Search results for: healthcare data

Authors: H. W. Meerveld, R. H. A. Lindelauf

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In contemporary warfare, data science is crucial for the military in achieving information superiority. Yet, to the authors’ knowledge, no extensive literature survey on data science in military decision-making has been conducted so far. In this study, 156 peer-reviewed articles were analysed through an integrative, semi-systematic literature review to gain an overview of the topic. The study examined to what extent literature is focussed on the opportunities or risks of data science in military decision-making, differentiated per level of war (i.e. strategic, operational, and tactical level). A relatively large focus on the risks of data science was observed in social science literature, implying that political and military policymakers are disproportionally influenced by a pessimistic view on the application of data science in the military domain. The perceived risks of data science are, however, hardly addressed in formal science literature. This means that the concerns on the military application of data science are not addressed to the audience that can actually develop and enhance data science models and algorithms. Cross-disciplinary research on both the opportunities and risks of military data science can address the observed research gaps. Considering the levels of war, relatively low attention for the operational level compared to the other two levels was observed, suggesting a research gap with reference to military operational data science. Opportunities for military data science mostly arise at the tactical level. On the contrary, studies examining strategic issues mostly emphasise the risks of military data science. Consequently, domain-specific requirements for military strategic data science applications are hardly expressed. Lacking such applications may ultimately lead to a suboptimal strategic decision in today’s warfare.

Keywords: data science, decision-making, information superiority, literature review, military

Procedia PDF Downloads 168

Authors: Cai Qianyi

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In the midst of global digital revolution, the flow of data poses security threats that call China's existing legislative framework for protecting personal information into question. As a preliminary procedure for risk analysis and prevention, the risk assessment of personal data transmission lacks detailed guidelines for support. Existing provisions reveal unclear responsibilities for network operators and weakened rights for data subjects. Furthermore, the regulatory system's weak operability and a lack of industry self-regulation heighten data transmission hazards. This paper aims to compare the regulatory pathways for data information transmission risks between China and Europe from a legal framework and content perspective. It draws on the “Data Protection Impact Assessment Guidelines” to empower multiple stakeholders, including data processors, controllers, and subjects, while also defining obligations. In conclusion, this paper intends to solve China's digital security shortcomings by developing a more mature regulatory framework and industry self-regulation mechanisms, resulting in a win-win situation for personal data protection and the development of the digital economy.

Keywords: personal information data transmission, risk assessment, DPIA, internet service provider, personal information data transimission, risk assessment

Procedia PDF Downloads 61

Authors: Habiba Ben Abdessalem

Abstract:

The emergence of giant set of textual data was the push that has encouraged researchers to invest in this field. The purpose of textual data analysis methods is to facilitate access to such type of data by providing various graphic visualizations. Applying these methods requires a corpus pretreatment step, whose standards are set according to the objective of the problem studied. This step determines the forms list contained in contingency table by keeping only those information carriers. This step may, however, lead to noisy contingency tables, so the use of wavelet denoising function. The validity of the proposed approach is tested on a text database that offers economic and political events in Tunisia for a well definite period.

Keywords: textual data, wavelet, denoising, contingency table

Procedia PDF Downloads 277

Authors: Burcu Oralhan, Zeki Oralhan, Nilsun Sariyer, Kumru Uyar

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Data mining has been becoming more and more important and a wide range of applications in recent years. Data mining is the process of find hidden and unknown patterns in big data. One of the applied fields of data mining is Customer Relationship Management. Understanding the relationships between products and customers is crucial for every business. Customer Relationship Management is an approach to focus on customer relationship development, retention and increase on customer satisfaction. In this study, we made an application of a data mining methods in telecommunication customer relationship management side. This study aims to determine the customers profile who likely to leave the system, develop marketing strategies, and customized campaigns for customers. Data are clustered by applying classification techniques for used to determine the churners. As a result of this study, we will obtain knowledge from international telecommunication industry. We will contribute to the understanding and development of this subject in Customer Relationship Management.

