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The usage of Electronic Health Record (EHR) has taken a big role in Healthcare Sector and EHR are started to implement in most of the countries in the world. EHR helps in improving the quality of the treatment by referring the previous history and any time access. But, as a system EHR can be used anywhere any time, the query arises on its data security while sharing the data. Despite its rising utility and growing enthusiasm for its adoption, the ethical considerations that may arise are receiving little attention. Need to look way on how to secure the EHR Systems and found one way as to encode the data and then it can be shared with a security pin. This article entitles the different possible ways of the ethics or ethical considerations which can come by using the EHR Systems and its thinkable results that can be implemented., {"references":["1.\tAbouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. (2017). Big healthcare data: preserving security and privacy. Journal of Big Data. 2.\tAzmaeen, Z. (2022). The ethical challenges facing the widespread adoption of digital healthcare technology. Health and Technology, 175–179. 3.\tBipin Kumar, R. (2014). Security Challenges and Solutions for Healthcare in the Internet of Things. IEEE International Congress on Big Data. 4.\tChristina, W. Y. (2017). Data Accuracy in Electronic Medical Record Documentation. JAMA Ophthalmol, 232-233. 5.\tCornelius, E., Susan, H., &Kris, D. (2021). How do healthcare professionals respond to ethical challenges regarding information management? A review of empirical studies. Global Bioethics, 67-84. 6.\tDixon-Woods M, M. S. (2012). Ten challenges in improving quality in healthcare: lessons from the Health Foundation's programme evaluations and relevant literature. BMJ Quality & Safety, 876-884. 7.\tDorothy, S., & Kenneth, P. M. (2020). The ethics of neurologically complicated pregnancies. Handbook of Clinical Neurology, 227-242. 8.\tDrumwright, M. (2015). Behavioral ethics and teaching ethical decision making. Decision Sciences Journal of Innovative Education, 176-180. 9.\tEllen, Z. A. (2008). Modifiers of Complementary Therapy: Legal, Ethical, and Cultural Issues. Complementary Therapies for Physical Therapy, 24-35. 10.\tFouzia, O. F., Nayer, J., Amit, S., &Praveen, A. (2015). Ethical issues in electronic health records: A general overview. Perspectives in Clinical Research, 73-76. 11.\tFred, F. M. (2008). The Ethical Principle of Justice: The Purveyor of Equality. Annals of Long-Term Care. 12.\tJukka , V. (2006). The value of autonomy in medical ethics. Medical, Healhcare and Philosophy, 377-388. 13.\tKelkar, A. H., Blake, J. A., Cherabuddi, K., Cornett, H., McKee, B. L., & Cogle, C. R. (2021). Vaccine Enthusiasm and Hesitancy in Cancer Patients and the Impact of a Webinar. Healthcare. 14.\tKirsti, D. A. (2001). Ethical Challenges of Medicine and Health on the Internet: A Review. Journal of Medical Internet Research. 15.\tLippeveld, T., Sauerborn, R., & Bodart, C. (2000). Design and implementation of health information systems. Geneva: World Health Organization. 16.\tMenachemi, N., Ford, E., Beitsch, L., &Brooks, R. (2007). Incomplete EHR adoption: Late uptake of patient safety and cost control functions. American Journal of Medical Quality, 319-326. 17.\tMohammad, J. S., Sabina, R., William, G., Eric, P., &Stuart, M. (2019). Health Care and Cybersecurity: Bibliometric Analysis of the Literature. Journal of Medical Research. 18.\tNicole, M.-M., Zelun, L., Amit, K., Ehsan, A., Albert, H., Sara, K. S., Arnold , M. (2021). Ethical issues in using ambient intelligence in health-care settings. Lancet Digit Health, 115-23. 19.\tPediatr, N. (2020). Practical Bioethics during the Exceptional Circumstances of a Pandemic. Elsevier Public Health Emergency Collection, 3-4. 20.\tPractical, E. (2015). Simplifying the Complexity of Confidentiality in Research. Journal of Empirical Research on Human Research Ethics. 21.\tRakesh, K. S., Gayathri, N., Muthuramalingam, S., Balamurugan, B., Ramesh, C., & Nallakaruppan, M. (2019). Medical Big Data Mining and Processing in e-Healthcare. In Valentina E, H. S. Le, J. Sudan, K. Manju , & K. Raghvendra , Internet of Things in Biomedical Engineering (pp. 323-339). Academic Press. 22.\tRicardo, L., Clarissa, C. M., Ademar, D., &Leonardo, C. C. (2021). Implementation of large-scale health information systems. Revista de Gestao, 106-132. 23.\tRozemarijn, L. B.-V., Gert, v. D., Inez, B. d., & Francesco, M.-R. (2020). Ethical frameworks for complex medical decision making in older patients: A narrative review. Archives of Gerontology and Geriatrics. 24.\tShaharior, R. R., Tasnuva, Y., Taimia , B. A., Shahin , I., Sheikh , M. I., Hailay , A. G., & Paul , W. (2021). Challenges Faced by Healthcare Professionals During the COVID-19 Pandemic: A Qualitative Inquiry From Bangladesh. Frontiers in Public Health . 25.\tSheikh, S. (2007). The importance of ethics in healthcare System. Journal of the Dow University of Health Sciences. 26.\tSiddiqui, A. (2016). Healthcare - Ethical Code, Code of Conduct and Professional Ethics. General Directorate of Health. RiyadhAt: Public Health Department. 27.\tSoile , J., Kirsi , R., & Piia , S. (2019). The ethics of care and justice in primary nursing of older patients. Clinical Ethics. 28.\tVarkey, B. (2021). Principles of Clinical Ethics and Their Application to Practice. Medical principles and practice , 17-28. 29.\tVijayalakshmi , S., Ishpreet , A., Hong , Y., Anju , P. s., & Akansha , S. (2021). Impact of big data in healthcare system—a quick look into electronic health record systems. In K. S. Krishna, E. Mohamed , S. Akansha , & E. A. Ahmed , Machine Learning and the Internet of Medical Things in Healthcare, (pp. 251-262). Academic Press. 30.\tYaser , J., Asem , T., Sahar , K., & Amro , A.-M. (2021). Challenges to healthcare information systems development: The case of Jordan. International Journal of Healthcare Management, 447-455."]}

