Search results for: health data

Authors: Elzbieta Sikorska-Simmons

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Advanced dementia is a progressive terminal brain disease that is accompanied by a syndrome of difficult to manage symptoms and complications that eventually lead to death. The management of advanced dementia poses major challenges to family caregivers who act as patient health care proxies in making medical treatment decisions. Little is known, however, about how they manage advanced dementia and how their treatment choices influence the quality of patient life. This prospective qualitative study examines the key medical treatment decisions that family caregivers make while managing advanced dementia. The term ‘family caregiver’ refers to a relative or a friend who is primarily responsible for managing patient’s medical care needs and legally authorized to give informed consent for medical treatments. Medical decision-making implies a process of choosing between treatment options in response to patient’s medical care needs (e.g., worsening comorbid conditions, pain, infections, acute medical events). Family caregivers engage in this process when they actively seek treatments or follow recommendations by healthcare professionals. Better understanding of medical decision-making from the family caregiver perspective is needed to design interventions that maximize the quality of patient life and limit inappropriate treatments. Data were collected in three waves of semi-structured interviews with 20 family caregivers for patients with advanced dementia. A purposive sample of 20 family caregivers was recruited from a senior care center in Central Florida. The qualitative personal interviews were conducted by the author in 4-5 months intervals. The ethical approval for the study was obtained prior to the data collection. Advanced dementia was operationalized as stage five or higher on the Global Deterioration Scale (GDS) (i.e., starting with the GDS score of five, patients are no longer able survive without assistance due to major cognitive and functional impairments). Information about patients’ GDS scores was obtained from the Center’s Medical Director, who had an in-depth knowledge of each patient’s health and medical treatment history. All interviews were audiotaped and transcribed verbatim. The qualitative data analysis was conducted to answer the following research questions: 1) what treatment decisions do family caregivers make while managing the symptoms of advanced dementia and 2) how do these treatment decisions influence the quality of patient life? To validate the results, the author asked each participating family caregiver if the summarized findings accurately captured his/her experiences. The identified medical decisions ranged from seeking specialist medical care to end-of-life care. The most common decisions were related to arranging medical appointments, medication management, seeking treatments for pain and other symptoms, nursing home placement, and accessing community-based healthcare services. The most challenging and consequential decisions were related to the management of acute complications, hospitalizations, and discontinuation of treatments. Decisions that had the greatest impact on the quality of patient life and survival were triggered by traumatic falls, worsening psychiatric symptoms, and aspiration pneumonia. The study findings have important implications for geriatric nurses in the context of patient/caregiver-centered dementia care. Innovative nursing approaches are needed to support family caregivers to effectively manage medical care needs of patients with advanced dementia.

Keywords: advanced dementia, family caregiver, medical decision-making, symptom management

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Authors: S. Fata, M. A. Tokat, N. Bagardi, B. Yilmaz

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Nowadays, various traditional practices are used throughout the world with aim to improve fertility. Various traditional remedies, acupuncture, religious practices such as sacrifice are frequently used. Studies often evaluate the traditional practices used by the women. But the use of this non-medical practice by couples and specific application reasons of this methods has been less investigated. The aim of this study was to evaluate the common used non-medical practices and determine perceived benefits by couples with fertility problems in Turkey. This is a descriptive study. Research data were collected between May-July 2016, in Izmir Ege Birth Education and Research Hospital Assisted Reproduction Clinic, from 151 couples with fertility problem. Personal Information Form and Non-Medical Practices Used for Fertility Evaluation Form was used. Number 'GOA 2649' permission letter from Dokuz Eylul University Non-Invasive Research Ethics Board, permission letter from the institution and the written consent from participants has been received to carry out the study. In the evaluation of the data, frequencies and proportions analysis were used. The average age of women participating in the study was 32.87, the 35.8% were high school graduates, 60.3% were housewife and the 58.9% lived in city. The 30.5% of husbands were high school graduates, the 96.7% were employed and the 60.9% lived in city. The 78.1% of couples lived as a nuclear family, the average marriage year was 7.58, in 33.8% the fertility problem stems from women, 42.4% of them received a diagnosis for 1-2 years, 35.1% were being treated for 1-2 years. The 35.8% of women reported use of non-medical applications. The 24.4% of women used figs, onion cure, hacemat, locust, bee-pollen milk, the 18.2% used herbs, the 13.1% vowed, the 12.1% went to the tomb, the 10.1% did not bath a few days after the embryo transfer, the 9.1% used thermal water baths, the 5.0% manually corrected the womb, the 5.0% printed amulets by Hodja, the 3.0% went to the Hodja/pilgrims. Among the perceived benefits of using non-medical practices; facilitate pregnancy and implantation, improve oocyte quality were the most recently expressed. Women said that they often used herbs to develop follicles, did not bath after embryo transfer with aim to provide implantation, and used thermal waters to get rid of the infection. Compared to women, only the 25.8% of men used the non-medical practice. The 52.1% reported that they used peanuts, hacemat, locust, bee-pollen milk, the 14.9% used herbs, the 12.8% vowed, the 10.1% went to the tomb, the 10.1% used thermal water baths. Improve sperm number, motility and quality were the most expected benefits. Men said that they often used herbs to improve sperm number, used peanuts, hacemat, locust, bee-pollen milk to improve sperm motility and quality. Couples in Turkey often use non-medical practices to deal with fertility problems. Some of the practices considered as useful can adversely affect health. Healthcare providers should evaluate the use of non-medical practices and should inform if the application is known adverse effects on health.