Keywords: customer churn analysis, customer relationship management, data mining, telecommunication industry

Procedia PDF Downloads 317

Authors: N. R. N. Idris, S. Baharom

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A meta-analysis may be performed using aggregate data (AD) or an individual patient data (IPD). In practice, studies may be available at both IPD and AD level. In this situation, both the IPD and AD should be utilised in order to maximize the available information. Statistical advantages of combining the studies from different level have not been fully explored. This study aims to quantify the statistical benefits of including available IPD when conducting a conventional summary-level meta-analysis. Simulated meta-analysis were used to assess the influence of the levels of data on overall meta-analysis estimates based on IPD-only, AD-only and the combination of IPD and AD (mixed data, MD), under different study scenario. The percentage relative bias (PRB), root mean-square-error (RMSE) and coverage probability were used to assess the efficiency of the overall estimates. The results demonstrate that available IPD should always be included in a conventional meta-analysis using summary level data as they would significantly increased the accuracy of the estimates. On the other hand, if more than 80% of the available data are at IPD level, including the AD does not provide significant differences in terms of accuracy of the estimates. Additionally, combining the IPD and AD has moderating effects on the biasness of the estimates of the treatment effects as the IPD tends to overestimate the treatment effects, while the AD has the tendency to produce underestimated effect estimates. These results may provide some guide in deciding if significant benefit is gained by pooling the two levels of data when conducting meta-analysis.

Keywords: aggregate data, combined-level data, individual patient data, meta-analysis

Procedia PDF Downloads 375

Authors: Hyun-Woo Cho

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The monitoring of industrial production quality has to be implemented to alarm early warning for unusual operating conditions. Furthermore, identification of their assignable causes is necessary for a quality control purpose. For such tasks many multivariate statistical techniques have been applied and shown to be quite effective tools. This work presents a process data-based monitoring scheme for production processes. For more reliable results some additional steps of noise filtering and preprocessing are considered. It may lead to enhanced performance by eliminating unwanted variation of the data. The performance evaluation is executed using data sets from test processes. The proposed method is shown to provide reliable quality control results, and thus is more effective in quality monitoring in the example. For practical implementation of the method, an on-line data system must be available to gather historical and on-line data. Recently large amounts of data are collected on-line in most processes and implementation of the current scheme is feasible and does not give additional burdens to users.

Keywords: detection, filtering, monitoring, process data

Procedia PDF Downloads 559

Authors: Muhammad Awais Shafique, Eiji Hato

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Household trip data is of crucial importance for managing present transportation infrastructure as well as to plan and design future facilities. It also provides basis for new policies implemented under Transportation Demand Management. The methods used for household trip data collection have changed with passage of time, starting with the conventional face-to-face interviews or paper-and-pencil interviews and reaching to the recent approach of employing smartphones. This study summarizes the step-wise evolution in the travel data collection methods. It provides a comprehensive review of the topic, for readers interested to know the changing trends in the data collection field.

