Search results for: healthcare data security

Authors: Fehmeen Anwar, Ujala Liaqat

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For over half a century, U.S. and the Soviet Union have been at daggers drawn with each other. Both leading powers of the world have been struggling hard to surpass each other in military and other technological fields. This neck-to-neck competition turned in favour of U.S. in the early 1990s when USSR had to face economic stagnation and later dismemberment of several of its states. The predominance of U.S. is still evident to date, rather it continues to grow. With this proposed defence program i.e. Anti-Ballistic Missile System, the U.S. will have a considerable chance of intercepting any nuclear strike by Russia, which re-asserts U.S. dominance in the region and creating a security dilemma for Russia and other states. The question is whether America’s recent nuclear deterrence project is merely to counter nuclear threats from Iran and North Korea or is it purely directed towards Russia, thus ensuring complete military supremacy in the world. Although U.S professes to direct its Anti-Ballistic Missile System (ABMS) against the axis of evil (Iran and North Korea), yet the deployment of this system in the East European territory undermines the Russian nuclear strategic capability, as this enables U.S. to initiate an attack and guard itself from retaliatory strike, thus disturbing the security equilibrium in Europe. The implications of this program can lead to power imbalance which can lead to the emergence of fundamentally different paradigm of international politics.

Keywords: Anti-Ballistic Missile System (ABMS), cold-war, axis of evil, power dynamics

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Authors: Alfiah Hasanah, Silvia Mendolia, Oleg Yerokhin

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This paper explores the impacts of adult child migration on the health of elderly parents left behind. The maternal and children health are a priority of health-related policy in most low and middle-income country, and so there is lack of evidence on the health of older population particularly in Indonesia. With increasing life expectancy and limited access to social security and social services for the elderly in this country, the consequences of increasing number of out-migration of adult children to parent health are important to investigate. This study use Indonesia Family Life Survey (IFLS), the only large-scale continuing longitudinal socioeconomic and health survey that based on a sample of households representing about 83 percent of the Indonesian population in its first wave. Using four waves of IFLS including the recent wave of 2014, several indicators of the self-rated health status, interviewer-rated health status and days of illness are used to estimate the impact of labour out-migration of adult children on parent health status. Incorporate both individual fixed effects to control for unobservable factors in migrant and non-migrant households and the ordered response of self-rated health, this study apply the ordered logit of “Blow-up and Cluster” (BUC ) estimator. The result shows that labour out-migration of adult children significantly improves the self-rated health status of the elderly parent left behind. Findings of this study are consistent with the view that migration increases family resources and contribute to better health care and nutrition of the family left behind.

Keywords: aging, migration, panel data, self-rated health

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Authors: Sakda Loetpiparwanich, Preecha Vichitthamaros

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Nowadays, poor data quality is considered one of the majority costs for a data project. The data project with data quality awareness almost as much time to data quality processes while data project without data quality awareness negatively impacts financial resources, efficiency, productivity, and credibility. One of the processes that take a long time is defining the expectations and measurements of data quality because the expectation is different up to the purpose of each data project. Especially, big data project that maybe involves with many datasets and stakeholders, that take a long time to discuss and define quality expectations and measurements. Therefore, this study aimed at developing meaningful indicators to describe overall data quality for each dataset to quick comparison and priority. The objectives of this study were to: (1) Develop a practical data quality indicators and measurements, (2) Develop data quality dimensions based on statistical characteristics and (3) Develop Composite Indicator that can describe overall data quality for each dataset. The sample consisted of more than 500 datasets from public sources obtained by random sampling. After datasets were collected, there are five steps to develop the Dataset Quality Index (SDQI). First, we define standard data quality expectations. Second, we find any indicators that can measure directly to data within datasets. Thirdly, each indicator aggregates to dimension using factor analysis. Next, the indicators and dimensions were weighted by an effort for data preparing process and usability. Finally, the dimensions aggregate to Composite Indicator. The results of these analyses showed that: (1) The developed useful indicators and measurements contained ten indicators. (2) the developed data quality dimension based on statistical characteristics, we found that ten indicators can be reduced to 4 dimensions. (3) The developed Composite Indicator, we found that the SDQI can describe overall datasets quality of each dataset and can separate into 3 Level as Good Quality, Acceptable Quality, and Poor Quality. The conclusion, the SDQI provide an overall description of data quality within datasets and meaningful composition. We can use SQDI to assess for all data in the data project, effort estimation, and priority. The SDQI also work well with Agile Method by using SDQI to assessment in the first sprint. After passing the initial evaluation, we can add more specific data quality indicators into the next sprint.

Keywords: data quality, dataset quality, data quality management, composite indicator, factor analysis, principal component analysis

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Authors: Annie Singh, Ishaan Singh