Keywords: fertility, couples, non-medical practice, perceived benefit

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Authors: Sonia Butalia, Huong Luu, Alexis Guigue, Karen J. B. Martins, Khanh Vu, Scott W. Klarenbach

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Obesity-related health conditions impose a substantial economic burden on payers due to increased healthcare use. Estimates of healthcare resource use and costs associated with obesity-related comorbidities are needed to inform policies and interventions targeting these conditions. Methods: Adults living with obesity were identified (a procedure-related body mass index code for class 2/3 obesity between 2012 and 2019 in Alberta, Canada; excluding those with bariatric surgery), and outcomes were compared over 1-year (2019/2020) between those who had and did not have specific obesity-related comorbidities. The probability of using a healthcare service (based on the odds ratio of a zero [OR-zero] cost) was compared; 95% confidence intervals (CI) were reported. Logistic regression and a generalized linear model with log link and gamma distribution were used for total healthcare cost comparisons ($CDN); cost ratios and estimated cost differences (95% CI) were reported. Potential socio-demographic and clinical confounders were adjusted for, and incremental cost differences were representative of a referent case. Results: A total of 220,190 adults living with obesity were included; 44% had hypertension, 25% had osteoarthritis, 24% had type-2 diabetes, 17% had cardiovascular disease, 12% had insulin resistance, 9% had chronic back pain, and 4% of females had polycystic ovarian syndrome (PCOS). The probability of hospitalization, ED visit, and ambulatory care was higher in those with a following obesity-related comorbidity versus those without: chronic back pain (hospitalization: 1.8-times [OR-zero: 0.57 [0.55/0.59]] / ED visit: 1.9-times [OR-zero: 0.54 [0.53/0.56]] / ambulatory care visit: 2.4-times [OR-zero: 0.41 [0.40/0.43]]), cardiovascular disease (2.7-times [OR-zero: 0.37 [0.36/0.38]] / 1.9-times [OR-zero: 0.52 [0.51/0.53]] / 2.8-times [OR-zero: 0.36 [0.35/0.36]]), osteoarthritis (2.0-times [OR-zero: 0.51 [0.50/0.53]] / 1.4-times [OR-zero: 0.74 [0.73/0.76]] / 2.5-times [OR-zero: 0.40 [0.40/0.41]]), type-2 diabetes (1.9-times [OR-zero: 0.54 [0.52/0.55]] / 1.4-times [OR-zero: 0.72 [0.70/0.73]] / 2.1-times [OR-zero: 0.47 [0.46/0.47]]), hypertension (1.8-times [OR-zero: 0.56 [0.54/0.57]] / 1.3-times [OR-zero: 0.79 [0.77/0.80]] / 2.2-times [OR-zero: 0.46 [0.45/0.47]]), PCOS (not significant / 1.2-times [OR-zero: 0.83 [0.79/0.88]] / not significant), and insulin resistance (1.1-times [OR-zero: 0.88 [0.84/0.91]] / 1.1-times [OR-zero: 0.92 [0.89/0.94]] / 1.8-times [OR-zero: 0.56 [0.54/0.57]]). After fully adjusting for potential confounders, the total healthcare cost ratio was higher in those with a following obesity-related comorbidity versus those without: chronic back pain (1.54-times [1.51/1.56]), cardiovascular disease (1.45-times [1.43/1.47]), osteoarthritis (1.36-times [1.35/1.38]), type-2 diabetes (1.30-times [1.28/1.31]), hypertension (1.27-times [1.26/1.28]), PCOS (1.08-times [1.05/1.11]), and insulin resistance (1.03-times [1.01/1.04]). Conclusions: Adults with obesity who have specific disease-related health conditions have a higher probability of healthcare use and incur greater costs than those without specific comorbidities; incremental costs are larger when other obesity-related health conditions are not adjusted for. In a specific referent case, hypertension was costliest (44% had this condition with an additional annual cost of $715 [$678/$753]). If these findings hold for the Canadian population, hypertension in persons with obesity represents an estimated additional annual healthcare cost of $2.5 billion among adults living with obesity (based on an adult obesity rate of 26%). Results of this study can inform decision making on investment in interventions that are effective in treating obesity and its complications.

Keywords: administrative data, healthcare cost, obesity-related comorbidities, real world evidence

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Authors: Noah Emil Glisik

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Alcohol use disorder (AUD) presents significant health challenges worldwide and is particularly concerning in Slovenia, where high alcohol consumption contributes to elevated rates of comorbidities, including depression and suicide. This review examines emerging evidence linking gut microbiome dysbiosis to AUD, exploring whether gut microbiome alterations merely result from alcohol use or actively contribute to the persistence of addiction. Additionally, it discusses how microbial changes may influence psychological symptoms, including anxiety and depressive states, which are closely associated with suicidality in this population. To address gaps in existing research, a systematic literature search was conducted through PubMed, Web of Science, and ScienceDirect. Inclusion criteria focused on studies examining gut microbiome changes in AUD, particularly those assessing gut-brain axis interactions and microbial species impacting inflammation and neurotransmitter pathways. Studies were excluded if they lacked peer review or did not specifically assess microbiome effects on mental health outcomes. A qualitative literature review approach was applied, synthesizing findings into key themes on microbial changes, neuroinflammatory pathways, and treatment implications. Data were organized into tables to provide a clear comparison of microbiota alterations across studies, highlighting specific bacterial species and their potential effects on AUD. This review emphasizes patterns in AUD patients, where reductions in anti-inflammatory species, such as Faecalibacterium prausnitzii and Roseburia intestinalis, coincide with increases in pro-inflammatory bacteria like Enterococcus faecalisand Lactobacillus rhamnosus. These shifts contribute to increased gut permeability and systemic inflammation, potentially influencing the kynurenine pathway, which is linked to depressive symptoms and elevated alcohol cravings. Furthermore, the review explores the potential of probiotic therapies targeting these microbial imbalances as adjunctive treatments for AUD, particularly those focusing on strains that support anti-inflammatory pathways and gut barrier integrity. Restoring microbial homeostasis through probiotics or fecal microbiota transplantation may not only reduce inflammation but also alleviate mental health symptoms associated with addiction, including suicidality. The findings underscore the need for further clinical trials assessing microbiome-targeted therapies as innovative, multifaceted approaches to AUD treatment in Slovenia and beyond.