Keywords: computer, smartphone, telephone, travel survey

Procedia PDF Downloads 313

Authors: Hiroaki Nasu, Ryota Miyamoto, Yuta Kodera, Yasuyuki Nogami

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To promote Industry 4.0 and Society 5.0 and so on, it is important to connect and share data so that every member can trust it. Blockchain (BC) technology is currently attracting attention as the most advanced tool and has been used in the financial field and so on. However, the data collaboration using BC has not progressed sufficiently among companies on the supply chain of manufacturing industry that handle sensitive data such as product quality, manufacturing conditions, etc. There are two main reasons why data utilization is not sufficiently advanced in the industrial supply chain. The first reason is that manufacturing information is top secret and a source for companies to generate profits. It is difficult to disclose data even between companies with transactions in the supply chain. In the blockchain mechanism such as Bitcoin using PKI (Public Key Infrastructure), in order to confirm the identity of the company that has sent the data, the plaintext must be shared between the companies. Another reason is that the merits (scenarios) of collaboration data between companies are not specifically specified in the industrial supply chain. For these problems this paper proposes a Business to Business (B2B) collaboration system using homomorphic encryption and BC technique. Using the proposed system, each company on the supply chain can exchange confidential information on encrypted data and utilize the data for their own business. In addition, this paper considers a scenario focusing on quality data, which was difficult to collaborate because it is a top secret. In this scenario, we show a implementation scheme and a benefit of concrete data collaboration by proposing a comparison protocol that can grasp the change in quality while hiding the numerical value of quality data.

Keywords: business to business data collaboration, industrial supply chain, blockchain, homomorphic encryption

Procedia PDF Downloads 136

Authors: Ali Rashash Alzahrani, Elizabeth Stojanovski

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Data on various aspects of education are collected at the institutional and government level regularly. In Australia, for example, students at various levels of schooling undertake examinations in numeracy and literacy as part of NAPLAN testing, enabling longitudinal assessment of such data as well as comparisons between schools and states within Australia. Another source of educational data collected internationally is via the PISA study which collects data from several countries when students are approximately 15 years of age and enables comparisons in the performance of science, mathematics and English between countries as well as ranking of countries based on performance in these standardised tests. As well as student and school outcomes based on the tests taken as part of the PISA study, there is a wealth of other data collected in the study including parental demographics data and data related to teaching strategies used by educators. Overall, an abundance of educational data is available which has the potential to be used to help improve educational attainment and teaching of content in order to improve learning outcomes. A multivariate assessment of such data enables multiple variables to be considered simultaneously and will be used in the present study to help develop profiles of students based on performance in mathematics using data obtained from the PISA study.

Keywords: cluster analysis, education, mathematics, profiles

Procedia PDF Downloads 126

Authors: Etaf Khlaed Haroun Alkhlaifat

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When doctors and patients do not share the same first language, language barriers may exist, which may have negative effects on the quality of communication and care provided. Doctors’ use of medical jargon and patients’ inability to fully express their illness, to a potential loss of relevant information can often create misunderstanding. This study sought to examine the extent to which a lack of “common” language represents one of the linguistic obstacles that may adversely influence the quality of healthcare services in Jordan. Communication Accommodation Theory (CAT) was used to interpret the phenomena under study. Doctors (n=9) and patients (n=18) were observed and interviewed in natural Jordanian medical settings. A thematic qualitative approach was employed to analyse the data. The preliminary findings of the study revealed that most doctors appeared to have a good sense of appropriate ways to break through communication barriers by changing medical terminologies or jargons into lay terms. However, for some, there were two main challenges: 1) the use of medical jargon in explaining medication and side effects and 2) the lack of patients’ knowledge in providing a full explanation about their illnesses. The study revealed that language barriers adversely affect health outcomes for patients with limited fluency in the English language. It argues that it is doctors’ responsibility to guarantee mutual understanding, educate patients on their condition and improve their health outcomes.

Keywords: communication accommodation theory, doctor-patient interaction, language barrier, medical jargon, misunderstanding

Procedia PDF Downloads 84

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

Procedia PDF Downloads 140

Authors: Saarah Juman, Ilona Johnson, Stephen Richmond, Brett Duane, Sheelagh Rogers

