Search results for: Atomic data

Authors: Javed Mohammed

Abstract:

Right now, bio-medical infrastructure lags well behind the curve. Our healthcare system is dispersed and disjointed; medical records are a bit of a mess; and we do not yet have the capacity to store and process the crazy amounts of data coming our way from widespread whole-genome sequencing. And then there are privacy issues. Despite these infrastructure challenges, some researchers are plunging into bio medical Big Data now, in hopes of extracting new and actionable knowledge. They are doing delving into molecular-level data to discover bio markers that help classify patients based on their response to existing treatments; and pushing their results out to physicians in novel and creative ways. Computer scientists and bio medical researchers are able to transform data into models and simulations that will enable scientists for the first time to gain a profound under-standing of the deepest biological functions. Solving biological problems may require High-Performance Computing HPC due either to the massive parallel computation required to solve a particular problem or to algorithmic complexity that may range from difficult to intractable. Many problems involve seemingly well-behaved polynomial time algorithms (such as all-to-all comparisons) but have massive computational requirements due to the large data sets that must be analyzed. High-throughput techniques for DNA sequencing and analysis of gene expression have led to exponential growth in the amount of publicly available genomic data. With the increased availability of genomic data traditional database approaches are no longer sufficient for rapidly performing life science queries involving the fusion of data types. Computing systems are now so powerful it is possible for researchers to consider modeling the folding of a protein or even the simulation of an entire human body. This research paper emphasizes the computational biology's growing need for high-performance computing and Big Data. It illustrates this article’s indispensability in meeting the scientific and engineering challenges of the twenty-first century, and how Protein Folding (the structure and function of proteins) and Phylogeny Reconstruction (evolutionary history of a group of genes) can use HPC that provides sufficient capability for evaluating or solving more limited but meaningful instances. This article also indicates solutions to optimization problems, and benefits Big Data and Computational Biology. The article illustrates the Current State-of-the-Art and Future-Generation Biology of HPC Computing with Big Data.

Keywords: high performance, big data, parallel computation, molecular data, computational biology

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Authors: A. S. V Polgampala, F. Huang

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This is an overview of what is entailed in an evaluation and issues to be aware of when class observation is being done. This study examined the effects of evaluating teaching practice of a 7-day ‘block teaching’ session in a pre -service science teacher training program at a reputed National College of Education in Sri Lanka. Effects were assessed in three areas: evaluation of the training process, evaluation of the training impact, and evaluation of the training procedure. Data for this study were collected by class observation of 18 teachers during 9th February to 16th of 2017. Prospective teachers of science teaching, the participants of the study were evaluated based on newly introduced format by the NIE. The data collected was analyzed qualitatively using the Miles and Huberman procedure for analyzing qualitative data: data reduction, data display and conclusion drawing/verification. It was observed that the trainees showed their confidence in teaching those competencies and skills. Teacher educators’ dissatisfaction has been a great impact on evaluation process.

Keywords: evaluation, perceptions & perspectives, pre-service, science teachering

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Authors: Sukhleen Kaur

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In security groundwork, Intrusion Detection System (IDS) has become an important component. The IDS has received increasing attention in recent years. IDS is one of the effective way to detect different kinds of attacks and malicious codes in a network and help us to secure the network. Data mining techniques can be implemented to IDS, which analyses the large amount of data and gives better results. Data mining can contribute to improving intrusion detection by adding a level of focus to anomaly detection. So far the study has been carried out on finding the attacks but this paper detects the malicious files. Some intruders do not attack directly, but they hide some harmful code inside the files or may corrupt those file and attack the system. These files are detected according to some defined parameters which will form two lists of files as normal files and harmful files. After that data mining will be performed. In this paper a hybrid classifier has been used via Naive Bayes and Ripper classification methods. The results show how the uploaded file in the database will be tested against the parameters and then it is characterised as either normal or harmful file and after that the mining is performed. Moreover, when a user tries to mine on harmful file it will generate an exception that mining cannot be made on corrupted or harmful files.