Keywords: alcohol use disorder, gut-brain axis, microbiome dysbiosis, probiotic therapy.

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Authors: Ayoola Adekunle Dada, Tolulope Omolola Fateropa

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There is no gain saying the fact that the Nigerian correctional services lack rehabilitation reformation. Owing to this, some so many inmates, including the female, become more emotionally bruised and hardened instead of coming out of the prison reformed. Although female inmates constitute only a small percentage worldwide, the challenges resulting from women falling under the provision of the penal system have prompted ficial and humanitarian bodies to consider female inmateas as vulnerable persons who need particular social work measures that meet their specific needs. Female inmates’condition may become worseinprisondue to the absence of the standard living condition. A survey of 100 female inmates will be used to determine the assessment of the living condition of the female inmates within the contexts in which they occur. Employing field methods from Medical Sociology and Law, the study seeks to make use of the collaboration of both disciplines for a comprehensive understanding of the scenario. Its specific objectives encompassed: (1) To examine access and use of health facilities among the female inmates;(2) To examine the effect of officers/warders attitude towards female inmates;(3)To investigate the perception of the female inmates towards the housing facilities in the centre and; (4) To investigate the feeding habit of the female inmates. Due to the exploratory nature of the study, the researchers will make use of mixed-method, such qualitative methods as interviews will be undertaken to complement survey research (quantitative). By adopting the above-explained inter-method triangulation, the study will not only ensure that the advantages of both methods are exploited but will also fulfil the basic purposes of research. The sampling for this study will be purposive. The study aims at sampling two correctional centres (Ado Ekiti and Akure) in order to generate representative data for the female inmates in South West Nigeria. In all, the total number of respondents will be 100. A cross-section of female inmates will be selected as respondents using a multi-stage sampling technique. 100 questionnaires will be administered. A semi structured (in-depth) interviews will be conducted among workers in the two selected correctional centres, respectively, to gain further insight on the living conditions of female inmates, which the survey may not readily elicit. These participants will be selected purposively in respect to their status in the organisation. Ethical issues in research on human subjects will be given due consideration. Such issues rest on principles of beneficence, non-maleficence, autonomy/justice and confidentiality. In the final analysis, qualitative data will be analyzed using manual content analysis. Both the descriptive and inferential statistics will be used for analytical purposes. Frequency, simple percentage, pie chart, bar chart, curve and cross-tabulations will form part of the descriptive analysis.

Keywords: assessment, health facilities, inmates, perception, living conditions

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Authors: Thammarak Srimarut, Witthaya Mekhum

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This study analysed and developed a model for monk’s food bowl production on occupational health safety and environment at work for the encouragement of Rattanakosin local wisdom at Banbart Community. The process of blowpipe welding was necessary to produce the bowl which was very dangerous or 93.59% risk. After the employment of new sitting posture, the work risk was lower 48.41% or moderate risk. When considering in details, it was found that: 1) the traditional sitting posture could create work risk at 88.89% while the new sitting posture could create the work risk at 58.86%. 2) About the environmental pollution, with the traditional sitting posture, workers exposed to the polluted fume from welding at 61.11% while with the new sitting posture workers exposed to the polluted fume from welding at 40.47%. 3) On accidental risk, with the traditional sitting posture, workers exposed to the accident from welding at 94.44% while with the new sitting posture workers exposed to the accident from welding at 62.54%.

Keywords: occupational health safety, environment at work, Monk’s food bowl, machine intelligence

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Authors: Jude Opara, Esemokumo Perewarebo Akpos

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This paper is on non-parametric linear regression over its parametric counterparts with large sample size. Data set on anthropometric measurement of primary school pupils was taken for the analysis. The study used 50 randomly selected pupils for the study. The set of data was subjected to normality test, and it was discovered that the residuals are not normally distributed (i.e. they do not follow a Gaussian distribution) for the commonly used least squares regression method for fitting an equation into a set of (x,y)-data points using the Anderson-Darling technique. The algorithms for the nonparametric Theil’s regression are stated in this paper as well as its parametric OLS counterpart. The use of a programming language software known as “R Development” was used in this paper. From the analysis, the result showed that there exists a significant relationship between the response and the explanatory variable for both the parametric and non-parametric regression. To know the efficiency of one method over the other, the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) are used, and it is discovered that the nonparametric regression performs better than its parametric regression counterparts due to their lower values in both the AIC and BIC. The study however recommends that future researchers should study a similar work by examining the presence of outliers in the data set, and probably expunge it if detected and re-analyze to compare results.