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Introduction: Researchers suggest that within 50 years or less, the available supply of a range of metals will be exhausted, potentially leading to increases in resource conflict and largescale production shortages. The healthcare, dental and orthodontic sectors will undoubtedly be affected as stainless steel instruments are generally heavily relied on. Although changing orthodontic archwires are unavoidable and necessary to allow orthodontic tooth movement through the progression of an archwire sequence with fixed appliances, they are thought to be manufactured in excess of what is needed. Furthermore, orthodontic archwires require trimming extraorally to allow safe intraoral insertion, thus contributing to unnecessary waste of natural resources. Currently, there is no evidence to support the optimisation of archwire length according to orthodontic fixed appliance stage. As such, this study aims to quantify archwire excess (extraoral archwire trimmings) for different stages of orthodontic fixed appliance treatment. Methodology: This prospective, observational, quantitative study observed trimmings made extraorally against pre-treatment study models by clinicians over a 3-month period. Archwires were categorised into one of three categories (initial aligning, sequence, working/finishing arcwhires) within the orthodontic fixed appliance archwire sequence. Data collection included archwire material composition and the corresponding length and weight of excess archwire. Data was entered using a Microsoft Excel spreadsheet and imported into statistical software to obtain simple descriptive statistics. Results: Measurements were obtained for a total of 144 archwires. Archwire materials included nickel titanium and stainless steel. All archwires observed required extraorally trimming to allow safe intraoral insertion. The manufactured lengths of orthodontic initial aligning, sequence, and working/finishing arcwhires were at least 31%, 26%, and 39% in excess, respectively. Conclusions: Orthodontic archwires are manufactured to be excessively long at all orthodontic archwire sequence stages. To conserve natural resources, this study’s findings support the optimisation of orthodontic archwire lengths by manufacturers according to the typical stages of an orthodontic archwire sequence.

Keywords: archwire, orthodontics, sustainability, waste

Procedia PDF Downloads 196

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

Procedia PDF Downloads 142

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

Procedia PDF Downloads 115

Authors: Sumia Fatima, Tayyaba Idrees

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Cardiac Arrest and Heart Failures are an important causes of mortality in developed and developing countries and even a second spent without Cardiopulmonary Resuscitation (CPR) increases the risk of mortality. Youngs doctors are expected to partake in CPR from the first day and if they are not taught basic life support (BLS) skills during their studies. They have next to no opportunity to learn them in clinical settings. To determine the exact level of knowledge of Basic Life Support among medical students. To compare the degree of knowledge among 1st and 2nd year medical students of RMU (Rawalpindi Medical University), using self-structured questionnaires. A cross sectional, qualitative primary study was conducted in March 2020 in order to analyse theoretical and practical knowledge of Basic Life Support among Medical Students of 1st and 2nd year MBBS. Self-Structured Questionnaires were distributed among 300 students, 150 from 1st year and 150 from 2nd year. Data was analysed using SPSS v 22. Chi Square test was employed. The results showed that only 13 (4%) students had received formal BLS training.129 (42%) students had encountered accidents in real life but had not known how to react. Majority responded that Basic Life Support should be made part of medical college curriculum (189 students), 194 participants (64%) had moderate knowledge of both theoretical and practical aspects of BLS. 75-80% students of both 1st and 2nd year had only moderate knowledge, which must be improved for them to be better healthcare providers in future. It was also found that male students had more practical knowledge than females, but both had almost the same proficiency in theoretical knowledge. The study concluded that the level of knowledge of BLS among the students was not up to the mark, and there is a dire need to include BLS training in the medical colleges’ curriculum.

Keywords: basic cardiac life support, cardiac arrest, awareness, medical students

Procedia PDF Downloads 94

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

Procedia PDF Downloads 885

Authors: Syed Kashif Ali, Usman Javaid, Abdullah Chohan

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In large enterprises multiple departments use different sort of information systems and databases according to their needs. These systems are independent and heterogeneous in nature and sharing information/data between these systems is not an easy task. The usage of middleware technologies have made data sharing between systems very easy. However, monitoring the exchange of data/information for verification purposes between target and source systems is often complex or impossible for maintenance department due to security/access privileges on target and source systems. In this paper, we are intended to present our experience of an end to end data monitoring approach at middle ware level implemented in Oracle BPEL for data verification without any help of monitoring tool.

