Search results for: healthcare networks

Authors: Shana Malio-Satele, Lemalu Silao Vaisola Sefo

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Introductory Statement: South Seas Healthcare is Ōtara’s largest Pacific health provider in South Auckland, New Zealand. Our vision is excellent health and well-being for Pacific people and all communities through strong Pacific values. During the DELTA and Omicron outbreak of COVID-19, our Pacific people, indigenous Māori, and the community of South Auckland were disproportionately affected and faced significant hardship with existing inequities magnified. This study highlights the community-based learnings of harnessing community-based strengths such as indigenous resilience, family-informed experiences and stories that provide critical insights that inform health reform changes that will be sustainable and equitable for all indigenous populations. This study is based on critical learnings acquired during COVID-19 that challenge the deficit narrative common in healthcare about indigenous populations. This study shares case studies of marginalised groups and religious groups and the successful application of indigenous cultural strengths, such as collectivism, positive protective factors, and using trusted relationships to create meaningful change in the way healthcare is delivered. The significance of this study highlights the critical conditions needed to adopt a community-informed way of creating integrated healthcare that works and the role that the community can play in being part of the solution. Methodologies: Key methodologies utilised are indigenous and Pacific-informed. To achieve critical learnings from the community, Pacific research methodologies, heavily informed by the Polynesian practice, were applied. Specifically, this includes; Teu Le Va (Understanding the importance of trusted relationships as a way of creating positive health solutions); The Fonofale Methodology (A way of understanding how health incorporates culture, family, the physical, spiritual, mental and other dimensions of health, as well as time, context and environment; The Fonua Methodology – Understanding the overall wellbeing and health of communities, families and individuals and their holistic needs and environmental factors and the Talanoa methodology (Researching through conversation, where understanding the individual and community is through understanding their history and future through stories). Major Findings: Key findings in the study included: 1. The collectivist approach in the community is a strengths-based response specific to populations, which highlights the importance of trusted relationships and cultural values to achieve meaningful outcomes. 2. The development of a “village model” which identified critical components to achieving health reform change; system navigation, a sense of service that was culturally responsive, critical leadership roles, culturally appropriate support, and the ability to influence the system enablers to support an alternative way of working. Concluding Statement: There is a strong connection between community-based strengths being implemented into healthcare strategies and reforms and the sustainable success of indigenous populations and marginalised communities accessing services that are cohesive, equitably resourced, accessible and meaningful for families. This study highlights the successful community-informed approaches and practices used during the COVID-19 response in New Zealand that are now being implemented in the current health reform.

Keywords: indigenous voice, community voice, health reform, New Zealand

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Authors: Rujijan Vichivanives

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This research aims to create a knowledge-based system as a database for self-healthcare analysis, diagnosis of simple illnesses, and the use of Thai herbs instead of modern medicine by using principles of Thai traditional medication theory. These were disseminated by website network programs within Suan Sunandha Rajabhat University. The population used in this study was divided into two groups: the first group consisted of four experts of Thai traditional medication and the second group was 300 website users. The methods used for collecting data were paper questionnaires and poll questionnaires on the website. The statistics used for analyzing data was at an average level. The results were divided into three parts: the first part was the development of a knowledge-based system and the second part was applied programs on website. Both parts could be fulfilled and achieved according to the set goal. The third part was the evaluation of the study: The evaluation of the viewpoints of the experts towards website designs were evaluated at a good level of 4.20. The satisfaction evaluation of the users was found at a good level of average satisfactory level at 4.24. It was found that the young population of those under the age of 16 had less cares about their health than the population of other teenagers, working age adults and those of older age. The research findings should be extended in order to encourage the lifestyle modifications to people of all ages by using the self-healthcare principles.

Keywords: developing, herbs, knowledge-based system, medical treatment

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Authors: Oyebade K. Oyedotun

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The cardiotocogram is a technical recording of the heartbeat rate and uterine contractions of a fetus during pregnancy. During pregnancy, several complications can occur to both the mother and the fetus; hence it is very crucial that medical experts are able to find technical means to check the healthiness of the mother and especially the fetus. It is very important that the fetus develops as expected in stages during the pregnancy period; however, the task of monitoring the health status of the fetus is not that which is easily achieved as the fetus is not wholly physically available to medical experts for inspection. Hence, doctors have to resort to some other tests that can give an indication of the status of the fetus. One of such diagnostic test is to obtain cardiotocograms of the fetus. From the analysis of the cardiotocograms, medical experts can determine the status of the fetus, and therefore necessary medical interventions. Generally, medical experts classify examined cardiotocograms into ‘normal’, ‘suspect’, or ‘pathological’. This work presents an artificial neural network based decision support system which can filter cardiotocograms data, producing the corresponding statuses of the fetuses. The capability of artificial neural network to explore the cardiotocogram data and learn features that distinguish one class from the others has been exploited in this research. In this research, feedforward and radial basis neural networks were trained on a publicly available database to classify the processed cardiotocogram data into one of the three classes: ‘normal’, ‘suspect’, or ‘pathological’. Classification accuracies of 87.8% and 89.2% were achieved during the test phase of the trained network for the feedforward and radial basis neural networks respectively. It is the hope that while the system described in this work may not be a complete replacement for a medical expert in fetus status evaluation, it can significantly reinforce the confidence in medical diagnosis reached by experts.