Keywords: Theil’s regression, Bayesian information criterion, Akaike information criterion, OLS

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Authors: D. Prangchumpol

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This research presents a forecasting method of requisition document demands for Military units by using Exponential Smoothing methods to analyze data. The data used in the forecast is an actual data requisition document of The Adjutant General Department. The results of the forecasting model to forecast the requisition of the document found that Holt–Winters’ trend and seasonality method of α=0.1, β=0, γ=0 is appropriate and matches for requisition of documents. In addition, the researcher has developed a requisition online system to improve the performance of requisition documents of The Adjutant General Department, and also ensuring that the operation can be checked.

Keywords: requisition, holt–winters, time series, royal thai army

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Authors: Michelle Jennett, Jana Dengler, Maytal Perlman

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Background: Cervical spinal cord injury (SCI) is a devastating event that results in upper limb paralysis, loss of independence, and disability. People living with cervical SCI have identified improvement of upper limb function as a top priority. Nerve and tendon transfer surgery has successfully restored upper limb function in cervical SCI but is not universally used or available to all eligible individuals. This exploratory mixed-methods study used an implementation science approach to better understand these factors that influence access to upper limb reconstruction in the Canadian context and design an intervention to increase access to care. Methods: Data from the Canadian Institute for Health Information’s Discharge Abstracts Database (CIHI-DAD) and the National Ambulatory Care Reporting System (NACRS) were used to determine the annual rate of nerve transfer and tendon transfer surgeries performed in cervical SCI in Canada over the last 15 years. Semi-structured interviews informed by the consolidated framework for implementation research (CFIR) were used to explore Ontario healthcare provider knowledge and practices around upper limb reconstruction. An inductive, iterative constant comparative process involving descriptive and interpretive analyses was used to identify themes that emerged from the data. Results: Healthcare providers (n = 10 upper extremity surgeons, n = 10 SCI physiatrists, n = 12 physical and occupational therapists working with individuals with SCI) were interviewed about their knowledge and perceptions of upper limb reconstruction and their current practices and discussions around upper limb reconstruction. Data analysis is currently underway and will be presented. Regional variation in rates of upper limb reconstruction and trends over time are also currently being analyzed. Conclusions: Utilization of nerve and tendon transfer surgery to improve upper limb reconstruction in Canada remains low. There are a complex array of interrelated individual-, provider- and system-level barriers that prevent individuals with cervical SCI from accessing upper limb reconstruction. In order to offer equitable access to care, a multi-modal approach addressing current barriers is required.

Keywords: cervical spinal cord injury, nerve and tendon transfer surgery, spinal cord injury, upper extremity reconstruction

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Authors: Tulika Bhattacharyya, Suhita C. Chatterjee

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West Bengal, one of the most rapidly ageing states in India, has inadequate structure for elder care. Therefore, there is an urgent need to improve elder care which involves focusing on different care settings where the elderly exists, like - Homes, Hospitals and Long-Term Care facilities (e.g. - Old Age Homes, Hospices). The study explores various elder care settings, with the intention to develop an understanding about them, and thereby generate comprehensive information about the entire spectrum of elder care in Kolkata. Empirical data are collected from the elderly and their caregivers in different settings. The tools for data collection are narratives, in-depth interviews and focus group discussions, along with field observations. Mixed method design is adopted to analyze the complexities of elder care in different set ups. The major challenges of elder care in private Homes are: architecturally inadequate housing conditions, paucity of financial support and scarcity of skilled caregivers. While the key factors preventing the Hospital and Long-Term Care Facilities from providing elder care services are inadequate policies and set governmental standards for elder care for the hospitalized elderly in various departments of the Hospital and the elderly residing in different kinds of Long Term Care Facilities. The limitations in each care setting results in considerable neglect and abuse of the elderly. The major challenges in elder care in West Bengal are lack of continuum between different care settings/ peripheral location of private Homes within public health framework and inadequate state Palliative policy- including narcotic regulations. The study suggests remedial measures to improve the capacity to deliver elder care in different settings.

Keywords: elder care settings, family caregiver, home care, geriatric hospital care, long term care facility

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Authors: Ibrahim Mohammed, Suleiman Taofiq, Muhammad Naziru Yahya

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Geoelectrical measurements using Schlumberger Vertical Electrical Sounding (VES) method were carried out in Waziri Umaru Federal Polytechnic, Birnin Kebbi, Nigeria, with the aim of determining the groundwater potential in the area. Twelve (12) Vertical Electric Sounding (VES) data were collected using Terrameter (ABEM SAS 300c) and analyzed using computer software (IPI2win), which gives an automatic interpretation of the apparent resistivity. The results of the interpretation of VES data were used in the characterization of three to five geo-electric layers from which the aquifer units were delineated. Data analysis indicated that water bearing formation exists in the third and fourth layers having resistivity range of 312 to 767 Ωm and 9.51 to 681 Ωm, respectively. The thickness of the formation ranges from 14.7 to 41.8 m, while the depth is from 8.22 to 53.7 m. Based on the result obtained from the interpretation of the data, five (5) VES stations were recommended as the most viable locations for groundwater exploration in the study area. The VES stations include VES A4, A5, A6, B1, and B2. The VES results of the entire area indicated that the water bearing formation occurs at maximum depth of 53.7 m at the time of this survey.

Keywords: aquifer, depth, groundwater, resistivity, Schlumberger

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Authors: Jonathan Frank, Michelle Salmona

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We conducted quantitative and qualitative analyses of nurses’ perceived ease of use of electronic medical records and telemedicine in the Cook Islands. We examined antecedents of perceived ease of use through the lens of social construction of learning, and cultural diffusion. Our findings confirmed expected linkages between PEOU, attitudes and intentions. Interviews with nurses suggested infrastructural barriers to engaged learning. We discussed managerial implications of our findings, and areas of interest for future research.