Keywords: data mining, association, classification, clustering, decision tree, intrusion detection system, misuse detection, anomaly detection, naive Bayes, ripper

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Authors: Abhijith Asok

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This paper presents a generalized approach for the simple linear data transformation, Y=bX, through an integration of multidimensional coordinate geometry, vector space theory and polygonal geometry. The scaling is performed by adding an additional ’Dummy Dimension’ to the n-dimensional data, which helps plot two dimensional component-wise straight lines on pairs of dimensions. The end result is a set of scaled extensions of observations in any of the 2n spatial divisions, where n is the total number of applicable dimensions/dataset variables, created by shifting the n-dimensional plane along the ’Dummy Axis’. The derived scaling factor was found to be dependent on the coordinates of the common point of origin for diverging straight lines and the plane of extension, chosen on and perpendicular to the ’Dummy Axis’, respectively. This result indicates the geometrical interpretation of a linear data transformation and hence, opportunities for a more informed choice of the factor ’b’, based on a better choice of these coordinate values. The paper follows on to identify the effect of this transformation on certain popular distance metrics, wherein for many, the distance metric retained the same scaling factor as that of the features.

Keywords: data transformation, dummy dimension, linear transformation, scaling

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Authors: Minna Pikkarainen, Yueqiang Xu

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The integration of digital technology with existing healthcare processes has been painfully slow, a huge gap exists between the fields of strictly regulated official medical care and the quickly moving field of health and wellness technology. We claim that the promises of preventive healthcare can only be fulfilled when this gap is closed – health care and self-care becomes seamless continuum “correct information, in the correct hands, at the correct time allowing individuals and professionals to make better decisions” what we call connected health approach. Currently, the issues related to security, privacy, consumer consent and data sharing are hindering the implementation of this new paradigm of healthcare. This could be solved by following MyData principles stating that: Individuals should have the right and practical means to manage their data and privacy. MyData infrastructure enables decentralized management of personal data, improves interoperability, makes it easier for companies to comply with tightening data protection regulations, and allows individuals to change service providers without proprietary data lock-ins. This paper tackles today’s unprecedented challenges of enabling and stimulating multiple healthcare data providers and stakeholders to have more active participation in the digital health ecosystem. First, the paper systematically proposes the MyData approach for healthcare and preventive health data ecosystem. In this research, the work is targeted for health and wellness ecosystems. Each ecosystem consists of key actors, such as 1) individual (citizen or professional controlling/using the services) i.e. data subject, 2) services providing personal data (e.g. startups providing data collection apps or data collection devices), 3) health and wellness services utilizing aforementioned data and 4) services authorizing the access to this data under individual’s provided explicit consent. Second, the research extends the existing four archetypes of orchestrator-driven healthcare data business models for the healthcare industry and proposes the fifth type of healthcare data model, the MyData Blockchain Platform. This new architecture is developed by the Action Design Research approach, which is a prominent research methodology in the information system domain. The key novelty of the paper is to expand the health data value chain architecture and design from centralization and pseudo-decentralization to full decentralization, enabled by blockchain, thus the MyData blockchain platform. The study not only broadens the healthcare informatics literature but also contributes to the theoretical development of digital healthcare and blockchain research domains with a systemic approach.

Keywords: blockchain, health data, platform, action design

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Authors: Janet C. Read

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UClan set collaboration with Lingokids to assess the Lingokids learning app's impact on learning outcomes in classrooms in the UK for children with ages ranging from 3 to 5 years. Data gathered during the controlled study with 69 children includes attitudinal data, engagement, and learning scores. Data shows that children enjoyment while learning was higher among those children using the game-based app compared to those children using other traditional methods. It’s worth pointing out that engagement when using the learning app was significantly higher than other traditional methods among older children. According to existing literature, there is a direct correlation between engagement, motivation, and learning. Therefore, this study provides relevant data points to conclude that Lingokids learning app serves its purpose of encouraging learning through playful and interactive content. That being said, we believe that learning outcomes should be assessed with a wider range of methods in further studies. Likewise, it would be beneficial to assess the level of usability and playability of the app in order to evaluate the learning app from other angles.

Keywords: learning app, learning outcomes, rapid test activity, Smileyometer, early childhood education, innovative pedagogy

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Authors: Jatin Kumar Choudhary, Naren Rayala, Abbas Eslami Kiasari, Fahimeh Jafari

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The Great Britain has one of the safest road networks in the world. However, the consequences of any death or serious injury are devastating for loved ones, as well as for those who help the severely injured. This paper aims to analyse the Great Britain's road safety situation and show the response measures for areas where the total damage caused by accidents can be significantly and quickly reduced. In this paper, we do an exploratory data analysis using STATS19 data. For the past 30 years, the UK has had a good record in reducing fatalities. The UK ranked third based on the number of road deaths per million inhabitants. There were around 165,000 accidents reported in the Great Britain in 2009 and it has been decreasing every year until 2019 which is under 120,000. The government continues to scale back road deaths empowering responsible road users by identifying and prosecuting the parameters that make the roads less safe.

Keywords: road safety, data analysis, openstreetmap, feature expanding.