Keywords: decision support, cardiotocogram, classification, neural networks

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Authors: Sachin Sharma

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A wireless sensor network (WSN) consists of a large number of sensor nodes which are deployed over an area to perform local computations based on information gathered from the surroundings. With the increasing demand for real-time applications in WSN, real-time critical events anticipate an efficient quality-of-service (QoS) based routing for data delivery from the network infrastructure. Hence, maximizing the lifetime of the network through minimizing the energy is an important challenge in WSN; sensors cannot be easily replaced or recharged due to their ad-hoc deployment in a hazardous environment. Considerable research has been focused on developing robust energy efficient QoS based routing protocols. The main focus of this article is primarily on periodical cycling schemes which represent the most compatible technique for energy saving and we also focus on the data-driven approaches that can be used to improve the energy efficiency. Finally, we will make a review on some communication protocols proposed for sensor networks.

Keywords: energy efficient, quality of service, wireless sensor networks, MAC

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Authors: Victor Radich, Tania Basso, Regina Moraes

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Lead qualification is one of the main procedures in Customer Relationship Management (CRM) projects. Its main goal is to identify potential consumers who have the ideal characteristics to establish a profitable and long-term relationship with a certain organization. Social networks can be an important source of data for identifying and qualifying leads since interest in specific products or services can be identified from the users’ expressed feelings of (dis)satisfaction. In this context, this work proposes the use of machine learning techniques and sentiment analysis as an extra step in the lead qualification process in order to improve it. In addition to machine learning models, sentiment analysis or opinion mining can be used to understand the evaluation that the user makes of a particular service, product, or brand. The results obtained so far have shown that it is possible to extract data from social networks and combine the techniques for a more complete classification.

Keywords: lead qualification, sentiment analysis, opinion mining, machine learning, CRM, lead scoring

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Authors: Rashid Ahmed , John N. Avaritsiotis

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Many signal subspace-based approaches have already been proposed for determining the fixed Direction of Arrival (DOA) of plane waves impinging on an array of sensors. Two procedures for DOA estimation based neural networks are presented. First, Principal Component Analysis (PCA) is employed to extract the maximum eigenvalue and eigenvector from signal subspace to estimate DOA. Second, minor component analysis (MCA) is a statistical method of extracting the eigenvector associated with the smallest eigenvalue of the covariance matrix. In this paper, we will modify a Minor Component Analysis (MCA(R)) learning algorithm to enhance the convergence, where a convergence is essential for MCA algorithm towards practical applications. The learning rate parameter is also presented, which ensures fast convergence of the algorithm, because it has direct effect on the convergence of the weight vector and the error level is affected by this value. MCA is performed to determine the estimated DOA. Preliminary results will be furnished to illustrate the convergences results achieved.

Keywords: Direction of Arrival, neural networks, Principle Component Analysis, Minor Component Analysis

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Authors: Madhukar Kasarla, Sumit Fogla, Kiran Panuganti, Gaurav Jain, Abhijit Ramanujam, Astha Jain, Shashank Kraleti, Sharat Musham, Arun Chaudhury

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Patients have often chosen to travel for care — making pilgrimages to academic meccas and state-of-the-art hospitals for sophisticated surgery. This culture is still persistent in the landscape of US healthcare, with hundred thousand of visitors coming to the shores of United States to seek the high quality of medical care. One of the major challenges in this form of medical tourism has been the language barrier. Thus, an Iraqi patient, with immediate needs of communicating the healthcare needs to the treating team in the hospital, may face huge barrier in effective patient-doctor communication, delaying care and even at times reducing the quality. To circumvent these challenges, we are proposing the use of a state-of-the-art tool, Talk2All, which can translate nearly one hundred international languages (and even sign language) in real time. The tool is an easy to download app and highly user friendly. It builds on machine learning principles to decode different languages in real time. We suggest that the use of Talk2All will tremendously enhance communication in the hospital setting, effectively breaking the language barrier. We propose that vigorous incorporation of Talk2All shall overcome practical challenges in international medical and surgical tourism.