Keywords: health information technology, ICT4D, TAM, developing countries

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Authors: Chao Yu, Qing-Guo Wang, Dan Zhang

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The outlier detection problem for dynamic systems is formulated as a matrix decomposition problem with low-rank, sparse matrices and further recast as a semidefinite programming (SDP) problem. A fast algorithm is presented to solve the resulting problem while keeping the solution matrix structure and it can greatly reduce the computational cost over the standard interior-point method. The computational burden is further reduced by proper construction of subsets of the raw data without violating low rank property of the involved matrix. The proposed method can make exact detection of outliers in case of no or little noise in output observations. In case of significant noise, a novel approach based on under-sampling with averaging is developed to denoise while retaining the saliency of outliers and so-filtered data enables successful outlier detection with the proposed method while the existing filtering methods fail. Use of recovered “clean” data from the proposed method can give much better parameter estimation compared with that based on the raw data.

Keywords: outlier detection, system identification, matrix decomposition, low-rank matrix, sparsity, semidefinite programming, interior-point methods, denoising

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Authors: C. Greenstone, R. Hansmann, K. Lawrence

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Portulacaria afra, commonly known as Spekboom is an indigenous South African plant. Spekboom is recognized for its medicinal, nutrient rich, easy to grow, drought tolerant and have climate change combating benefits. Durban’s air quality currently falls below the acceptable level. Urban greening absorbs air pollutants which can improve human health; however, urban planning often neglects the aspect of air quality on human health. It is therefore imperative that there is an investigation generating some quantification of the Spekboom plant on air quality. Though there are numerous advantages that Spekboom brings to ecosystems, the effect of Spekboom on air quality in context specific locales remains under researched. This study seeks to address this gap and bring forward the effect of Spekboom on air quality and improving human health overall using locations with specific characteristics ranging from industrial, commercial and residential. The study adopted a field sampling and spatial analysis approach through the collection of cuttings of Spekboom from various locations to measure the amount of toxins absorbed by the plant and thereafter using Geographic Information Systems (GIS) to spatially map the location of each sample. Through the results found, the implementation of Spekboom as an air purifier in areas that have poor air quality can be carried out. Spekboom could even be cultivated around cities forming a green belt to improve air quality on a much larger scale. Due to Spekboom's low maintenance characteristics, it makes the entire implementation process quite simple. Proposed Future research will be to collect yearly cuttings from the same plant in order to get a longitudinal, long-term assessment of air quality improvements in areas where Spekboom is implemented.

Keywords: air quality, human health, portulacaria afra, spekboom

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Authors: Walter Bernhofer, Peter Haber, Tobias Mayer, Manfred Mayr, Markus Ziegler

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Current preventive maintenance measures are cost intensive and not efficient. With the available sensor data of state of the art internet of things devices new possibilities of automated data processing emerge. Current advances in data science and in machine learning enable new, so called predictive maintenance technologies, which empower data scientists to forecast possible system failures. The goal of this approach is to cut expenses in preventive maintenance by automating the detection of possible failures and to improve efficiency and quality of maintenance measures. Additionally, a centralization of the sensor data monitoring can be achieved by using this approach. This paper describes the approach of three students to define a reference architecture for a predictive maintenance solution in the internet of things domain with a connected smartphone app for service technicians. The reference architecture is validated by a case study. The case study is implemented with current Microsoft Azure cloud technologies. The results of the case study show that the reference architecture is valid and can be used to achieve a system for predictive maintenance execution with the cloud components of Microsoft Azure. The used concepts are technology platform agnostic and can be reused in many different cloud platforms. The reference architecture is valid and can be used in many use cases, like gas station maintenance, elevator maintenance and many more.

Keywords: case study, internet of things, predictive maintenance, reference architecture

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Authors: Naomi W. Maina

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Fertility preference is a subject of great significance in developing countries. Studies reveal that the preferences of fertility are actually significant in determining the society’s fertility levels because the fertility behavior of the future has a high likelihood of falling under the effect of currently observed fertility inclinations. The objective of this study was to establish the factors associated with fertility preference amongst couples in Kenya by fitting a multinomial logistic regression model against 5,265 couple data obtained from Kenya demographic health survey 2014. Results revealed that the type of place of residence, the region of residence, age and spousal age gap significantly influence desire for additional children among couples in Kenya. There was the notable high likelihood of couples living in rural settlements having similar fertility preference compared to those living in urban settlements. Moreover, geographical disparities such as in northern Kenya revealed significant differences in a couples desire to have additional children compared to Nairobi. The odds of a couple’s desire for additional children were further observed to vary dependent on either the wife or husbands age and to a large extent the spousal age gap. Evidenced from the study, was the fact that as spousal age gap increases, the desire for more children amongst couples decreases. Insights derived from this study would be attractive to demographers, health practitioners, policymakers, and non-governmental organizations implementing fertility related interventions in Kenya among other stakeholders. Moreover, with the adoption of devolution, there is a clear need for adoption of population policies that are County specific as opposed to a national population policy as is the current practice in Kenya. Additionally, researchers or students who have little understanding in the application of multinomial logistic regression, both theoretical understanding and practical analysis in SPSS as well as application on real datasets, will find this article useful.