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Authors: Zyad Elkhadir, Khalid Chougdali, Mohammed Benattou

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Most of the existing intrusion detection systems works on quantitative network traffic data with many irrelevant and redundant features, which makes detection process more time’s consuming and inaccurate. A several feature extraction methods, such as linear discriminant analysis (LDA), have been proposed. However, LDA suffers from the small sample size (SSS) problem which occurs when the number of the training samples is small compared with the samples dimension. Hence, classical LDA cannot be applied directly for high dimensional data such as network traffic data. In this paper, we propose two solutions to solve SSS problem for LDA and apply them to a network IDS. The first method, reduce the original dimension data using principal component analysis (PCA) and then apply LDA. In the second solution, we propose to use the pseudo inverse to avoid singularity of within-class scatter matrix due to SSS problem. After that, the KNN algorithm is used for classification process. We have chosen two known datasets KDDcup99 and NSLKDD for testing the proposed approaches. Results showed that the classification accuracy of (PCA+LDA) method outperforms clearly the pseudo inverse LDA method when we have large training data.

Keywords: LDA, Pseudoinverse, PCA, IDS, NSL-KDD, KDDcup99

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Authors: Tetsuro Saeki, Yuichi Kato, Shoutarou Mizuno

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STRIM (Statistical Test Rule Induction Method) has been proposed as a method to effectively induct if-then rules from the decision table which is considered as a sample set obtained from the population of interest. Its usefulness has been confirmed by simulation experiments specifying rules in advance, and by comparison with conventional methods. However, scope for future development remains before STRIM can be applied to the analysis of real-world data sets. The first requirement is to determine the size of the dataset needed for inducting true rules, since finding statistically significant rules is the core of the method. The second is to examine the capacity of rule induction from datasets with contaminated attribute values created by missing data and noise, since real-world datasets usually contain such contaminated data. This paper examines the first problem theoretically, in connection with the rule length. The second problem is then examined in a simulation experiment, utilizing the critical size of dataset derived from the first step. The experimental results show that STRIM is highly robust in the analysis of datasets with contaminated attribute values, and hence is applicable to realworld data.

Keywords: rule induction, decision table, missing data, noise

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Authors: Staniek Halina

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The inappropriate trace elements supply such as iron(III) and chromium(III) may be risk factors of many metabolic disorders (e.g., anemia, diabetes, as well cause toxic effect). However, little is known about their mutual interactions and their impact on these disturbances. The effects of Cr(III) supplementation with a deficit or excess supply of Fe(III) in vivo conditions are not known yet. The objective of the study was to investigate the combined effect of different Fe(III) levels in the diet and simultaneous Cr(III) supplementation on the Ca distribution in organs in healthy rats. The assessment was based on a two-factor (2x3) experiment carried out on 54 female Wistar rats (Rattus norvegicus). The animals were randomly divided into 9 groups and for 6 weeks, they were fed semi-purified diets AIN-93 with three different Fe(III) levels in the diet as a factor A [control (C) 45 mg/kg (100% Recommended Daily Allowance for rodents), deficient (D) 5 mg/kg (10% RDA), and oversupply (H) 180 mg/kg (400% RDA)]. The second factor (B) was the simultaneous dietary supplementation with Cr(III) at doses of 1, 50 and 500 mg/kg of the diet. Iron(III) citrate was the source of Fe(III). The complex of Cr(III) with propionic acid, also called Cr₃ or chromium(III) propionate (CrProp), was used as a source of Cr(III) in the diet. The Ca content of analysed samples (liver, kidneys, spleen, heart, and femur) was determined with the Atomic Absorption Spectrometry (AAS) method. It was found that different dietary Fe(III) supply as well as Cr(III) supplementation independently and in combination influenced Ca metabolism in healthy rats. Regardless of the supplementation of Cr(III), the oversupply of Fe(III) (180 mg/kg) decreased the Ca content in the liver and kidneys, while it increased the Ca saturation of bone tissue. High Cr(III) doses lowered the hepatic Ca content. Moreover, it tended to decrease the Ca content in the kidneys and heart, but this effect was not statistically significant. The combined effect of the experimental factors on the Ca content in the liver and the femur was observed. With the increase in the Fe(III) content in the diet, there was a decrease in the Ca level in the liver and an increase in bone saturation, and the additional Cr(III) supplementation intensified those effects. The study proved that the different Fe(III) content in the diet, independently and in combination with Cr(III) supplementation, affected the Ca distribution in organisms of healthy rats.