Keywords: language translation, communication, machine learning, medical tourism

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Authors: Mai Ali

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Advancing beyond the junior stage of a paediatrician’s career is a crucial step where they accumulate essential skills and knowledge. This process prepares them for the challenges they'll encounter throughout their profession, particularly in dealing with paediatric emergencies. This can be especially demanding for trainees specializing in fields like endocrinology, particularly in the management of Diabetic Ketoacidosis (DKA) in the UK. In different societal contexts, junior doctors, whether specializing in pediatrics or other medical fields, are generally expected to possess a fundamental level of knowledge and skills necessary for managing diabetic ketoacidosis (DKA) emergencies. These physicians consistently concurred in recognizing prevalent problems in the healthcare facilities they examined. Such issues include the lack of established guidelines for DKA treatment and the inadequate availability of comprehensive training opportunities. The abstract underscores the critical importance of junior paediatricians acquiring expertise in managing paediatric emergencies, with a specific focus on DKA. Commonly, issues like the lack of standardized protocols and training deficiencies are recurring themes across healthcare facilities. This research proposal aims to conduct a thematic analysis of the proficiency of paediatric trainees in the United Kingdom when handling DKA in various clinical contexts. The primary goal is to assess their competency and suggest effective strategies for comprehensive DKA training improvement.

Keywords: junior pediatrician, DKA, standardized protocols, level of competence

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Authors: Regan Harper, Song Yang, Douglas Adams

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During the historically unprecedent time of Covid-19 pandemic, we survey college students with social issue generators to measure their core discussion groups. For the total 191 students, we elicit 847 conversation partners (alters) with our five social issue generators such as school closing, facemasks, collegiate sports, race and policing, and social inequality, producing an average of 4.43 alters per respondent. The core discussion groups of our sample are very gender balanced, with female alters slightly outnumbering male alters. However, the core discussion groups are racially homogenous, consisting of mostly white students (around or above 80 percent). Explanatory analyses reveal that gender and race of respondents significantly impact the size, gender composition, and racial composition of their core discussion networks. We discuss those major findings and implications of future studies in our conclusion section.

Keywords: core discussion groups, social issue generators, ego-centric network, Covid-19 pandemic

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Authors: M. R. Ebrahimi, B. Mahdaviani

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Conventional electric distribution systems are radial in nature, supplied at one end through a main source. These networks generally have a simple protection system usually implemented using fuses, re-closers, and over-current relays. Recently, great attention has been paid to applying Distributed energy resources (DERs) throughout electric distribution systems. Presence of such generation in a network leads to losing coordination of protection devices. Therefore, it is desired to develop an algorithm which is capable of protecting distribution systems that include DER. On the other hand smart grid brings opportunities to the power system. Fast advancement in communication and measurement techniques accelerates the development of multi agent system (MAS). So in this paper, a new approach for the protection of distribution networks in the presence of DERs is presented base on MAS. The proposed scheme has been implemented on a sample 27-bus distribution network.

Keywords: distributed energy resource, distribution network, protection, smart grid, multi agent system

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Authors: Lee Myung-Won, Kwak Keun-Chang

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This paper is concerned with knowledge representation and extraction of fuzzy if-then rules using Interval Type-2 Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of fuzzy granulation. This proposed clustering algorithm is based on information granulation in the form of IT2 based Fuzzy C-Means (IT2-FCM) clustering and estimates the cluster centers by preserving the homogeneity between the clustered patterns from the IT2 contexts produced in the output space. Furthermore, we can obtain the automatic knowledge representation in the design of Radial Basis Function Networks (RBFN), Linguistic Model (LM), and Adaptive Neuro-Fuzzy Networks (ANFN) from the numerical input-output data pairs. We shall focus on a design of ANFN in this paper. The experimental results on an estimation problem of energy performance reveal that the proposed method showed a good knowledge representation and performance in comparison with the previous works.

Keywords: IT2-FCM, IT2-CFCM, context-based fuzzy clustering, adaptive neuro-fuzzy network, knowledge representation

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Authors: Haowei Chen, Kaiqi Xiong

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This paper aims to answer how malware will propagate in Wireless Mesh Networks (WMNs) and how communication radius and distributed density of nodes affects the process of spreading. The above analysis is essential for devising network-wide strategies to counter malware. We answer these questions by developing an improved dynamical system that models malware propagation in the area where nodes were uniformly distributed. The proposed model captures both the spatial and temporal dynamics regarding the malware spreading process. Equilibrium and stability are also discussed based on the threshold of the system. If the threshold is less than one, the infected nodes disappear, and if the threshold is greater than one, the infected nodes asymptotically stabilize at the endemic equilibrium. Numerical simulations are investigated about communication radius and distributed density of nodes in WMNs, which allows us to draw various insights that can be used to guide security defense.