Keywords: couples' desire, fertility, fertility preference, multinomial regression analysis

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Authors: Shilpa Sondkar, Maithili Patil, Atharva Potnis

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The current health care landscape desires efficiency and patient satisfaction for optimal performance. Medical appointment booking apps have increased the overall efficiency of clinics, hospitals, and e-health marketplaces while simplifying processes. These apps allow patients to connect with doctors online. Not only are mobile doctor appointment apps a reliable and efficient solution, but they are also the future of clinical progression and a distinct new stage in the patient-doctor relationship. Compared to the usual queuing method, the web-based appointment system could significantly increase patients' satisfaction with registration and reduce total waiting time effectively.

Keywords: appointment, patient, scheduling, design and development, Figma

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Authors: Yan Zhang

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Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Judith Mwakalonge, Geophrey Mbatta, Saidi Siuhi, Gurcan Comert, Cuthbert Ruseruka

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Transportation agencies worldwide continuously monitor their roads' conditions to minimize road maintenance costs and maintain public safety and rideability quality. Existing methods for carrying out road condition surveys involve manual observations of roads using standard survey forms done by qualified road condition surveyors or engineers either on foot or by vehicle. Automated road condition survey vehicles exist; however, they are very expensive since they require special vehicles equipped with sensors for data collection together with data processing and computing devices. The manual methods are expensive, time-consuming, infrequent, and can hardly provide real-time information for road conditions. This study contributes to this arena by utilizing built-in vehicle technologies, drones, and deep learning to automate road condition surveys while using low-cost technology. A single model is trained to capture flexible pavement distresses (Potholes, Rutting, Cracking, and raveling), thereby providing a more cost-effective and efficient road condition monitoring approach that can also provide real-time road conditions. Additionally, data fusion is employed to enhance the road condition assessment with data from vehicles and drones.

Keywords: road conditions, built-in vehicle technology, deep learning, drones

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Authors: Thien Van Ngo

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The engineering design process is a systematic approach to solving problems. It involves identifying a problem, brainstorming solutions, prototyping and testing solutions, and evaluating the results. The engineering design process can be used to teach students how to solve problems in a creative and innovative way. The research aim of this study was to investigate the effectiveness of using the engineering design process to enhance student learning outcomes in a physics course. A mixed research method was used in this study. The quantitative data were collected using a pretest-posttest control group design. The qualitative data were collected using semi-structured interviews. The sample was 150 first-year students in the Department of Mechanical Engineering Technology at Cao Thang Technical College in Vietnam in the 2022-2023 school year. The quantitative data were collected using a pretest-posttest control group design. The pretest was administered to both groups at the beginning of the study. The posttest was administered to both groups at the end of the study. The qualitative data were collected using semi-structured interviews with a sample of eight students in the experimental group. The interviews were conducted after the posttest. The quantitative data were analyzed using independent sample T-tests. The qualitative data were analyzed using thematic analysis. The quantitative data showed that students in the experimental group, who were taught using the engineering design process, had significantly higher post-test scores on physics problem-solving than students in the control group, who were taught using the conventional method. The qualitative data showed that students in the experimental group were more motivated and engaged in the learning process than students in the control group. Students in the experimental group also reported that they found the engineering design process to be a more effective way of learning physics. The findings of this study suggest that the engineering design process can be an effective way of enhancing student learning outcomes in physics courses. The engineering design process engages students in the learning process and helps them to develop problem-solving skills.

Keywords: engineering design process, problem-solving, learning outcome of physics, students’ physics competencies, deep learning

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Authors: Jalal Haghighat Monfared, Zahra Akbari

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Today, business executives need to have useful information to make better decisions. Banks have also been using information tools so that they can direct the decision-making process in order to achieve their desired goals by rapidly extracting information from sources with the help of business intelligence. The research seeks to investigate whether there is a relationship between the quality of decision making and the business intelligence capabilities of Mellat Bank. Each of the factors studied is divided into several components, and these and their relationships are measured by a questionnaire. The statistical population of this study consists of all managers and experts of Mellat Bank's General Departments (including 190 people) who use commercial intelligence reports. The sample size of this study was 123 randomly determined by statistical method. In this research, relevant statistical inference has been used for data analysis and hypothesis testing. In the first stage, using the Kolmogorov-Smirnov test, the normalization of the data was investigated and in the next stage, the construct validity of both variables and their resulting indexes were verified using confirmatory factor analysis. Finally, using the structural equation modeling and Pearson's correlation coefficient, the research hypotheses were tested. The results confirmed the existence of a positive relationship between decision quality and business intelligence capabilities in Mellat Bank. Among the various capabilities, including data quality, correlation with other systems, user access, flexibility and risk management support, the flexibility of the business intelligence system was the most correlated with the dependent variable of the present research. This shows that it is necessary for Mellat Bank to pay more attention to choose the required business intelligence systems with high flexibility in terms of the ability to submit custom formatted reports. Subsequently, the quality of data on business intelligence systems showed the strongest relationship with quality of decision making. Therefore, improving the quality of data, including the source of data internally or externally, the type of data in quantitative or qualitative terms, the credibility of the data and perceptions of who uses the business intelligence system, improves the quality of decision making in Mellat Bank.