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Background: Transgenders are people who have a gender identity different from their sex assigned at birth. Their gender behaviour doesn’t match their body anatomy. The community faces discrimination due to their gender identity all across the world. The term transgender is an umbrella term for many people non-conformal to their biological identity; note that the term transgender is different from gender dysphoria, which is a DSM-5 disorder defined as problems faced by an individual due to their non-conforming gender identity. Transgender people have been a part of Indian culture for ages yet have continued to face exclusion and discrimination in society. This has led to the low socio-economic status of the community. Various studies done across the world have established the role of discrimination, harassment and exclusion in the development of psychological disorders. The study is aimed to assess the frequency of stress and anxiety in the transgender population and understand the various factors affecting the same. Methodology: A cross-sectional survey of self consenting transgender individuals above the age of 18 residing in Delhi was done to assess their socioeconomic status and experiential ecology. Recruitment of participants was done with the help of NGOs. The survey was constructed GAD-7 and PSS-10, two well-known scales were used to assess the stress and anxiety levels. Medians, means and ranges are used for reporting continuous data wherever required, while frequencies and percentages are used for categorical data. For associations and comparison between groups in categorical data, the Chi-square test was used, while the Kruskal-Wallis H test was employed for associations involving multiple ordinal groups. SPSS v28.0 was used to perform the statistical analysis for this study. Results: The survey showed that the frequency of stress and anxiety is high in the transgender population. A demographic survey indicates a low socio-economic background. 44% of participants reported facing discrimination on a daily basis; the frequency of discrimination is higher in transwomen than in transmen. Stress and anxiety levels are similar among both transmen and transwomen. Only 34.5% of participants said they had receptive family or friends. The majority of participants (72.7%) reported a positive or neutral experience with healthcare workers. The prevalence of discrimination is significantly lower in the higher educated groups. Analysis of data shows a positive impact of acceptance and reception on mental health, while discrimination is correlated with higher levels of stress and anxiety. Conclusion: The prevalence of widespread transphobia and discrimination faced by the transgender community has culminated in high levels of stress and anxiety in the transgender population and shows variance according to multiple socio-demographic factors. Educating people about the LGBT community formation of support groups, policies and laws are required to establish trust and promote integration.

Keywords: transgender, gender, stress, anxiety, mental health, discrimination, exclusion

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Authors: Ameur Abdelkader, Abed Bouarfa Hafida

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Since its inception, predictive analysis has revolutionized the IT industry through its robustness and decision-making facilities. It involves the application of a set of data processing techniques and algorithms in order to create predictive models. Its principle is based on finding relationships between explanatory variables and the predicted variables. Past occurrences are exploited to predict and to derive the unknown outcome. With the advent of big data, many studies have suggested the use of predictive analytics in order to process and analyze big data. Nevertheless, they have been curbed by the limits of classical methods of predictive analysis in case of a large amount of data. In fact, because of their volumes, their nature (semi or unstructured) and their variety, it is impossible to analyze efficiently big data via classical methods of predictive analysis. The authors attribute this weakness to the fact that predictive analysis algorithms do not allow the parallelization and distribution of calculation. In this paper, we propose to extend the predictive analysis algorithm, Classification And Regression Trees (CART), in order to adapt it for big data analysis. The major changes of this algorithm are presented and then a version of the extended algorithm is defined in order to make it applicable for a huge quantity of data.

Keywords: predictive analysis, big data, predictive analysis algorithms, CART algorithm

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Authors: Agata Zakrzewska, Dominik Kopeć, Adrian Ochtyra

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The growing number of new cameras, sensors, and research methods allow for a broader application of thermal data in remote sensing vegetation studies. The aim of this research was to check whether it is possible to use thermal infrared data with a spectral range (3.6-4.9 μm) obtained during the day and the night to assess the health condition of selected species of deciduous trees in an urban environment. For this purpose, research was carried out in the city center of Warsaw (Poland) in 2020. During the airborne data acquisition, thermal data, laser scanning, and orthophoto map images were collected. Synchronously with airborne data, ground reference data were obtained for 617 studied species (Acer platanoides, Acer pseudoplatanus, Aesculus hippocastanum, Tilia cordata, and Tilia × euchlora) in different health condition states. The results were as follows: (i) healthy trees are cooler than trees in poor condition and dying both in the daytime and nighttime data; (ii) the difference in the canopy temperatures between healthy and dying trees was 1.06oC of mean value on the nighttime data and 3.28oC of mean value on the daytime data; (iii) condition classes significantly differentiate on both daytime and nighttime thermal data, but only on daytime data all condition classes differed statistically significantly from each other. In conclusion, the aerial thermal data can be considered as an alternative to hyperspectral data, a method of assessing the health condition of trees in an urban environment. Especially data obtained during the day, which can differentiate condition classes better than data obtained at night. The method based on thermal infrared and laser scanning data fusion could be a quick and efficient solution for identifying trees in poor health that should be visually checked in the field.

Keywords: middle wave infrared, thermal imagery, tree discoloration, urban trees

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Authors: Kashyap L. Mokariya, Varsha A. Shah, Makarand M. Lokhande

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As the current status and growth of Indian automobile industry is remarkable, transportation sectors are the main concern in terms of Energy security and climate change. Rising demand of fuel and its dependency on other countries affects the GDP of nation. So in this context if the 10 percent of vehicle got operated in Electrical mode how much saving in terms of Rs and in terms of liters is achieved has been analyzed which is also a part of Nations Electric mobility mission plan. Analysis is also done for converting unit consumption of Electricity of Electric vehicle into equivalent fuel consumption in liters which shows that at present tariff rate Electrical operated vehicles are far more beneficial. It also gives benchmark to the authorities to set the tariff rate for Electrical vehicles. Current situation of Indian grid is shown and how the Gap between Generation and Demand can be reduced is analyzed in terms of increasing generation capacity and Energy Conservation measures. As the certain regions of country is facing serious deficit than how to take energy conservation measures in Industry and especially in rural areas where generally Energy Auditing is not carried out that is analyzed in context of Electric vehicle penetration in near future. Author was a part of Vishvakarma yojna where in 255 villages of Gujarat Energy losses were measured and solutions were given to mitigate them and corresponding report to the authorities of villages was delivered.