Keywords: service level agreement, SOA, BPEL, oracle fusion middleware, web service monitoring

Procedia PDF Downloads 481

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: 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: Ketan Tamirisa, Kathleen P. Tebb

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In the U.S., annually, there are over 1 million pregnancies in teenagers and most (85%) are unintended. The need for proactive prevention measures is imperative to support adolescents with their pregnancy prevention and family planning goals. To date, there is limited research examining the extent to which support from a sexual partner(s) influences contraceptive use. To address this gap, this study assessed the relationship between sexually active adolescents, sex-assigned birth as female, and their perceived support from their sexual partner(s) about their contraceptive use in the last three months. Baseline data from sexually active adolescent females, between 13-19 years who were not currently using a long-acting contraceptive device, were recruited from 32 school-based health centers (SBHCs) in seven states in the U.S. as part of a larger study to evaluate Health-E You/ Salud iTuTM, a web-based contraceptive decision support tool. Fisher’s exact test assessed the cross-sectional association between perceived sexual partner support of contraceptive use in the past three months (felt no support, felt little support, and felt a lot of support), and current use of non-barrier contraception. A total of 91 sexually active adolescent females were eligible and completed the baseline survey. The mean age was 16.7 and nearly half (49.3%) were Hispanic/Latina. Most (85.9%) indicated it was very important to avoid becoming pregnant. A total of 60 participants (65.9%) reported use of non-barrier contraception. Of these, most used birth control pills (n=26), followed by Depo-Provera injection (n=12), patch (n=1), and ring (n=1). Most of the participants (80.2%) indicated that they perceived a lot of support from their partners and 19.8% reported no or little support. Among those reporting a lot of support, 69.9% (51/73) reported current use of non-barrier contraception compared to 50% (9/18) who felt no/little support and reported contraceptive use. This difference approached but did not reach statistical significance (p=0.096). Results from this preliminary data indicate that many adolescents who are coming in for care at SBHCs are at risk of unintended pregnancy. Many participants also reported a lot of support from their sexual partner(s) to use contraception. While the associations only approached significance, this is likely due to the small sample size. This and future research can better understand this association to inform interventions aimed at sexual partners to strengthen education and social support, increase healthcare accessibility, and ultimately reduce rates of unintended pregnancy.

Keywords: adolescents, contraception, pregnancy, SBHCs, sexual partners

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Authors: Clemens Bulitta, Sasan Sadrizadeh, Sebastian Buhl

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Introduction: Ventilation technology in operating theatres (OT)is internationally regulated by dif-ferent standards, which define basic specifications for technical equipment and many times also the necessary operating and performance parameters. This confronts the operators of healthcare facilities with the question of finding the best ventilation and air conditioning system for the OT in order to achieve the goal of a large and robust surgicalworkzone with appropriate air quality and climate for patient safety and occupational health. Additionally, energy consumption and the potential need for clothing that limits transmission of bacteria must be considered as well as the total life cycle cost. However, the evaluation methodology of ventilation systems regarding these matters are still a topic of discussion. To date, there are neither any uniform standardized specifications nor any common validation criteria established. Thus, this study aimed to review data in the literature and add ourown research results to compare and assess the performance of different ventilations systems regarding infection preventive effects, energy efficiency, and staff comfort. Methods: We have conducted a comprehensive literature review on OT ventilation-related topics to understand the strengths and limitations of different ventilation systems. Furthermore, data from experimental assessments on OT ventilation systems at the University of Amberg-Weidenin Germany were in-cluded to comparatively assess the performance of Laminar Airflow (LAF), Turbulent Mixing Air-flow(TMA), and Temperature-controlled Airflow (TcAF) with regards to patient and occupational safety as well as staff comfort including indoor climate.CFD simulations from the Royal Institute of Technology in Sweden (KTH) were also studied to visualize the differences between these three kinds of ventilation systems in terms of the size of the surgical workzone, resilience to obstacles in the airflow, and energy use. Results: A variety of ventilation concepts are in use in the OT today. Each has its advantages and disadvantages, and thus one may be better suited than another depend-ing on the built environment and clinical workflow. Moreover, the proper functioning of OT venti-lation is also affected by multiple external and internal interfering factors. Based on the available data TcAF and LAF seem to provide the greatest effects regarding infection control and minimizing airborne risks for surgical site infections without the need for very tight surgical clothing systems. Resilience to obstacles, staff comfort, and energy efficiency seem to be favourable with TcAF. Conclusion: Based on literature data in current publications and our studies at the Technical Uni-versity of Applied Sciences Amberg-Weidenand the Royal Institute of Technoclogy, LAF and TcAF are more suitable for minimizing the risk for surgical site infections leading to improved clin-ical outcomes. Nevertheless, regarding the best management of thermal loads, atmosphere, energy efficiency, and occupational safety, overall results and data suggest that TcAF systems could pro-vide the economically most efficient and clinically most effective solution under routine clinical conditions.