Keywords: business intelligence, business intelligence capability, decision making, decision quality

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Authors: Rahul Patel

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Ahmedabad, a city located in western India, is experiencing rapid growth due to urbanization and industrialization. It is projected to become a metropolitan city in the near future, resulting in various construction activities. Soil testing is necessary before construction can commence, requiring construction companies and contractors to periodically conduct soil testing. The focus of this study is on the process of creating a spatial database that is digitally formatted and integrated with geotechnical data and a Geographic Information System (GIS). Building a comprehensive geotechnical (Geo)-database involves three steps: collecting borehole data from reputable sources, verifying the accuracy and redundancy of the data, and standardizing and organizing the geotechnical information for integration into the database. Once the database is complete, it is integrated with GIS, allowing users to visualize, analyze, and interpret geotechnical information spatially. Using a Topographic to Raster interpolation process in GIS, estimated values are assigned to all locations based on sampled geotechnical data values. The study area was contoured for SPT N-Values, Soil Classification, Φ-Values, and Bearing Capacity (T/m2). Various interpolation techniques were cross-validated to ensure information accuracy. This GIS map enables the calculation of SPT N-Values, Φ-Values, and bearing capacities for different footing widths and various depths. This study highlights the potential of GIS in providing an efficient solution to complex phenomena that would otherwise be tedious to achieve through other means. Not only does GIS offer greater accuracy, but it also generates valuable information that can be used as input for correlation analysis. Furthermore, this system serves as a decision support tool for geotechnical engineers.

Keywords: ArcGIS, borehole data, geographic information system, geo-database, interpolation, SPT N-value, soil classification, Φ-Value, bearing capacity

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Authors: B. R. Shen, J. R. Wang, J. H. Yang, S. W. Chen, C. Shih, Y. Chiang, Y. F. Chang, Y. H. Huang

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In this research, TRACE code with the interface code-SNAP was used to simulate and analyze the SBO (station blackout) accident which occurred in Maanshan PWR (pressurized water reactor) nuclear power plant (NPP). There are four main steps in this research. First, the SBO accident data of Maanshan NPP were collected. Second, the TRACE/SNAP model of Maanshan NPP was established by using these data. Third, this TRACE/SNAP model was used to perform the simulation and analysis of SBO accident. Finally, the simulation and analysis of SBO with mitigation equipments was performed. The analysis results of TRACE are consistent with the data of Maanshan NPP. The mitigation equipments of Maanshan can maintain the safety of Maanshan in the SBO according to the TRACE predictions.

Keywords: pressurized water reactor (PWR), TRACE, station blackout (SBO), Maanshan

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Authors: Jojo Y. C. Mo

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Memories are good. They remind us of people, places and experiences that we cherish. But memories cannot be changed and there may well be memories that we do not want to remember. This is particularly true in relation to information which causes us embarrassment and humiliation or simply because it is private – we all want to erase or delete such information. This desire to delete is recently recognised by the Court of Justice of the European Union in the 2014 case of Google Spain SL, Google Inc. v Agencia Española de Protección de Datos, Mario Costeja González in which the court ordered Google to remove links to some information about the complainant which he wished to be removed. This so-called ‘right to be forgotten’ received serious attention and significantly, the European Council and the European Parliament enacted the General Data Protection Regulation (GDPR) to provide a more structured and normative framework for implementation of right to be forgotten across the EU. This development in data protection laws will, undoubtedly, have significant impact on companies and co-operations not just within the EU but outside as well. Hong Kong, being one of the world’s leading financial and commercial center as well as one of the first jurisdictions in Asia to implement a comprehensive piece of data protection legislation, is therefore a jurisdiction that is worth looking into. This article/project aims to investigate the following: a) whether there is a right to be forgotten under the existing Hong Kong data protection legislation b) if not, whether such a provision is necessary and why. This article utilises a comparative methodology based on a study of primary and secondary resources, including scholarly articles, government and law commission reports and working papers and relevant international treaties, constitutional documents, case law and legislation. The author will primarily engage literature and case-law review as well as comparative and doctrinal analyses. The completion of this article will provide privacy researchers with more concrete principles and data to conduct further research on privacy and data protection in Hong Kong and internationally and will provide a basis for policy makers in assessing the rationale and need for a right to be forgotten in Hong Kong.

Keywords: privacy, right to be forgotten, data protection, Hong Kong

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Authors: Hyeongju Jeon, Yonghyun Kim, Yongil Kim

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Synthetic Aperture Radar (SAR) is an effective tool for damage assessment induced by disasters due to its all-weather and night/day acquisition capability. In this paper, the 2017 Colombia landslide was observed using full-polarimetric ALOS/PALSAR-2 data. Polarimetric decompositions, including the Freeman-Durden decomposition and the Cloude decomposition, are utilized to analyze the scattering mechanisms changes before and after-landslide. These analyses are used to detect the damaged areas induced by the landslide. Experimental results validate the efficiency of the full polarimetric SAR data since the damaged areas can be well discriminated. Thus, we can conclude the proposed method using full polarimetric data has great potential for damage assessment of landslides.

Keywords: Synthetic Aperture Radar (SAR), polarimetric decomposition, damage assessment, landslide

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Authors: Sofia Stoica

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In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.

Keywords: finance, machine learning, opening price, stock market

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Authors: Jihen Bennaceur, Wissem Zouaghi, Ali Mabrouk

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The major aim of cyber threat intelligence (CTI) is to provide sophisticated knowledge about cybersecurity threats to ensure internal and external safeguards against modern cyberattacks. Inaccurate, incomplete, outdated, and invaluable threat intelligence is the main problem. Therefore, data analysis based on AI algorithms is one of the emergent solutions to overcome the threat of information-sharing issues. In this paper, we propose a supervised machine learning-based algorithm to improve threat information sharing by providing a sophisticated classification of cyber threats and data. Extensive simulations investigate the accuracy, precision, recall, f1-score, and support overall to validate the designed algorithm and to compare it with several supervised machine learning algorithms.