While the stratospheric ozone protects the biosphere against ultraviolet (UV) radiation, tropospheric ozone acts like a greenhouse gas and an indicator of anthropogenic pollution. In this paper, we combined ground-based and satellite ozone observations over Irene site (25.90&deg, S, 28.22&deg, E), one of the most ancient ozone-observing stations in the southern tropics. The dataset is made of daily total columns and weekly profiles of ozone collected over 20 years, from 1998 to 2017. In order to fill in some missing data and split the total column of ozone into a tropospheric and a stratospheric column, we used satellite observations from TOMS (Total Ozone Mapping Spectrometer), OMI (Ozone Monitoring Instrument), and MLS (Microwave Limb Sounder) experiments. The tropospheric column is derived by integrating ozone profiles from an ozonesonde experiment, while the stratospheric column is obtained by subtracting the tropospheric column from the total column (recorded by the Dobson spectrometer), and by assuming that the mesospheric contribution is negligible. Each of the obtained ozone time series was then analyzed by applying the method of wavelet transform, which permitted the determination of the main forcings that contribute to each ozone time series. We then applied the multivariate Trend-Run model and the Mann&ndash, Kendall test for trend analysis. Despite the different analytical approaches, the obtained results are broadly similar and consistent. They showed a decrease in the stratospheric column (&minus, 0.56% and &minus, 1.7% per decade, respectively, for Trend-Run and Mann&ndash, Kendall) and an increase in the tropospheric column (+2.37% and +3.6%, per decade, respectively, for Trend-Run and Mann&ndash, Kendall). Moreover, the results presented here indicated that the slowing down of the total ozone decline is somewhat due to the contribution of the tropospheric ozone concentration.