Keywords: Bluetooth security, malware propagation, wireless mesh networks, stability analysis

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Authors: Ntokozo C. Mthembu

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The notion of sustainable chemistry has become significant in the discourse for a global post-colonial era, including South Africa, especially when it comes to access to the general health system and related policies in relation to disease or ease of human life. In view of the stubborn vestiges of coloniality in the daily lives of indigenous African people in general, the fundamentals of present Western medical and traditional medicine systems and related policies in the democratic era were examined in this study. The situation of traditional healers in relation to current policy was also reviewed. The advent of democracy in South Africa brought about a variety of development opportunities and limitations, particularly with respect to indigenous African knowledge systems such as traditional medicine. There were high hopes that the limitations of previous narrow cultural perspectives would be rectified in the democratic era through development interventions, but some sections of society, such as traditional healers, remain marginalised. The Afrocentric perspective was explored in dissecting government interventions related to traditional medicine. This article highlights that multiple medical systems should be adopted and that health policies should be aligned in order to guarantee mutual respect and to address the remnants of colonialism in South Africa, Africa and the broader global community.

Keywords: traditional healing system, healers, pluralist healthcare system, post-colonial era

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Authors: Tugba Bayoglu

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Aerodynamic parameter estimation is very crucial in missile design phase, since accurate high fidelity aerodynamic model is required for designing high performance and robust control system, developing high fidelity flight simulations and verification of computational and wind tunnel test results. However, in literature, there is not enough missile aerodynamic parameter identification study for three main reasons: (1) most air to air missiles cannot fly with constant speed, (2) missile flight test number and flight duration are much less than that of fixed wing aircraft, (3) variation of the missile aerodynamic parameters with respect to Mach number is higher than that of fixed wing aircraft. In addition to these challenges, identification of aerodynamic parameters for high wind angles by using classical estimation techniques brings another difficulty in the estimation process. The reason for this, most of the estimation techniques require employing polynomials or splines to model the behavior of the aerodynamics. However, for the missiles with a large variation of aerodynamic parameters with respect to flight variables, the order of the proposed model increases, which brings computational burden and complexity. Therefore, in this study, it is aimed to solve nonlinear aerodynamic parameter identification problem for a supersonic air to air missile by using Artificial Neural Networks. The method proposed will be tested by using simulated data which will be generated with a six degree of freedom missile model, involving a nonlinear aerodynamic database. The data will be corrupted by adding noise to the measurement model. Then, by using the flight variables and measurements, the parameters will be estimated. Finally, the prediction accuracy will be investigated.

Keywords: air to air missile, artificial neural networks, open loop simulation, parameter identification

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Authors: Vladimir Siska, Lukas Kober

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Introduction: The research deals with the work satisfaction of nurses working in healthcare institutions in the Slovak Republic, and factors influencing it. Employers should create working conditions that are consonant with the requirements of their employees and make the most of motivation strategies to help them answer to the employess' needs in concordance with various needs and motivation process theories. Methodology: In our research, we aimed to investigate the level of work satisfaction in nurses by carrying out a quantitative analysis using the standardized McCloskey/Mueller Satisfaction scale questionnaire. We used the descriptive positioning characteristics (average, median and variability, standard deviation, minimum and maximum) to process the collected data and, to verify our hypotheses; we employed the double-selection Student T-test, Mann-Whitney U test, and a one-way analysis of variance (One-way ANOVA). Results: Nurses´satisfaction with external rewards is influenced by their age, years of experience, and level of completed education, with all of the abovementioned factors also impacting on the nurses' satisfaction with their work schedule. The type of founding authority of the healthcare institution also constitutes an influence on the nurses' satisfaction concerning relationships in the workplace. Conclusion: The feelling of work dissatisfaction can influence employees in many ways, e.g., it can take the form of burn-out syndrome, absenteeism, or increased fluctuation. Therefore, it is important to pay increased attention to all employees of an organisation, regardless of their position.

Keywords: motivation, nurse, work satisfaction, McCloskey/Mueller satisfaction scale

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Authors: Mera Ababneh, Sayer Al-Azzam, Karem Alzoubi, Abeer Rababa'h

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Background: Medication errors are among the most common medical errors. Their occurrences result in patient’s mortality, morbidity, and additional healthcare costs. Continuous monitoring and detection is required. Objectives: The aim of this study was to compare medication errors in outpatient’s prescriptions in two different hospitals (paper system vs. electronic system). Methods: This was a cross sectional observational study conducted in two major hospitals; King Abdullah University Hospital (KAUH) and Princess Bassma Teaching Hospital (PBTH) over three months period. Data collection was conducted by two trained pharmacists at each site. During the study period, medication prescriptions and dispensing procedures were screened for medication errors in both participating centers by two trained pharmacist. Results: In the electronic prescription hospital, 2500 prescriptions were screened in which 631 medication errors were detected. Prescription errors were 231 (36.6%), and dispensing errors were 400 (63.4%) of all errors. On the other side, analysis of 2500 prescriptions in paper-based hospital revealed 3714 medication errors, of which 288 (7.8%) were prescription errors, and 3426 (92.2%) were dispensing errors. A significant number of 2496 (67.2%) were inadequately and/or inappropriately labeled. Conclusion: This study provides insight for healthcare policy makers, professionals, and administrators to invest in advanced technology systems, education, and epidemiological surveillance programs to minimize medication errors.