Keywords: threat information sharing, supervised learning, data classification, performance evaluation

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Authors: Wenru Wang

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Over the past decades, Asia has emerged as the ‘diabetes epicentre’ in the world due to rapid economic development, urbanization and nutrition transition. There is an urgent need to develop more effective and cost-effective care management strategies in response to this rising diabetes epidemic. This study aims to develop and compare a nurse-led smartphone-based self-management programme with an existing nurse-led diabetes service on health-related outcomes among type 2 diabetes patients with poor glycemic control in Singapore. We proposed a randomized controlled trial with pre- and repeated post-tests control group design. A total of 128 type 2 diabetes patients with poor glycemic control will be recruited from the diabetes clinic of an acute public hospital in Singapore through convenience sampling. Study participants will be either randomly allocated to the experimental group or control group. Outcome measures used will include the 10-item General Self-Efficacy Scale, 11-item Revised Summary of Diabetes Self-care Activities, and 19-item Diabetes-Dependent Quality of Life. Data will be collected at 3-time points: baseline, three months and six months from the baseline, respectively. It is expected that this programme will be an alternative offered to diabetes patients to master their self-care management skills, in addition to the existing diabetes service provided in diabetes clinics in Singapore hospitals. Also, the self-supporting and less resource-intensive nature of this programme, through the use of smartphone app as a mode of intervention delivery, will greatly reduce nurses’ direct contact time with patients and allow more time to be allocated to those who require more attention. The study has been registered with clinicaltrials.gov. The trial registration number is NCT03088475.

Keywords: type 2 diabetes, poor glycaemic control, nurse-led, smartphone-based, self-management, health-relevant outcomes

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Authors: Mashaal Hooda

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The rationale for conducting this action research was to increase students' Academic Achievement (AA) contexts of studying/researching by employing the Goal Setting intervention (GS). The purposive sample consisted of 10 female undergraduate students at a university in Dubai. The intervention was introduced through workshop classes conducted online. The pre-intervention consisted of discussions concentrating on participants' research contexts amidst a pandemic. The GS moderators were implemented in the class, followed by scaffolding and mentoring interactions and self-reflective accounts of students' actions and feelings of using the intervention to better plan and structure their dissertation tasks. The research incorporated a Mixed Methods Methodology (MMM). Quantitative data collection took place through surveys, while qualitative data were collected using semi-structured interviews. Triangulation of the emergent themes showed a positive increase in students achievable GS, self-regulatory study skills, feedback-seeking behaviours, research organisation and synthesis, self-reflection and Academic Resilient (AR) attitudes amalgamate to enhance students' AA outcomes. Though, students' intrinsic motivational levels to study and research observed minor changes only. Nonetheless, the pebble in the shoe was removed as students AA contexts improved in undertaking better actionable steps for their research. Therefore, the GS intervention enabled students to set, balance, and achieve academic goals while catering to their academic anxieties, mental health concerns, and adaptability to the e-learning platforms amidst the COVID-19 pandemic. Despite the wide-scale changes the pandemic brought to the teaching and learning communities, the GS intervention served as a targeted intervention to help students maintain their achievement contexts in a goal-oriented way.

Keywords: academic achievement, acadeic resilience, COVID-19, goal setting

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Authors: Tetyana Kendzerska, David T. Zhu, Michael Pugliese, Douglas Manuel, Mohsen Sadatsafavi, Marcus Povitz, Therese A. Stukel, Teresa To, Shawn D. Aaron, Sunita Mulpuru, Melanie Chin, Claire E. Kendall, Kednapa Thavorn, Rebecca Robillard, Andrea S. Gershon

Abstract:

The impact of the COVID-19 pandemic on the management of ambulatory care sensitive conditions (ACSCs) remains unknown. To compare observed and expected (projected based on previous years) trends in all-cause mortality and healthcare use for ACSCs in the first year of the pandemic (March 2020 - March 2021). A population-based study using provincial health administrative data.General adult population (Ontario, Canada). Monthly all-cause mortality, and hospitalizations, emergency department (ED) and outpatient visit rates (per 100,000 people at-risk) for seven combined ACSCs (asthma, COPD, angina, congestive heart failure, hypertension, diabetes, and epilepsy) during the first year were compared with similar periods in previous years (2016-2019) by fitting monthly time series auto-regressive integrated moving-average models. Compared to previous years, all-cause mortality rates increased at the beginning of the pandemic (observed rate in March-May 2020 of 79.98 vs. projected of 71.24 [66.35-76.50]) and then returned to expected in June 2020—except among immigrants and people with mental health conditions where they remained elevated. Hospitalization and ED visit rates for ACSCs remained lower than projected throughout the first year: observed hospitalization rate of 37.29 vs. projected of 52.07 (47.84-56.68); observed ED visit rate of 92.55 vs. projected of 134.72 (124.89-145.33). ACSC outpatient visit rates decreased initially (observed rate of 4,299.57 vs. projected of 5,060.23 [4,712.64-5,433.46]) and then returned to expected in June 2020. Reductions in outpatient visits for ACSCs at the beginning of the pandemic combined with reduced hospital admissions may have been associated with temporally increased mortality—disproportionately experienced by immigrants and those with mental health conditions. The Ottawa Hospital Academic Medical Organization

Keywords: COVID-19, chronic disease, all-cause mortality, hospitalizations, emergency department visits, outpatient visits, modelling, population-based study, asthma, COPD, angina, heart failure, hypertension, diabetes, epilepsy

Procedia PDF Downloads 97