This study aims to investigate the Stratosphere-Troposphere Exchange (STE) events and ozone trends over Irene (25.5°S, 28.1°E). Twelve years of ozonesondes data (2000–2007, 2012–2015) from Irene station operating in the framework of the Southern Hemisphere Additional Ozonesodes (SHADOZ) was used to study the troposphere (0–16 km) and stratosphere (17– 28 km) ozone (O3) vertical profiles. Ozone profiles were grouped into three categories (2000–2003, 2004–2007 and 2012–2015) and average composites were calculated for each category. Fifteen O3 enhancement events were identified over the study period. These events were observed in all seasons (one event in summer, four events in autumn, five events in winter and five events in spring), however, they predominantly occur in winter and spring. The STE events presented here are observed to be influenced by the Southern Hemisphere polar vortex. During the STE events, the advected potential vorticity maps assimilated using Modélisation Isentrope du transport Méso–échelle de l’Ozone Stratosphérique par Advection (MIMOSA) model for the 350 K (~12–13 km) isentropic level indicated a transport of high latitude air masses which seems to be responsible for the reduction of the O3 mole fractions at the lower stratosphere over Irene which takes place at the same time with the enhancement of ozone in the upper troposphere. In general, the stratosphere is dominated by higher Modern Retrospective Analysis for Research Application (MERRA-2) potential vorticity (PV) values compared to the troposphere. However, during the STE events, higher PV values from the stratosphere were observed to intrude the troposphere. Ozone decline was observed from 12 km to 24 km with highest decline occurring from 14 km to 18 km. An average decrease of 6.0 and 9.1% was calculated from 12 to 24 km in 2004–2007 and 2012–2015 respectively. The observed decline occurred in the upper troposphere and lower stratosphere with winter and spring showing more decline compared with summer and autumn.

The detailed analysis of columnar optical and microphysical properties of aerosols obtained from the AErosol RObotic NETwork (AERONET) Cimel sun photometer operated at Skukuza (24.98° S, 31.60° E, 150 m above sea level), South Africa was carried out using the level 2.0 direct sun and inversion products measured during 1999–2010. The observed aerosol optical depth (AOD) was generally low over the region, with high values noted in late winter (August) and mid-spring (September and October) seasons. The major aerosol types found during the study period were made of 3.74, 69.63, 9.34, 8.83, and 8.41% for polluted dust (PD), polluted continental (PC), non-absorbing (NA), slightly absorbing (SA), and moderately absorbing (MA) aerosols, respectively. Much attention was given to the aerosol fine- and coarse-modes deduced from the particle volume concentration, effective radius, and fine-mode volume fraction. The aerosol volume size distribution pattern was found to be bimodal with the fine-mode showing predominance relative to coarse-mode during the winter and spring seasons, owing to the onset of the biomass burning season. The mean values of total, fine-, and coarse-mode volume particle concentrations were 0.07 ± 0.04, 0.03 ± 0.03, and 0.04 ± 0.02 μm3 μm−2, respectively, whereas the mean respective effective radii observed at Skukuza for the abovementioned modes were 0.35 ± 0.17, 0.14 ± 0.02, and 2.08 ± 0.02 μm. The averaged shortwave direct aerosol radiative forcing (ARF) observed within the atmosphere was found to be positive (absorption or heating effect), whereas the negative forcing in the surface and TOA depicted significant cooling effect due to more scattering type particles.

Long-term variability in ozone trends was assessed over eight Southern Hemisphere tropical and subtropical sites (Natal, Nairobi, Ascension Island, Java, Samoa, Fiji, Reunion and Irene), using total column ozone data (TCO) and vertical ozone profiles (altitude range 15–30 km) recorded during the period January 1998–December 2012. The TCO datasets were constructed by combination of satellite data (OMI and TOMS) and ground-based observations recorded using Dobson and SAOZ spectrometers. Vertical ozone profiles were obtained from balloon-sonde experiments which were operated within the framework of the SHADOZ network. The analysis in this study was performed using the Trend-Run model. This is a multivariate regression model based on the principle of separating the variations of ozone time series into a sum of several forcings (annual and semi-annual oscillations, QBO (Quasi-Biennial Oscillation), ENSO, 11-year solar cycle) that account for most of its variability. The trend value is calculated based on the slope of a normalized linear function which is one of the forcing parameters included in the model. Three regions were defined as follows: equatorial (0–10∘ S), tropical (10–20∘ S) and subtropical (20–30∘ S). Results obtained indicate that ozone variability is dominated by seasonal and quasi-biennial oscillations. The ENSO contribution is observed to be significant in the tropical lower stratosphere and especially over the Pacific sites (Samoa and Java). The annual cycle of ozone is observed to be the most dominant mode of variability for all the sites and presents a meridional signature with a maximum over the subtropics, while semi-annual and quasi-biannual ozone modes are more apparent over the equatorial region, and their magnitude decreases southward. The ozone variation mode linked to the QBO signal is observed between altitudes of 20 and 28 km. Over the equatorial zone there is a strong signal at ∼26 km, where 58 % ±2 % of total ozone variability is explained by the effect of QBO. Annual ozone oscillations are more apparent at two different altitude ranges (below 24 km and in the 27–30 km altitude band) over the tropical and subtropical regions, while the semi-annual oscillations are more significant over the 27–30 km altitude range in the tropical and equatorial regions. The estimated trend in TCO is positive and not significant and corresponds to a variation of ∼1.34±0.50 % decade−1 (averaged over the three regions). The trend estimated within the equatorial region (0–15∘ S) is less than 1 % per decade, while it is assessed at more than 1.5 % decade−1 for all the sites located southward of 17∘ S. With regard to the vertical distribution of trend estimates, a positive trend in ozone concentration is obtained in the 22–30 km altitude range, while a delay in ozone improvement is apparent in the UT–LS (upper troposphere–lower stratosphere) below 22 km. This is especially noticeable at approximately 19 km, where a negative value is observed in the tropical regions.