Keywords: vehiclepenetration, feasibility, Energyconservation, future grid, Energy security, pf controller

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Authors: Memar Ayalew Demeke, Solomon Gebreyohans Gebru

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In Africa, Regional Economic Communities (RECs) were initially established to tackle the economic challenges of the continent. However, overtime, they expanded their mandate to deal with the security threats of the continent such as terrorism. In fact, the fight against terrorism has been internationalized following the September 9/11 terrorist attack in the U.S.A. Since then, RECs have been giving considerable attention to preventing and combating terrorism in their respective regions. Similarly, IGAD has been involved in preventing and combating terrorism. So far, however, little has been done with regard to what IGAD has performed in fighting terrorism. Therefore, this study was intended to describe and analyze the legal and practical activities carried out by IGAD in its fight against terrorism in the region general and in Somalia in particular. Both descriptive and analytical methods were employed and data were analyzed through qualitative approach. Finally, based on the findings, the study argues that, instead of over-reliance on hard power as a means of fighting terrorism, IGAD should invest more on the political and socio-economic problems of its member states so as to address the root causes.

Keywords: regional economic communities, IGAD, terrorism, treaties, conventions

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Authors: Li Jiuen Ong, Magdalin Cheong, Sri Rahayu, Lek Alexander, Pei Ting Tan

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Background: Previous programme evaluations from the diabetes training programme conducted in Changi General Hospital revealed that healthcare professionals (HCPs) are keen to receive advance diabetes training and education, specifically in medical, nutritional therapy. HCPs also expressed a preference for interactive activities over didactic teaching methods to enhance their learning. Since the War on Diabetes was initiated by MOH in 2016, HCPs are challenged to be actively involved in continuous education to be better equipped to reduce the growing burden of diabetes. Hence, streamlining training to incorporate an element of fun is of utmost importance. Aim: The nutrition programme incorporates game play using an interactive board game that aims to provide a more conducive and less stressful environment for learning. The board game could be adapted for training of community HCPs, health ambassadors or caregivers to cope with the increasing demand of diabetes care in the hospital and community setting. Methodology: Stages for game’s conception (Jaffe, 2001) were adopted in the development of the interactive board game ‘Sweet Score™ ’ Nutrition concepts and topics in diabetes self-management are embedded into the game elements of varying levels of difficulty (‘Easy,’ ‘Medium,’ ‘Hard’) including activities such as a) Drawing/ sculpting (Pictionary-like) b)Facts/ Knowledge (MCQs/ True or False) Word definition) c) Performing/ Charades To study the effects of game play on knowledge acquisition and perceived experiences, participants were randomised into two groups, i.e., lecture group (control) and game group (intervention), to test the difference. Results: Participants in both groups (control group, n= 14; intervention group, n= 13) attempted a pre and post workshop quiz to assess the effectiveness of knowledge acquisition. The scores were analysed using paired T-test. There was an improvement of quiz scores after attending the game play (mean difference: 4.3, SD: 2.0, P<0.001) and the lecture (mean difference: 3.4, SD: 2.1, P<0.001). However, there was no significance difference in the improvement of quiz scores between gameplay and lecture (mean difference: 0.9, 95%CI: -0.8 to 2.5, P=0.280). This suggests that gameplay may be as effective as a lecture in terms of knowledge transfer. All the13 HCPs who participated in the game rated 4 out of 5 on the likert scale for the favourable learning experience and relevance of learning to their job, whereas only 8 out of 14 HCPs in the lecture reported a high rating in both aspects. 16. Conclusion: There is no known board game currently designed for diabetes training for HCPs.Evaluative data from future training can provide insights and direction to improve the game format and cover other aspects of diabetes management such as self-care, exercise, medications and insulin management. Further testing of the board game to ensure learning objectives are met is important and can assist in the development of awell-designed digital game as an alternative training approach during the COVID-19 pandemic. Learning through gameplay increases opportunities for HCPs to bond, interact and learn through games in a relaxed social setting and potentially brings more joy to the workplace.

Keywords: active learning, game, diabetes, nutrition

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Authors: Juhan Kim, Jinsoo Kim

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South Korea cannot import natural gas in any form other than LNG because of the division of South and North Korea. Further, the high proportion of natural gas in the national energy mix makes this resource crucial for energy security in Korea. Expansion of Panama Canal will allow for reducing the cost of shipping between the Far East and U.S East. Panama Canal expansion can have significant impacts on South Korea. Due to this situation, we review the natural gas optimal portfolio by considering the uniqueness of the Korean Natural gas market and expansion of Panama Canal. In order to assess Korea’s natural gas optimal portfolio, we developed natural gas portfolio model. The model comprises two steps. First, to obtain the optimal long-term spot contract ratio, the study examines the price level and the correlation between spot and long-term contracts by using the Markowitz, portfolio model. The optimal long-term spot contract ratio follows the efficient frontier of the cost/risk level related to this price level and degree of correlation. Second, by applying the obtained long-term contract purchase ratio as the constraint in the linear programming portfolio model, we determined the natural gas optimal import portfolio that minimizes total intangible and tangible costs. Using this model, we derived the optimal natural gas portfolio considering the expansion of Panama Canal. Based on these results, we assess the portfolio for natural gas import to Korea from the perspective of energy security and present some relevant policy proposals.

Keywords: natural gas, Panama Canal, portfolio analysis, South Korea

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Authors: Z. Abdullah, A. R. Hamdan

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Clustering is a process of grouping objects and data into groups of clusters to ensure that data objects from the same cluster are identical to each other. Clustering algorithms in one of the areas in data mining and it can be classified into partition, hierarchical, density based, and grid-based. Therefore, in this paper, we do a survey and review for four major hierarchical clustering algorithms called CURE, ROCK, CHAMELEON, and BIRCH. The obtained state of the art of these algorithms will help in eliminating the current problems, as well as deriving more robust and scalable algorithms for clustering.