Keywords: ventilation systems, infection control, energy efficiency, operating theatre, airborne infection risks

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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: Chaliya Wamaloon, Malee Chaisaena, Nusara Prasertsri

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Informal caregivers providing home-based palliative and end-of-life (EOL) care to people with advanced cancer is needed, however, there has not been develop caregiver skills for the EOL in cancer patients. The aim of this research was to study the model development of caregiver skills for the EOL in cancer patients. Mixed methods research was conducted in 3 phases. All subjects were in Ubon Rathchathani Cancer Hospital including 30 EOL cancer patient caregivers, 30 EOL cancer patients, and 111 health care professionals who provided care for the EOL cancer patients and 30 EOL target participants who had been trained to be cancer patient caregivers. The research tools were questionnaires, semi structured interviews, and caregiver skills questionnaires. Data were analyzed by using percentage, mean, standard deviation, pair t-test, and content analysis. The result from this study showed the model development of caregiver skills for cancer patients consisted of 9 domains skills: 1. monitoring, 2. interpreting, 3. making decisions, 4. taking action, 5. making adjustments, 6. providing hands-on care, 7. accessing resources, 8. working together with the ill patients, and 9. navigating the healthcare system. The model composed of skills development curriculum for cancer patient caregivers, Manual of palliative care for caregivers, diary of health care records for cancer patients, and the evaluation model of development of caregiver skills for EOL cancer patients. The results of the evaluation in the development model of caregiver skills for EOL cancer patients showed that the caregivers were satisfied with the model of development for caregiver skills at a high level. The comparison of the caregiver skills before and after obtaining the development of caregivers skills revealed that it improved at a statistically significant level (p < 0.05).

Keywords: caregiver, caregiver skills, cancer patients, end of life

Procedia PDF Downloads 169

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

Procedia PDF Downloads 350

Authors: Atul Khurana, Rajul Rastogi, Hans-Joachim Gamperl

Abstract:

The pharmaceutical companies are getting more inclined towards patient support programs (PSPs) which assist patients and/or healthcare professionals (HCPs) in more desirable disease management and cost-effective treatment. The utmost objective of these programs is patient care. The PSPs may include financial assistance to patients, medicine compliance programs, access to HCPs via phone or online chat centers, etc. The PSP has a crucial role in terms of customer acquisition and retention strategies. During the conduct of these programs, Marketing Authorisation Holder (MAH) may receive information related to concerned medicinal products, which is usually reported by patients or involved HCPs. This information may include suspected adverse reaction(s) during/after administration of medicinal products. Hence, the MAH should design PSP to comply with regulatory reporting requirements and avoid non-compliance during PV inspection. The emergence of wireless health devices is lowering the burden on patients to manually incorporate safety data, and building a significant option for patients to observe major swings in reference to drug safety. Therefore, to enhance the adoption of these programs, MAH not only needs to aware patients about advantages of the program, but also recognizes the importance of time of patients and commitments made in a constructive manner. It is indispensable that strengthening the public health is considered as the topmost priority in such programs, and the MAH is compliant to Pharmacovigilance (PV) requirements along with regulatory obligations.