Keywords: medication errors, prescription errors, dispensing errors, electronic prescription, handwritten prescription

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Authors: Nana Benma Osei

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Patient discharge can be a hectic process. Patients are sometimes sent to the wrong location or forgotten in lounges in the waiting room. This ends up compromising patient care because the delay in picking the patients can affect how they adhere to medication. Patients may fail to take their medication, and this will lead to negative outcomes. The situation highlights the demands of modern-day healthcare, and the use of technology can help in reducing such challenges and in enhancing the patient’s experience, leading to greater satisfaction with the care provided. The paper contains the proposed changes to a healthcare facility by introducing the clinical decision support system, which will be needed to improve coordination and communication during patient discharge. This will be done under Kurt Lewin’s Change Management Model, which recognizes the different phases in the change process. A pilot program is proposed initially before the program can be implemented in the entire organization. This allows for the identification of challenges and ways of managing them. The paper anticipates some of the possible challenges that may arise during implementation, and a multi-disciplinary approach is considered the most effective. Opposition to the change is likely to arise because staff members may lack information on how the changes will affect them and the skills they will need to learn to use the new system. Training will occur before the technology can be implemented. Every member will go for training, and adequate time is allocated for training purposes. A comparison of data will determine whether the project has succeeded.

Keywords: patient discharge, clinical decision support system, communication, collaboration

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Authors: J. Milton, C. Uzoigwe, O. Ayeko, B. Offorha, K. Anderson, R. G. Middleton

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Background: We used the UK National Hip Fracture database to determine the effect of institution hip fracture case volume on hip fracture healthcare outcomes in 2019. Using logistic regression for each healthcare outcome, we compared the best performing 50 units with the poorest performing 50 units in order to determine if the unit volume was associated with performance for each particular outcome. Method: We analysed 175 institutions treating a total of 67,673 patients over the course of a year. Results: The number of hip fractures seen per unit ranged between 86 and 952. Larger units tendered to perform health assessments more consistently and mobilise patients more expeditiously post-operatively. Patients treated at large institutions had shorter lengths of stay. With regard to most other outcomes, there was no association between unit case volume and performance, notably compliance with the Best Practice Tariff, time to surgery, proportion of eligible patients undergoing total hip arthroplasty, length of stay, delirium risk, and pressure sore risk assessments. Conclusion: There is no relationship between unit volume and the majority of health care outcomes. It would seem that larger institutions tend to perform better at parameters that are dependent upon personnel numbers. However, where the outcome is contingent, even partially, on physical infrastructure capacity, there was no difference between larger and smaller units.

Keywords: institution volume, mortality, neck of femur fractures, osteoporosis

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Authors: B. Kirubakaran, C. Rajasekaran

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Reconfigurable techniques are used in many engineering and industrial applications for the efficient data transmissions through the wireless sensor networks. Nowadays most of the industrial applications are work for try to minimize the size and cost. During runtime the reconfigurable technique avoid the unwanted hang and delay in the system performance. In recent world Field Programmable Gate Array (FPGA) as one of the most efficient reconfigurable device and widely used for most of the hardware and software reconfiguration applications. In this paper, the work deals with whatever going to make changes in the hardware and software during runtime it’s should not affect the current running process that’s the main objective of the paper our changes be done in a parallel manner at the same time concentrating the cost and power transmission problems during data trans-receiving. Analog sensor (Temperature) as an input for the controller (PIC) through that control the FPGA digital sensors in generalized manner.

Keywords: field programmable gate array, peripheral interrupt controller, runtime reconfigurable techniques, wireless sensor networks

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Authors: Iris Xiaohong Quan, Sharon Qi, Kelly Tianqin Shi