Keywords: clustering, unsupervised learning, algorithms, hierarchical

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Authors: Madre Paarlber, Alwiena Blignaut

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Background: Medication administration errors remains a global patient safety problem targeted by the WHO (World Health Organization), yet research on this matter is sparce within the South African context. Objective: The aim was to explore and describe nurses’ (medication administrators) perceptions regarding medication administration safety-related culture, incidence, causes, and reporting in the Gauteng Province of South Africa, and to determine any relationships between perceived variables concerned with medication safety (safety culture, incidences, causes, reporting of incidences, and reasons for non-reporting). Method: A quantitative research design was used through which self-administered online surveys were sent to 768 nurses (medication administrators) (n=217). The response rate was 28.26%. The survey instrument was synthesised from the Agency of Healthcare Research and Quality (AHRQ) Hospital Survey on Patient Safety Culture, the Registered Nurse Forecasting (RN4CAST) survey, a survey list prepared from a systematic review aimed at generating a comprehensive list of medication administration error causes and the Medication Administration Error Reporting Survey from Wakefield. Exploratory and confirmatory factor analyses were used to determine the validity and reliability of the survey. Descriptive and inferential statistical data analysis were used to analyse quantitative data. Relationships and correlations were identified between items, subscales and biographic data by using Spearmans’ Rank correlations, T-Tests and ANOVAs (Analysis of Variance). Nurses reported on their perceptions of medication administration safety-related culture, incidence, causes, and reporting in the Gauteng Province. Results: Units’ teamwork deemed satisfactory, punitive responses to errors accentuated. “Crisis mode” working, concerns regarding mistake recording and long working hours disclosed as impacting patient safety. Overall medication safety graded mostly positively. Work overload, high patient-nurse ratios, and inadequate staffing implicated as error-inducing. Medication administration errors were reported regularly. Fear and administrative response to errors effected non-report. Non-report of errors’ reasons was affected by non-punitive safety culture. Conclusions: Medication administration safety improvement is contingent on fostering a non-punitive safety culture within units. Anonymous medication error reporting systems and auditing nurses’ workload are recommended in the quest of improved medication safety within Gauteng Province private hospitals.

Keywords: incidence, medication administration errors, medication safety, reporting, safety culture

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Authors: K. Umbleja, M. Ichino

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Symbolic data mining has been developed to analyze data in very large datasets. It is also useful in cases when entry specific details should remain hidden. Symbolic data mining is quickly gaining popularity as datasets in need of analyzing are becoming ever larger. One type of such symbolic data is a histogram, which enables to save huge amounts of information into a single variable with high-level of granularity. Other types of symbolic data can also be described in histograms, therefore making histogram a very important and general symbolic data type - a method developed for histograms - can also be applied to other types of symbolic data. Due to its complex structure, analyzing histograms is complicated. This paper proposes a method, which allows to compare two histogram-valued variables and therefore find a dissimilarity between two histograms. Proposed method uses the Ichino-Yaguchi dissimilarity measure for mixed feature-type data analysis as a base and develops a dissimilarity measure specifically for histogram data, which allows to compare histograms with different number of bins and bin widths (so called general histogram). Proposed dissimilarity measure is then used as a measure for clustering. Furthermore, linkage method based on weighted averages is proposed with the concept of cluster compactness to measure the quality of clustering. The method is then validated with application on real datasets. As a result, the proposed dissimilarity measure is found producing adequate and comparable results with general histograms without the loss of detail or need to transform the data.

Keywords: dissimilarity measure, hierarchical clustering, histograms, symbolic data analysis

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Authors: Botshelo Rachel Sebola

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Unintended pregnancy often results in pregnancy termination. Most countries have legalised the termination of a pregnancy and pregnant adolescents can visit designated clinics without their parents’ consent. In most African and Asian countries, certain cultural practices are performed following any form of childbirth, including abortion, and such practices are ingrained in societies. The aim of this paper was to understand how women who terminated a pregnancy during adolescence coped by embracing cultural practices. A descriptive multiple case study design was adopted for the study. In-depth, semi-structured interviews and reflective diaries were used for data collection. Thirteen women aged 25 to 35 who had terminated a pregnancy in adolescence participated in the study. Three women kept their soiled sanitary pads, burned them to ash and waited for the rainy season to scatter the ash in a flowing stream. This ritual was performed to appease the ancestors, ask them for forgiveness and as a send-off for the aborted foetus. Five women secretly consulted Sangoma (traditional healers) to perform certain rituals. Three women isolated themselves to perform herbal cleansings, and the last two chose not to engage in any sexual activity for one year, which led to the loss of their partners. This study offers a unique contribution to understanding the solitary journey of women who terminated a pregnancy. The study challenges healthcare professionals who work in clinics that offer pregnancy termination services to look beyond releasing the foetus to advocating and providing women with the necessary care and support in performing cultural practices.

Keywords: adolescence, cultural practices, case study, pregnancy

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Authors: Taimaz Larimian

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During recent years, sustainable development has increasingly influenced urban policy, housing and planning in cities all over the world. Debates about sustainability no longer consider it solely as an environmental concern, but also incorporate social and economic dimensions. However, while a social dimension of sustainability is extensively accepted, the exact definition of the concept is still vague and unclear. This study is addressing this lack of specificity through a detailed exploration of social sustainability as the least studied pillar of sustainable development and sheds light on the debate over the definition of social sustainability through developing a measurement model of the constitutive dimensions of the concept. With this aim, a conceptual framework is developed based on the existing literature, determining seven main dimensions of the social sustainability concept namely: social interaction, safety and security, social equity, social participation, neighborhood satisfaction, housing satisfaction and sense of place. The validity and reliability of the model is then tested using exploratory and confirmatory factor analysis. In order to do so, five case study neighborhoods from Dunedin city with a range of urban forms and characters are investigated, to define social sustainability concept and its consisting dimensions from people’s perspective. The findings of this study present a clear definition of social sustainability at neighborhood scale and highlight all different dimensions of the concept in the context of New Zealand cities. According to the results, among the investigated dimensions, neighborhood satisfaction and safety and security had the most influence on people’s feeling of social sustainability in their neighborhood.