Keywords: drug safety, good pharmacovigilance practice, patient support program, pharmacovigilance

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Authors: Ashima Anurag Sharma

Abstract:

Optical fiber based networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This research paper is targeted to show the simultaneous delivery of triple play service (data, voice, and video). The comparison between various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

Procedia PDF Downloads 527

Authors: Fuad M. Alkoot

Abstract:

Different experimental technologies such as microarray sequencing have been proposed to generate high-resolution genetic data, in order to understand the complex dynamic interactions between complex diseases and the biological system components of genes and gene products. However, the generated samples have a very large dimension reaching thousands. Therefore, hindering all attempts to design a classifier system that can identify diseases based on such data. Additionally, the high overlap in the class distributions makes the task more difficult. The data we experiment with is generated for the identification of autism. It includes 142 samples, which is small compared to the large dimension of the data. The classifier systems trained on this data yield very low classification rates that are almost equivalent to a guess. We aim at reducing the data dimension and improve it for classification. Here, we experiment with applying a multistage PCA on the genetic data to reduce its dimensionality. Results show a significant improvement in the classification rates which increases the possibility of building an automated system for autism detection.

Keywords: PCA, gene expression, dimensionality reduction, classification, autism

Procedia PDF Downloads 560

Authors: Y. Alqaheem, A. Alomair, F. Altarkait, F. Alswaileh, Nusrat Tanoli

Abstract:

Helium market is continuously growing due to its essential uses in the electronic and healthcare sectors. Currently, helium is produced by cryogenic distillation but the process is uneconomical especially for low production volumes. On the other hand, polymeric membranes can provide a cost-effective solution for helium purification due to their low operating energy. However, the preparation of membranes involves the use of very toxic solvents such as chloroform. In this work, polyetherimide membranes were prepared using a less toxic solvent, n-methylpyrrolidone with a polymer-to-solvent ratio of 27 wt%. The developed membrane showed a superior helium permeability of 15.9 Barrer that surpassed the permeability of membranes made by chloroform.

Keywords: helium separation, polyetherimide, dense membrane, gas permeability

Procedia PDF Downloads 168

Authors: Chang Qin, Daham Mustafa, Abderrahmane Khiat, Pierre Bienert, Paulo Zanini

Abstract:

Nowadays, companies are facing lots of challenges in the process of digital transformation, which can be a complex and costly undertaking. Digital transformation involves the collection and analysis of large amounts of data, which can create challenges around data management and governance. Furthermore, it is also challenged to integrate data from multiple systems and technologies. Although with these pains, companies are still pursuing digitalization because by embracing advanced technologies, companies can improve efficiency, quality, decision-making, and customer experience while also creating different business models and revenue streams. In this paper, the issue that data is stored in data silos with different schema and structures is focused. The conventional approaches to addressing this issue involve utilizing data warehousing, data integration tools, data standardization, and business intelligence tools. However, these approaches primarily focus on the grammar and structure of the data and neglect the importance of semantic modeling and semantic standardization, which are essential for achieving data interoperability. In this session, the challenge of data silos in Industry 4.0 is addressed by developing a semantic modeling approach compliant with Asset Administration Shell (AAS) models as an efficient standard for communication in Industry 4.0. The paper highlights how our approach can facilitate the data mapping process and semantic lifting according to existing industry standards such as ECLASS and other industrial dictionaries. It also incorporates the Asset Administration Shell technology to model and map the company’s data and utilize a knowledge graph for data storage and exploration.

Keywords: data interoperability in industry 4.0, digital integration, industrial dictionary, semantic modeling

Procedia PDF Downloads 94