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This study focuses on the critical role of insurance policies in a world grappling with increasing mental health challenges, as they significantly influence the dynamics of healthcare businesses and entrepreneurial ventures. The paper utilizes the mental health sector as a case to examine the impact of insurance policies on healthcare service providers, entrepreneurs, and individuals seeking mental health support. This paper addressed the following research questions: To what extent do changes in insurance reimbursement policies affect the accessibility and affordability of mental health services for patients, and how does this impact the overall demand for such services? What are the barriers and opportunities that mental health entrepreneurs face and what strategies and adaptations do mental health businesses employ when navigating the evolving landscape of insurance reimbursement policies? How do changes in insurance reimbursement policies, specifically related to mental health services, influence the financial viability and sustainability of mental health clinics and private practices? Employing a self-designed survey aimed at autism spectrum disorder (ASD) treatment companies, alongside two in-depth case studies and an analysis of pertinent insurance policies and documents, this research aims to elucidate the multifaceted influence of insurance policies on the mental health industry. The findings from this study reveal how insurance policies shape the landscape of mental health businesses and their operations. A total of 821 autism treatment organizations or offices were contacted by telephone between November 1, 2019, and January 31, 2020. About half of the offices (53.33%) were established in the past five years, and 80% were established in the past 15 years. There is a significant increase in the establishment of ABA service centers in the recent two decades as a result of autism insurance reform, the increasing social awareness of ASD, and the redefinition of autism. In addition, almost half of the ABA service providers we surveyed had a patient size ranging from 20 to 50 in the year when the residence state passed the legislation for autism insurance coverage. On average, an ABA service provider works with 5.3 insurance companies. This research find that insurance is the main source of revenue for most ABA service providers. However, our survey reveals that clients’ out of pocket payment has been the second main revenue sources. Despite the changes of regulations and insurance policies in all states, clients still have to pay a fraction of, if not all, the ABA treatment service fees out of pocket. This research shows that some ABA service providers seek federal and government funds and grants to support their services and businesses. Our further analysis with the in-depth case studies and other secondary data also indicate the rise of entrepreneurial startups in the mental health industry. Overall, this research sheds light on both the challenges and opportunities presented by insurance policies in the mental health sector, offering insights into the new industry landscape.

Keywords: entrepreneurship, healthcare policy, insurance policy, mental health industry

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Authors: Miriam Schiff, Hadas Rosenne, Ran Nir-Paz, Shiri Shinan Altman

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To examine (1) the level, predictors, and subjective perception of professional quality of life (PRoQL), posttraumatic growth, roles, task changes during the pandemic, and perceived preparedness for the next pandemic. These variables were added as part of an international study on social workers in healthcare stress, resilience, and perceived preparedness we took part in, along with Australia, Canada, China, Hong Kong, Singapore, and Taiwan. (2) The extent to which background variables, rate of exposure to the virus, working in COVID wards, profession, personal resilience, and resistance to organizational change predict posttraumatic growth, perceived preparedness, and PRoQL (the latter was examined among social workers only). (3) The teams' perceptions of how the pandemic impacted them at the personal, professional, and organizational levels and what assisted them. Methodologies: Mixed quantitative and qualitative methods were used. 1039 hospital healthcare workers from various professions participated in the quantitative study while 32 participated in in-depth interviews. The same methods were used in six other countries. Findings: The level of PRoQL was moderate, with higher burnout and secondary traumatization level than during routine times. Differences between countries in the level of PRoQL were found as well. Perceived preparedness for the next pandemic at the personal level was moderate and similar among the different health professions. Higher exposure to the virus was associated with lower perceived preparedness of the hospitals. Compared to other professions, doctors and nurses perceived hospitals as significantly less prepared for the next pandemic. The preparedness of the State of Israel for the next pandemic is perceived as low by all healthcare professionals. A moderate level of posttraumatic growth was found. Staff who worked at the COVID ward reported a greater level of growth. Doctors reported the lowest level of growth. The staff's resilience was high, with no differences among professions or levels of exposure. Working in the COVID ward and resilience predicted better preparedness, while resistance to organizational change predicted worse preparedness. Findings from the qualitative part of the study revealed that healthcare workers reported challenges at the personal, professional and organizational level during the different waves of the pandemic. They also report on internal and external resources they either owned or obtained during that period. Conclusion: Exposure to the COVID-19 virus is associated with secondary traumatization on one hand and personal posttraumatic growth on the other hand. Personal and professional discoveries and a sense of mission helped cope with the pandemic that was perceived as a historical event, war, or mass casualty event. Personal resilience, along with the support of colleagues, family, and direct management, were seen as significant components of coping. Hospitals should plan ahead and improve their preparedness to the next pandemic.

Keywords: covid-19, health-care, social workers, burnout, preparedness, international perspective

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Authors: Bhat Geetalaxmi Jairam, D. V. Ashoka

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Wireless Sensor Networks are used in many applications to collect sensed data from different sources. Sensed data has to be delivered through sensors wireless interface using multi-hop communication towards the sink. The data collection in wireless sensor networks consumes energy. Energy consumption is the major constraints in WSN .Reducing the energy consumption while increasing the amount of generated data is a great challenge. In this paper, we have implemented two data gathering protocols with multiple mobile sinks/elements to collect data from sensor nodes. First, is Energy-Efficient Data Gathering with Tour Length-Constrained Mobile Elements in Wireless Sensor Networks (EEDG), in which mobile sinks uses vehicle routing protocol to collect data. Second is An Intelligent Agent-based Routing Structure for Mobile Sinks in WSNs (IAR), in which mobile sinks uses prim’s algorithm to collect data. Authors have implemented concepts which are common to both protocols like deployment of mobile sinks, generating visiting schedule, collecting data from the cluster member. Authors have compared the performance of both protocols by taking statistics based on performance parameters like Delay, Packet Drop, Packet Delivery Ratio, Energy Available, Control Overhead. Authors have concluded this paper by proving EEDG is more efficient than IAR protocol but with few limitations which include unaddressed issues likes Redundancy removal, Idle listening, Mobile Sink’s pause/wait state at the node. In future work, we plan to concentrate more on these limitations to avail a new energy efficient protocol which will help in improving the life time of the WSN.