Keywords: social sustainability, factor analysis, neighborhood level, New Zealand cities

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Authors: Benjamin Arazi

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Blockchain is the technology behind cryptocurrencies like Bitcoin. It revolutionizes the meaning of trust in the sense of offering total reliability without relying on any central entity that controls or supervises the system. The Wall Street Journal states: “Blockchain Marks the Next Step in the Internet’s Evolution”. Blockchain was listed as #1 in Linkedin – The Learning Blog “most in-demand hard skills needed in 2020”. As stated there: “Blockchain’s novel way to store, validate, authorize, and move data across the internet has evolved to securely store and send any digital asset”. GSMA, a leading Telco organization of mobile communications operators, declared that “Blockchain has the potential to be for value what the Internet has been for information”. Motivated by these seminal observations, this paper presents the foundations of a Blockchain-based “Internet of Assets” academic program that joins under one roof leading application areas that are characterized by the transfer of assets over communication lines. Two such areas, which are pillars of our economy, are Fintech – Financial Technology and mobile communications services. The next application in line is Healthcare. These challenges are met based on available extensive professional literature. Blockchain-based assets communication is based on extending the principle of Bitcoin, starting with the basic question: If digital money that travels across the universe can ‘prove its own validity’, can this principle be applied to digital content. A groundbreaking positive answer here led to the concept of “smart contract” and consequently to DLT - Distributed Ledger Technology, where the word ‘distributed’ relates to the non-existence of reliable central entities or trusted third parties. The terms Blockchain and DLT are frequently used interchangeably in various application areas. The World Bank Group compiled comprehensive reports, analyzing the contribution of DLT/Blockchain to Fintech. The European Central Bank and Bank of Japan are engaged in Project Stella, “Balancing confidentiality and auditability in a distributed ledger environment”. 130 DLT/Blockchain focused Fintech startups are now operating in Switzerland. Blockchain impact on mobile communications services is treated in detail by leading organizations. The TM Forum is a global industry association in the telecom industry, with over 850 member companies, mainly mobile operators, that generate US$2 trillion in revenue and serve five billion customers across 180 countries. From their perspective: “Blockchain is considered one of the digital economy’s most disruptive technologies”. Samples of Blockchain contributions to Fintech (taken from a World Bank document): Decentralization and disintermediation; Greater transparency and easier auditability; Automation & programmability; Immutability & verifiability; Gains in speed and efficiency; Cost reductions; Enhanced cyber security resilience. Samples of Blockchain contributions to the Telco industry. Establishing identity verification; Record of transactions for easy cost settlement; Automatic triggering of roaming contract which enables near-instantaneous charging and reduction in roaming fraud; Decentralized roaming agreements; Settling accounts per costs incurred in accordance with agreement tariffs. This clearly demonstrates an academic education structure where fundamental technologies are studied in classes together with these two application areas. Advanced courses, treating specific implementations then follow separately. All are under the roof of “Internet of Assets”.

Keywords: blockchain, education, financial technology, mobile telecommunications services

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Authors: Majid Mokhtarnia, Alireza Amini

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Mobile operators deal with increasing in the data traffic as a critical issue. As a result, a vital responsibility of the operators is to deal with such a trend in order to create added values. This paper addresses a bundling method in a Canvas business model in a WiFi Data Offloading (WDO) strategy by which some elements of the model may be affected. In the proposed method, it is supposed to sell a number of data packages for subscribers in which there are some packages with a free given volume of data-offloaded WiFi complimentary. The paper on hands analyses this method in the views of attractiveness and profitability. The results demonstrate that the quality of implementation of the WDO strongly affects the final result and helps the decision maker to make the best one.

Keywords: bundling, canvas business model, telecommunication, WiFi data offloading

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Authors: Rayeheh Khatami, Toktam Hanaei, Mohammad Reza Mansouri Daneshvar

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The growth of the agricultural sector is known as an essential way to achieve development goals in developing countries. Urban agriculture is a way to reduce the vulnerability of urban populations of the world toward global environmental change. It is a sustainable and efficient system to respond to the environmental, social and economic needs of the city, which leads to urban sustainability. Today, many local and national governments are developing urban agriculture as an effective tool in responding to challenges such as poverty, food security, and environmental problems. In this study, we follow a perspective based on urban agriculture literature in order to indicate the urban agriculture’s benefits in environmental planning strategies in non-western countries like Iran. The methodological approach adopted is based on qualitative approach and documentary studies. A total of 35 articles (mixed quantitative and qualitative methods studies) were studied in final analysis, which are published in relevant journals that focus on this subject. Studies show the wide range of positive benefits of urban agriculture on food security, nutrition outcomes, health outcomes, environmental outcomes, and social capital. However, there was no definitive conclusion about the negative effects of urban agriculture. This paper provides a conceptual and theoretical basis to know about urban agriculture and its roles in environmental planning, and also conclude the benefits of urban agriculture for researchers, practitioners, and policymakers who seek to create spaces in cities for implementation urban agriculture in future.

