nature of the Renyi and Tsallis entropy, by changing the parameters, different detection percentages and false alarm rates will be gained with the possibility to adjust the sensitivity of the detection mechanism.

Keywords: VOIP networks, flooding attacks, entropy, computer networks

Procedia PDF Downloads 400

Authors: Guenther Schuh, Sebastian Woelk

Abstract:

Companies face increasing challenges in research due to higher costs and risks. The intensifying technology complexity and interdisciplinarity require unique know-how. Therefore, companies need to decide whether research shall be conducted internally or externally with partners. On the other hand, research institutes meet increasing efforts to achieve good financing and to maintain high research reputation. Therefore, relevant research topics need to be identified and specialization of competency is necessary. However, additional competences for solving interdisciplinary research projects are also often required. Secured financing can be achieved by bonding industry partners as well as public fundings. The realization of faster and better research drives companies and research institutes to cooperate in organized research networks, which are managed by an administrative organization. For an effective and efficient cooperation, necessary processes, roles, tools and a set of rules need to be determined. The goal of this paper is to show the state-of-art research and to propose a governance framework for organized research networks.

Keywords: interorganizational cooperation, design of network governance, research network

Procedia PDF Downloads 363

Authors: Aydin Teymourifar, Gurkan Ozturk

Abstract:

In this paper, a distributed hybrid algorithm is proposed for solving the job shop scheduling problem. The suggested method executes different artificial neural networks, heuristics and meta-heuristics simultaneously on more than one machine. The neural networks are used to control the constraints of the problem while the meta-heuristics search the global space and the heuristics are used to prevent the premature convergence. To attain an efficient distributed intelligent method for solving big and distributed job shop scheduling problems, Apache Spark and Hadoop frameworks are used. In the algorithm implementation and design steps, new approaches are applied. Comparison between the proposed algorithm and other efficient algorithms from the literature shows its efficiency, which is able to solve large size problems in short time.

Keywords: distributed algorithms, Apache Spark, Hadoop, job shop scheduling, neural network

Procedia PDF Downloads 382

Authors: Ahmadu Maidorawa, Kamalrulnizam Abu Bakar

Abstract:

This paper proposed an Enhanced Connectivity Aware Routing (ECAR) protocol for Vehicular Ad hoc Network (VANET). The protocol uses a control broadcast to reduce the number of overhead packets needed in a route discovery process. It is also equipped with an alternative backup route that is used whenever a primary path to destination failed, which highly reduces the frequent launching and re-launching of the route discovery process that waste useful bandwidth and unnecessarily prolonging the average packet delay. NS2 simulation results show that the performance of ECAR protocol outperformed the original connectivity aware routing (CAR) protocol by reducing the average packet delay by 28%, control overheads by 27% and increased the packet delivery ratio by 22%.

Keywords: alternative path, primary path, protocol, routing, VANET, vehicular ad hoc networks

Procedia PDF Downloads 395

Authors: AmirHossein Mohajerzadeh, Mohammad Alishahi, Saeed Aslishahi, Mohsen Zabihi

Abstract:

In this paper, we consider sensor nodes with the capability of measuring the bearings (relative angle to the target). We use geometric methods to select a set of observer nodes which are responsible for collecting data from the target. Considering the characteristics of target tracking applications, it is clear that significant numbers of sensor nodes are usually inactive. Therefore, in order to minimize the total network energy consumption, a set of sensor nodes, called sentinel, is periodically selected for monitoring, controlling the environment and transmitting data through the network. The other nodes are inactive. Furthermore, the proposed algorithm provides a joint scheduling and routing algorithm to transmit data between network nodes and the fusion center (FC) in which not only provides an efficient way to estimate the target position but also provides an efficient target tracking. Performance evaluation confirms the superiority of the proposed algorithm.

Keywords: coverage, routing, scheduling, target tracking, wireless sensor networks

Procedia PDF Downloads 372

Authors: Xiyang Li, Qi Yu, Lun Zhang

Abstract:

In the face of natural disasters and other emergency situations, determining the optimal location of rescue centers is crucial for improving rescue efficiency and minimizing impact on affected populations. This paper proposes a method that integrates genetic algorithms (GA) and backpropagation neural networks (BPNN) to address the site selection optimization problem for emergency rescue centers. We utilize BPNN to accurately estimate the cost of delivering supplies from rescue centers to each temporary camp. Moreover, a genetic algorithm with a special partially matched crossover (PMX) strategy is employed to ensure that the number of temporary camps assigned to each rescue center adheres to predetermined limits. Using the population distribution data during the 2022 epidemic in Jiading District, Shanghai, as an experimental case, this paper verifies the effectiveness of the proposed method. The experimental results demonstrate that the BPNN-GA method proposed in this study outperforms existing algorithms in terms of computational efficiency and optimization performance. Especially considering the requirements for computational resources and response time in emergency situations, the proposed method shows its ability to achieve rapid convergence and optimal performance in the early and mid-stages. Future research could explore incorporating more real-world conditions and variables into the model to further improve its accuracy and applicability.

Keywords: emergency rescue centers, genetic algorithms, back-propagation neural networks, site selection optimization

Procedia PDF Downloads 72

Authors: Laura Bravo-García, Gotzone Barandika, Begoña Bazán, M. Karmele Urtiaga, Luis M. Lezama, María I. Arriortua

Abstract:

Solid coordination networks (SCNs) are materials consisting of metal ions or clusters that are linked by polyfunctional organic ligands and can be designed to form tridimensional frameworks. Their structural features, as for example high surface areas, thermal stability, and in other cases large cavities, have opened a wide range of applications in fields like drug delivery, host-guest chemistry, biomedical imaging, chemical sensing, heterogeneous catalysis and others referred to greenhouse gases storage or even separation. In this sense, the use of polycarboxylate anions and dipyridyl ligands is an effective strategy to produce extended structures with the needed characteristics for these applications. In this context, a novel compound, [Cu4(m-BDC)4(bpa)2DMF]•DMF has been obtained by microwave synthesis, where m-BDC is 1,3-benzenedicarboxylate and bpa 1,2-bis(4-pyridyl)ethane. The crystal structure can be described as a three dimensional framework formed by two equal, interpenetrated networks. Each network consists of two different CuII dimers. Dimer 1 have two coppers with a square pyramidal coordination, and dimer 2 have one with a square pyramidal coordination and other with octahedral one, the last dimer is unique in literature. Therefore, the combination of both type of dimers is unprecedented. Thus, benzenedicarboxylate ligands form sinusoidal chains between the same type of dimers, and also connect both chains forming these layers in the (100) plane. These layers are connected along the [100] direction through the bpa ligand, giving rise to a 3D network with 10 Å2 voids in average. However, the fact that there are two interpenetrated networks results in a significant reduction of the available volume. Structural analysis was carried out by means of single crystal X-ray diffraction and IR spectroscopy. Thermal and magnetic properties have been measured by means of thermogravimetry (TG), X-ray thermodiffractometry (TDX), and electron paramagnetic resonance (EPR). Additionally, CO2 and CH4 high pressure adsorption measurements have been carried out for this compound.

Keywords: gas adsorption, interpenetrated networks, magnetic measurements, solid coordination network (SCN), thermal stability

Procedia PDF Downloads 315