Keywords: aggregation, consumption, data gathering, efficiency

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Authors: Johnson O. Adeogo, Ayodele S. Oluwole, O. Akinsanmi, Olawale J. Olaluyi

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In this 5G network, very high traffic density in densely populated areas, most especially in densely populated areas, is one of the key requirements. Radiation reduction becomes one of the major concerns to secure the future life of mobile network users in ultra-dense network areas using an improved coordinated multipoint technology. Coordinated Multi-Point (CoMP) is based on transmission and/or reception at multiple separated points with improved coordination among them to actively manage the interference for the users. Small cells have two major objectives: one, they provide good coverage and/or performance. Network users can maintain a good quality signal network by directly connecting to the cell. Two is using CoMP, which involves the use of multiple base stations (MBS) to cooperate by transmitting and/or receiving at the same time in order to reduce the possibility of electromagnetic radiation increase. Therefore, the influence of the screen guard with rubber condom on the mobile transceivers as one major piece of equipment radiating electromagnetic radiation was investigated by mobile network users amidst ultra-dense networks in 5g. The results were compared with the same mobile transceivers without screen guards and rubber condoms under the same network conditions. The 5 cm distance from the mobile transceivers was measured with the help of a ruler, and the intensity of Radio Frequency (RF) radiation was measured using an RF meter. The results show that the intensity of radiation from various mobile transceivers without screen guides and condoms was higher than the mobile transceivers with screen guides and condoms when call conversation was on at both ends.

Keywords: ultra-dense networks, mobile network users, 5g, coordinated multi-point.

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Authors: Tolga Barisik, Ali Fuat Guneri, K. Dastan

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Major industrial facilities have a high potential for fire risk. In particular, the indices used for the detection of hidden fire are used very effectively in order to prevent the fire from becoming dangerous in the initial stage. These indices provide the opportunity to prevent or intervene early by determining the stage of the fire, the potential for hazard, and the type of the combustion agent with the percentage values of the ambient air components. In this system, artificial neural network will be modeled with the input data determined using the Levenberg-Marquardt algorithm, which is a multi-layer sensor (CAA) (teacher-learning) type, before modeling the modeling methods in the literature. The actual values produced by the indices will be compared with the outputs produced by the network. Using the neural network and the curves to be created from the resulting values, the feasibility of performance determination will be investigated.

Keywords: artifical neural networks, fire, Graham Index, levenberg-marquardt algoritm, oxygen decrease percentage index, risk assessment, Trickett Index

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Authors: Takwa Belwakess

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With the wave of the digitization that invades the modern world, communication tools took their place in the world of business. As for organizations, being part of the digital era necessarily involves an evolution of the management style, mainly in processes management, knowing also as quality management system (QMS). For more than 50 years quality management standards have been adopted by organizations to prove their operational and financial performances. We believe that achieving a high-level of communication can lead to better quality management and greater customer satisfaction, which is essential to make sure long-term competitiveness. In this paper, a questionnaire survey was developed to investigate the use of collaboration tools such as Content Management System and Social Networks. Data from more than 100 companies based in France was analyzed, the results show that adopting new digital communication tools while applying quality management practices over a reasonable period, contributed to delivering a better implementation of the QMS for a better business performance.

Keywords: communication tools, content management system, digital, effectiveness, French companies, quality management system, quality management practices, social networks

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Authors: Jenny Ringblom, Marie Proczkowska, Laura Korhonen, Ingrid Wåhlin

Abstract:

Background: Emergence delirium is a well-known behaviour of perceptual disturbances that may occur after general anaesthesia in children. Children with emergence delirium are often confused; they cry, are involuntarily physically active and are almost impossible to console. The prevalence varies considerably between about 13% and 53%. Research has mainly focused on how different medication accents affect the incidence of emergence delirium, but less is known about parents’ experiences of emergence delirium during the recovery process. Aim: The aim of this study was to describe parents’ experiences and reflections during their child's emergence delirium behaviour when recovering from anaesthesia. Method: The study has a qualitative design, and the data has been analyzed using thematic analysis. A total of 16 parents were interviewed at two county hospitals in Sweden. Results: When the parents reunited with their child at the recovering unit, they felt as if they were encountering an incomprehensible scenario. When watching their child demonstrating emergence delirium, they experienced fear and insecurity and had feelings of powerlessness and guilt. Information and previous experience turned out to offer relief and being seen by the healthcare staff when they, in their vulnerability, failed to reach or console their child gave hope and energy. Conclusion: Emergence delirium must be extensively considered in children undergoing general anaesthesia. Healthcare staff needs to be aware of the parental difficulties it may cause. There is also important to know what parents experience as relieving, such as receiving information and when staff members are being available, responsive and supportive during the wake-up period.