Keywords: urban agriculture, environmental planning, urban planning, literature

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Authors: Jaweriya Aftab, Madho Mal, Hamida Memon

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The first case of coronavirus disease 2019 (COVID-19) in Pakistan was recorded on February 26th, 2020. While the country went through various phases of lockdowns, the importance of proper sensitization campaigns was highlighted by healthcare workers to combat misinformation. Past studies via Google trends analysis have shown a rise in public interest in multiple COVID-19-related symptoms as well as cardiovascular symptoms. As there is a paucity of data related to the trends in Pakistan, we conducted a retrospective analysis to bridge further information. Methods: As per the recommendations from past studies, a Google trend analysis was conducted for various symptoms, including ‘Fever’, ‘Chest Pain’, ‘Shortness of Breath’, and ‘Cough’ between 1st January 2019 to 31st December 2021. The trends in various search results were analyzed and modeled. Results: Our analysis found various rises in public interest in the various symptoms (fever, chest pain, shortness of breath, and cough) that correspond closely to the wave of the virus's spread in the country. Conclusion: Our study confirms similar trends in Pakistan as previously reported in studies from India, USA, and UK, whereby the public interest in various COVID-19 symptoms rose with the number of cases. This further highlights the need for a strong approach to combat misinformation during such a critical period.

Keywords: covid, trend, Pakistan, public

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Authors: Raymond Xu, Cindy Jingru Wang

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Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

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Authors: Tak-Chung Fu, Ying-Kit Hung, Fu-Lai Chung

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In the field of time series data mining, the concept of the Perceptually Important Point (PIP) identification process is first introduced in 2001. This process originally works for financial time series pattern matching and it is then found suitable for time series dimensionality reduction and representation. Its strength is on preserving the overall shape of the time series by identifying the salient points in it. With the rise of Big Data, time series data contributes a major proportion, especially on the data which generates by sensors in the Internet of Things (IoT) environment. According to the nature of PIP identification and the successful cases, it is worth to further explore the opportunity to apply PIP in time series ‘Big Data’. However, the performance of PIP identification is always considered as the limitation when dealing with ‘Big’ time series data. In this paper, two distributed versions of PIP identification based on the Specialized Binary (SB) Tree are proposed. The proposed approaches solve the bottleneck when running the PIP identification process in a standalone computer. Improvement in term of speed is obtained by the distributed versions.

Keywords: distributed computing, performance analysis, Perceptually Important Point identification, time series data mining

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Authors: Edward K. Boahen, Brunel E. Bouya-Moko, Changda Wang

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The major threat to online social network (OSN) users is account compromisation. Spammers now spread malicious messages by exploiting the trust relationship established between account owners and their friends. The challenge in detecting a compromised account by service providers is validating the trusted relationship established between the account owners, their friends, and the spammers. Another challenge is the increase in required human interaction with the feature selection. Research available on supervised learning (machine learning) has limitations with the feature selection and accounts that cannot be profiled, like application programming interface (API). Therefore, this paper discusses the various behaviours of the OSN users and the current approaches in detecting a compromised OSN account, emphasizing its limitations and challenges. We propose a deep learning approach that addresses and resolve the constraints faced by the previous schemes. We detailed our proposed optimized nonsymmetric deep auto-encoder (OPT_NDAE) for unsupervised feature learning, which reduces the required human interaction levels in the selection and extraction of features. We evaluated our proposed classifier using the NSL-KDD and KDDCUP'99 datasets in a graphical user interface enabled Weka application. The results obtained indicate that our proposed approach outperformed most of the traditional schemes in OSN compromised account detection with an accuracy rate of 99.86%.

Keywords: computer security, network security, online social network, account compromisation

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Authors: Botshelo Rachel Sebola

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Unintended pregnancy often results in pregnancy termination. Most countries have legalised the termination of a pregnancy, and pregnant adolescents can visit designated clinics without their parents’ consent. In most African and Asian countries, certain cultural practices are performed following any form of childbirth, including abortion, and such practices are ingrained in societies. The aim of this paper was to understand how women who terminated a pregnancy during adolescence coped by embracing cultural practices. A descriptive multiple case study design was adopted for the study. In-depth, semi-structured interviews and reflective diaries were used for data collection. 13 women aged 20 to 35 years who had terminated a pregnancy in adolescence participated in the study. Three women kept their soiled sanitary pads, burned them to ash and waited for the rainy season to scatter the ash in a flowing stream. This ritual was performed to appease the ancestors, ask them for forgiveness and as a send-off for the aborted foetus. Five women secretly consulted Sangoma (traditional healers) to perform certain rituals. Three women isolated themselves to perform herbal cleansings, and the last two chose not to engage in any sexual activity for one year, which led to the loss of their partners. This study offers a unique contribution to understanding the solitary journey of women who terminate a pregnancy. The study challenges healthcare professionals who work in clinics that offer pregnancy termination services to look beyond releasing the foetus to advocating and providing women with the necessary care and support in performing cultural practices.

Keywords: adolescence, culture, case study, pregnancy

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Authors: Philipp Ruf, Massiwa Chabbi, Christoph Reich, Djaffar Ould-Abdeslam

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In recent years, convolutional neural networks (CNN) have demonstrated high performance in image analysis, but oftentimes, there is only structured data available regarding a specific problem. By interpreting structured data as images, CNNs can effectively learn and extract valuable insights from tabular data, leading to improved predictive accuracy and uncovering hidden patterns that may not be apparent in traditional structured data analysis. In applying a single neural network for analyzing multimodal data, e.g., both structured and unstructured information, significant advantages in terms of time complexity and energy efficiency can be achieved. Converting structured data into images and merging them with existing visual material offers a promising solution for applying CNN in multimodal datasets, as they often occur in a medical context. By employing suitable preprocessing techniques, structured data is transformed into image representations, where the respective features are expressed as different formations of colors and shapes. In an additional step, these representations are fused with existing images to incorporate both types of information. This final image is finally analyzed using a CNN.

Keywords: CNN, image processing, tabular data, mixed dataset, data transformation, multimodal fusion

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Authors: Filiz Ersoz, Taner Ersoz, Erkin Guler

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This paper addresses the issues and technique for textile industry using data mining techniques. Data mining has been applied to the stitching of garments products that were obtained from a textile company. Data mining techniques were applied to the data obtained from the CHAID algorithm, CART algorithm, Regression Analysis and, Artificial Neural Networks. Classification technique based analyses were used while data mining and decision model about the production per person and variables affecting about production were found by this method. In the study, the results show that as the daily working time increases, the production per person also decreases. In addition, the relationship between total daily working and production per person shows a negative result and the production per person show the highest and negative relationship.