Background/Objectives: A novel feature selection framework using minimum variance method is proposed. The purpose of the proposed method is to reduce the computational complexity, reduce the number of initial features and increase the classification accuracy of the selected feature subsets. Methods/Statistical Analysis: The clusters are formed using minimum variance method. The process must be repeated for different pairs of records and voting is done on the different sets of cluster pairs. The cluster pair which has the maximum number of votes is chosen and the highest priority member is chosen from each cluster using information gain and removing the remaining attributes, thus reducing the number of attributes. Findings: The proposed feature selector is evaluated by comparing it with existing feature selection algorithms over 9 datasets from UCI and WebKb Datasets. The proposed method shows better results in terms of number of selected features, classification accuracy, and running time than most existing algorithms. Improvements/Applications: A new feature selector using minimum variance method is implemented and found that it performs better than the popular and computationally expensive traditional algorithms.

Background/Objectives: Educational Data mining is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. The objective of this work is to identify relevant attributes from socio-demographic, academic and institutional data from undergraduate students at the university located in India and develop an improved decision tree algorithm based on ID3 which can able to predict whether the students continue or drop their studies. Methods/Statistical Analysis: The traditional ID3 algorithm is improved by using Renyi entropy, Information gain and Association Function and the model generated by improved decision tree algorithm may be beneficial for university administrators to create guidelines and policies related to raise the enrollment rate in university and to take precautionary and advisory measures and thereby reduce student dropout. It can also used to find the reasons and relevant factors that affect the dropout students. Findings: Experimental results proved that improved decision tree algorithm provides better prediction accuracy in educational data than that of traditional classification algorithms in the literature. Improvements/Applications: Improved decision algorithm was proposed that enhances the ability to form decision trees and thereby to prove that the classification accuracy of improved decision algorithm on educational dataset is greater.

The climatology of tropospheric ozone at Irene has been investigated using SHADOZ network data to assess the correlation between the observed seasonal ozone enhancement and meteorological factors. Previous studies identified photochemical sources (biomass burning, biogenic and lightning emissions) as well as dynamic factors (synoptic weather system, stratospheric intrusion) as contributing factors to ozone enhancement observed during Austral spring (October) and Austral summer (February). Recent global increase in temperature due to climate change has raised concern on the impact of such increase on seasonal ozone enhancement over this region. As tropospheric ozone is poorly documented over southern Africa, a few studies have been undertaken to understand the correlation between change in meteorological parameters and tropospheric ozone variation. The objective of this paper is to providing a comprehensive correlation between meteorological parameters and tropospheric ozone concentrations over Irene (South Africa) for the period 1998 to 2013 in order to predict possible change in the concentration of ozone and water vapor as greenhouse gases. To this end correlation analysis has been used to assess annual and seasonal TTO (Total Tropospheric Ozone) variation over different layers up to the tropical tropopause height. Seasonal TTO trends have shown identical seasonal ozone patterns with two maxima occurring in summer and spring respectively. However an increase on ozone concentrations from 55 to 65.6 DU in spring and from 32 to 55 DU in summer have noted in comparison with previous short term study at the same location. This was evidenced by seasonal ozone profiles which showed a sharp seasonal increase of 23 and 14 ppbv in the layer 10-12 km in spring and summer respectively. While autumn profile displays an increase of 12 ppbv, winter profile presents a 6 ppbv decrease at this very layer. The role played by temperature and relative humidity is depicted by the strong correlation existing between both temperature and ozone concentrations from surface: 2 km and 2-4 km and weak correlation in upper layers. In contrast relative humidity shows a weak correlation from surface to 3 km and a strong correlation from 3 km to upper layers. A multiple linear regression model was used to provide seasonal correlation between ozone and temperature and relative humidity. All seasons display strong regression coefficients (0.96