Keywords: emergence delirium, experiences, pediatrics, parents, postoperative care

Procedia PDF Downloads 124

Authors: Tanish Mittal, Preyansh Agrawal, Esha Pahwa, Aarya Makwana

Abstract:

Multi-Scale and U-shaped Networks are widely used in various image restoration problems, including deblurring. Keeping in mind the wide range of applications, we present a comparison of these architectures and their effects on image deblurring. We also introduce a new block called as NFResblock. It consists of a Fast Fourier Transformation layer and a series of modified Non-Linear Activation Free Blocks. Based on these architectures and additions, we introduce NFResnet and NFResnet+, which are modified multi-scale and U-Net architectures, respectively. We also use three differ-ent loss functions to train these architectures: Charbonnier Loss, Edge Loss, and Frequency Reconstruction Loss. Extensive experiments on the Deep Video Deblurring dataset, along with ablation studies for each component, have been presented in this paper. The proposed architectures achieve a considerable increase in Peak Signal to Noise (PSNR) ratio and Structural Similarity Index (SSIM) value.

Keywords: multi-scale, Unet, deblurring, FFT, resblock, NAF-block, nfresnet, charbonnier, edge, frequency reconstruction

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Authors: Inci Aksu Kargin

Abstract:

The civil war which began in the Daraa province of Syria in March 2011, has caused thousands of Syrians to die and millions more to seek refuge in other countries such as Turkey, Lebanon, Jordan, Iraq, and Egypt. In order to understand the Syrian refugees’ living conditions and the problems they have experienced in Turkey in-depth, and to analyze how the arrival of the Syrian refugees in Turkey has affected the local people who live in Turkish-Syrian border, this study employed interviews, which were conducted with three different groups. First, 60 Syrian refugees, who have settled in Hatay and Gaziantep, were interviewed. Then, the Turkish government institutions, and NGOs, which are responsible for assisting the refugees, were interviewed. These interviews revealed that many Syrian refugees have encountered with several issues such as access to labor and housing markets as well as free healthcare and public education services. Second, 60 Turkish citizens living in Hatay and Gaziantep provinces were interviewed. These interviews shed light on the many issues (e.g., increase of unemployment, increase in the rental and sale prices of the houses, decrease in the quality of healthcare services, increase in traffic problems, problems with regard to the usage of parks and gardens) that Turkish citizens began experiencing after mass asylum claim of the Syrian refugees to Turkey. In addition to these, the existing social problems in Turkey such as child labor, begging, child brides, and illegal marriages (religious marriages) worsen.

Keywords: migration, refugees, Syrian civil war, Turkey

Procedia PDF Downloads 277

Authors: Walid Moudani

Abstract:

The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the coronary heart disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts’ knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: multi-classifier decisions tree, features reduction, dynamic programming, rough sets

Procedia PDF Downloads 405

Authors: Innocent Uzougbo Onwuegbuzie

Abstract:

Wireless Sensor Networks (WSNs) are crucial for numerous applications, yet they face significant challenges due to resource constraints such as limited power and memory. Traditional routing algorithms like Dijkstra, Ad hoc On-Demand Distance Vector (AODV), and Bellman-Ford, while effective in path establishment and discovery, are not optimized for the unique demands of WSNs due to their large memory footprint and power consumption. This paper introduces the Adaptive Energy-Aware Routing (AEAR) model, a solution designed to address these limitations. AEAR integrates reactive route discovery, localized decision-making using geographic information, energy-aware metrics, and dynamic adaptation to provide a robust and efficient routing strategy. We present a detailed comparative analysis using a dataset of 50 sensor nodes, evaluating power consumption, memory footprint, and path cost across AEAR, Dijkstra, AODV, and Bellman-Ford algorithms. Our results demonstrate that AEAR significantly reduces power consumption and memory usage while optimizing path weight. This improvement is achieved through adaptive mechanisms that balance energy efficiency and link quality, ensuring prolonged network lifespan and reliable communication. The AEAR model's superior performance underlines its potential as a viable routing solution for energy-constrained WSN environments, paving the way for more sustainable and resilient sensor network deployments.

Keywords: wireless sensor networks (WSNs), adaptive energy-aware routing (AEAR), routing algorithms, energy, efficiency, network lifespan

Procedia PDF Downloads 28