Keywords: data mining, textile production, decision trees, classification

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Authors: Bernice Boafoaa Gyapong, Anne Jones, Sam Bassett, Janet Anderson

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Background: Maternal mortality and morbidity are global concerns, especially in sub-Saharan Africa (SSA). Most maternal mortalities occur at the time of birth. Quality intrapartum care is essential for improving maternal and newborn health outcomes. This scoping review aimed to assess and describe the quality of care during childbirth in SSA to provide an overview of the regional trend of the quality of intrapartum care, the challenges to quality care provision, and identify research gaps. Methods: A scoping review based on Arksey and O’Malley’s scoping review framework was conducted. Medline, CINAHL, PsycINFO, and maternal-infant databases were searched to identify the relevant studies for this review. A narrative summary was presented using themes based on the Donabedian structure, process, and outcome quality of care model. Results: A total of five hundred and forty-seven (547) publications were identified. Fifty-six (56) studies conducted in twenty (20) countries were included in the review. Thirty-four (34) were quantitative, sixteen (16) were qualitative, and six (6) were mixed methods. Most of the studies were related to the process component of quality of care. The provision of emergency obstetric care services, infrastructure, and availability of essential staff and equipment for perinatal care was inadequate in many facilities, particularly rural and peripheral health facilities. Many women experienced disrespectful care during childbirth. Routine care during labour and delivery was observed to be sub-optimal, yet some women reported high satisfaction with care. The use of health facilities for delivery was lower in health centres compared to hospitals. Conclusion: There are variations in the quality of maternity care provided in SSA. Intrapartum care quality is generally deficient in SSA, particularly in peripheral health facilities, health centres, and community clinics. Many of the quality-of-care issues identified are related to the structure component. Stakeholders must develop interventions that comprehensively address these interrelated issues to improve maternal healthcare quality, especially in primary healthcare facilities.

Keywords: quality of care, maternity health, Sub-Saharan Africa, intrapartum

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Authors: Sneha Shankar, Orlando Buendia, Will Evans

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Alpha-1 antitrypsin deficiency (AATD) is a rare, genetic, and multisystemic condition. Underdiagnosis is common, leading to chronic pulmonary and hepatic complications, increased resource utilization, and additional costs to the healthcare system. Currently, there is limited evidence of the direct medical costs of AATD diagnosis in the UK. This study explores the economic impact of AATD patients during the 3 years before diagnosis and to identify the major cost drivers using primary and secondary care electronic health record (EHR) data. The 3 years before diagnosis time period was chosen based on the ability of our tool to identify patients earlier. The AATD algorithm was created using published disease criteria and applied to 148 known AATD patients’ EHR found in a primary care database of 936,148 patients (413,674 Biobank and 501,188 in a single primary care locality). Among 148 patients, 9 patients were flagged earlier by the tool and, on average, could save 3 (1-6) years per patient. We analysed 101 of the 148 AATD patients’ primary care journey and 20 patients’ Hospital Episode Statistics (HES) data, all of whom had at least 3 years of clinical history in their records before diagnosis. The codes related to laboratory tests, clinical visits, referrals, hospitalization days, day case, and inpatient admissions attributable to AATD were examined in this 3-year period before diagnosis. The average cost per patient was calculated, and the direct medical costs were modelled based on the mean prevalence of 100 AATD patients in a 500,000 population. A deterministic sensitivity analysis (DSA) of 20% was performed to determine the major cost drivers. Cost data was obtained from the NHS National tariff 2020/21, National Schedule of NHS Costs 2018/19, PSSRU 2018/19, and private care tariff. The total direct medical cost of one hundred AATD patients three years before diagnosis in primary and secondary care in the UK was £3,556,489, with an average direct cost per patient of £35,565. A vast majority of this total direct cost (95%) was associated with inpatient admissions (£3,378,229). The DSA determined that the costs associated with tier-2 laboratory tests and inpatient admissions were the greatest contributors to direct costs in primary and secondary care, respectively. This retrospective study shows the role of EHRs in calculating direct medical costs and the potential benefit of new technologies for the early identification of patients with AATD to reduce the economic burden in primary and secondary care in the UK.

Keywords: alpha-1 antitrypsin deficiency, costs, digital health, early diagnosis

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Authors: Kalpana Gupta

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Sexually transmitted infections (STIs) are infections that are spread primarily through sexual contact. In our daily practice, we see gonorrhea, chancroid, syphilis, and chlamydial infections that can be cured, as well as HIV, genital herpes, HPV, and hepatitis B infections that cannot be cured but can be managed with available treatments. Many people in India are infected with Sexually transmitted diseases (STDs), and the figures are quite high because of a lack of awareness and communication, as well as a taboo against these diseases. Numerous taboos and associated stigma shape patients’ lives and have a significant impact on health care policies, medical research, and current issues in medical ethics. Current statistics emphasize the importance of delivering sex education to this important demographic promptly. The long-standing tradition of girls marrying very young, especially in rural areas, and often too much older men, causes a slew of STIs. Stigma and HIV have a cyclical relationship; people who experience stigma and discrimination are marginalized and made more vulnerable to HIV/STDs, while those living with HIV are more vulnerable to stigma and discrimination. As urban pressures have grown, so have slums - and they have fast become ideal breeding grounds for STDs. In developed countries, strict laws have been enacted requiring people suffering from STDs to seek immediate treatment as well as contact the health department. Unfortunately, because of the stigma associated with the disease, patients in India are reluctant to reveal the source of infection. With various schemes, India is attempting to promote sex education and awareness. For example, the Ministry of Health and Family Welfare developed the National Adolescent Health Programme (also known as the Rashtriya Kishor Swasthya Karyakram) in partnership with the United Nations Population Fund (UNFPA). Whereas, National AIDS Control Organisation was set up so that every person living with HIV has access to quality care and is treated with dignity and breaking all taboos. It becomes clear that research and healthcare policies will not be effective in assisting patients with STDs unless these "nonscientific" elements are taken into account.

Keywords: sexually transmitted diseases, sexually transmitted infections, taboo, stigma, HIV/STDs, sex education and awareness, treatment, quality care, medications, healthcare policies

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Authors: Saul Ngarava, Leocadia Zhou, Nomakhaya Monde

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Water crises have the fourth most significant societal impact after weapons of mass destruction, climate change, and extreme weather conditions, ahead of natural disasters. Intricacies between women and water are central to achieving the 2030 Sustainable Development Goals (SDGs). The majority of the 1.2 billion poor people worldwide, with two-thirds being women, and mostly located in Sub Sahara Africa (SSA) and South Asia, do not have access to safe and reliable sources of water. There exist gendered differences in water security based on the division of labour associating women with water. Globally, women and girls are responsible for water collection in 80% of the households which have no water on their premises. Women spend 16 million hours a day collecting water, while men and children spend 6 million and 4 million per day, respectively, which is time foregone in the pursuit of other livelihood activities. Due to their proximity and activities concerning water, women are vulnerable to water insecurity through exposures to water-borne diseases, fatigue from physically carrying water, and exposure to sexual and physical harassment, amongst others. Proximity to treated water and their wellbeing also has an effect on their sensitivity and adaptive capacity to water insecurity. The great distances, difficult terrain and heavy lifting expose women to vulnerabilities of water insecurity. However, few studies have quantified the vulnerabilities and burdens on women, with a few taking a phenomenological qualitative approach. Vulnerability studies have also been scanty in the water security realm, with most studies taking linear forms of either quantifying exposures, sensitivities or adaptive capacities in climate change studies. The current study argues for the need for a water insecurity vulnerability assessment, especially for women into research agendas as well as policy interventions, monitoring, and evaluation. The study sought to identify and provide pathways through which female-headed households were water insecure in South Africa, the 30th driest country in the world. This was through linking the drinking water decision as well as the vulnerability frameworks. Secondary data collected during the 2016 General Household Survey (GHS) was utilised, with a sample of 5928 female-headed households. Principal Component Analysis and Structural Equation Modelling were used to analyse the data. The results show dynamic relationships between water characteristics and water treatment. There were also associations between water access and wealth status of the female-headed households. Association was also found between water access and water treatment as well as between wealth status and water treatment. The study concludes that there are dynamic relationships in water insecurity (exposure, sensitivity, and adaptive capacity) for female-headed households in South Africa. The study recommends that a multi-prong approach is required in tackling exposures, sensitivities, and adaptive capacities to water insecurity. This should include capacitating and empowering women for wealth generation, improve access to water treatment equipment as well as prioritising the improvement of infrastructure that brings piped and safe water to female-headed households.

Keywords: gender, principal component analysis, structural equation modelling, vulnerability, water insecurity

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Authors: A. F. Jaber, E. Dean, M. Liu, J. He, D. Sabata, J. Radel

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Background: Stroke is a serious health care concern and a major cause of disability in the United States. This condition impacts the individual’s functional ability to perform daily activities. Predicting functional performance of people with stroke assists health care professionals in optimizing the delivery of health services to the affected individuals. The purpose of this study was to identify significant predictors of Motor FIM and of Cognitive FIM subscores among individuals with stroke after discharge from inpatient rehabilitation (typically 4-6 weeks after stroke onset). A second purpose is to explore the relation among personal characteristics, health status, and functional performance of daily activities within 2 weeks of stroke onset. Methods: This study used a retrospective chart review to conduct a secondary analysis of data obtained from the Healthcare Enterprise Repository for Ontological Narration (HERON) database. The HERON database integrates de-identified clinical data from seven different regional sources including hospital electronic medical record systems of the University of Kansas Health System. The initial HERON data extract encompassed 1192 records and the final sample consisted of 207 participants who were mostly white (74%) males (55%) with a diagnosis of ischemic stroke (77%). The outcome measures collected from HERON included performance scores on the National Institute of Health Stroke Scale (NIHSS), the Glasgow Coma Scale (GCS), and the Functional Independence Measure (FIM). The data analysis plan included descriptive statistics, Pearson correlation analysis, and Stepwise regression analysis. Results: significant predictors of discharge Motor FIM subscores included age, baseline Motor FIM subscores, discharge NIHSS scores, and comorbid electrolyte disorder (R2 = 0.57, p <0.026). Significant predictors of discharge Cognitive FIM subscores were age, baseline cognitive FIM subscores, client cooperative behavior, comorbid obesity, and the total number of comorbidities (R2 = 0.67, p <0.020). Functional performance on admission was significantly associated with age (p < 0.01), stroke severity (p < 0.01), and length of hospital stay (p < 0.05). Conclusions: our findings show that younger age, good motor and cognitive abilities on admission, mild stroke severity, fewer comorbidities, and positive client attitude all predict favorable functional outcomes after inpatient stroke rehabilitation. This study provides health care professionals with evidence to evaluate predictors of favorable functional outcomes early at stroke rehabilitation, to tailor individualized interventions based on their client’s anticipated prognosis, and to educate clients about the benefits of making lifestyle changes to improve their anticipated rate of functional recovery.

Keywords: functional performance, predictors, stroke, recovery

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