Keywords: calcium, chromium(III), iron(III), rats, supplementation

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Authors: Delphine Vendryes, Dushyanth Sekhar, Baojia Tong, Matthew Theisen, Chester Curme

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Much of the data that inform the decisions of governments, corporations and individuals are harvested from unstructured documents. Data extraction is defined here as a process that turns non-machine-readable information into a machine-readable format that can be stored, for instance, in a database. In financial services, introducing more automation in data extraction pipelines is a major challenge. Information sought by financial data consumers is often buried within vast bodies of unstructured documents, which have historically required thorough manual extraction. Automated solutions provide faster access to non-machine-readable datasets, in a context where untimely information quickly becomes irrelevant. Data quality standards cannot be compromised, so automation requires high data integrity. This multifaceted task is broken down into smaller steps: ingestion, table parsing (detection and structure recognition), text analysis (entity detection and disambiguation), schema-based record extraction, user feedback incorporation. Selected intermediary steps are phrased as machine learning problems. Solutions leveraging cutting-edge approaches from the fields of computer vision (e.g. table detection) and natural language processing (e.g. entity detection and disambiguation) are proposed.

Keywords: computer vision, entity recognition, finance, information retrieval, machine learning, natural language processing

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Authors: Haitao Yang, Jianming Lv, Fei Xu, Xintong Wang, Yilin Huang, Lanting Xia, Xuewu Zhu

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Given a fixed fund, purchasing fewer hosts of higher capability or inversely more of lower capability is a must-be-made trade-off in practices for building a Hadoop big data platform. An exploratory study is presented for a Housing Big Data Platform project (HBDP), where typical big data computing is with SQL queries of aggregate, join, and space-time condition selections executed upon massive data from more than 10 million housing units. In HBDP, an empirical formula was introduced to predict the performance of host clusters potential for the intended typical big data computing, and it was shaped via a regression approach. With this empirical formula, it is easy to suggest an optimal cluster configuration. The investigation was based on a typical Hadoop computing ecosystem HDFS+Hive+Spark. A proper metric was raised to measure the performance of Hadoop clusters in HBDP, which was tested and compared with its predicted counterpart, on executing three kinds of typical SQL query tasks. Tests were conducted with respect to factors of CPU benchmark, memory size, virtual host division, and the number of element physical host in cluster. The research has been applied to practical cluster procurement for housing big data computing.

Keywords: Hadoop platform planning, optimal cluster scheme at fixed-fund, performance predicting formula, typical SQL query tasks

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Authors: Tesfaye Alamirew Dessie

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Our study focuses on developing a Model Predictive Controller (MPC) and evaluating it against a traditional PID for a pasteurization process. Utilizing system identification from the experimental data, the dynamics of the pasteurization process were calculated. Using best fit with data validation, residual, and stability analysis, the quality of several model architectures was evaluated. The validation data fit the auto-regressive with exogenous input (ARX322) model of the pasteurization process by roughly 80.37 percent. The ARX322 model structure was used to create MPC and PID control techniques. After comparing controller performance based on settling time, overshoot percentage, and stability analysis, it was found that MPC controllers outperform PID for those parameters.

Keywords: MPC, PID, ARX, pasteurization

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Authors: Rana Rimawi, Ayman Baklizi

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Skewed distributions are important models that are frequently used in applications. Generalized distributions form a class of skewed distributions and gain widespread use in applications because of their flexibility in data analysis. More specifically, the Generalized Logistic Distribution with its different types has received considerable attention recently. In this study, based on progressively type-II censored data, we will consider point estimation in type II Generalized Logistic Distribution (Type II GLD). We will develop several estimators for its unknown parameters, including maximum likelihood estimators (MLE), Bayes estimators and linear estimators (BLUE). The estimators will be compared using simulation based on the criteria of bias and Mean square error (MSE). An illustrative example of a real data set will be given.

Keywords: point estimation, type II generalized logistic distribution, progressive censoring, maximum likelihood estimation

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Authors: Saeedeh Imani Moqadam, Ilya Bobrov, Jérémy Epp, Nils Ellendt, Lutz Mädler

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High entropy alloys (HEAs), having adjustable properties and enhanced stability compared with intermetallic compounds, are solid solution alloys that contain more than five principal elements with almost equal atomic percentage. The concept of producing such alloys pave the way for developing advanced materials with unique properties. However, the synthesis of such alloys may require advanced processes with high cooling rates depending on which alloy elements are used. In this study, the micro spheres of different diameters of HEAs were generated via a drop-on-demand droplet generator and subsequently solidified during free-fall in an argon atmosphere. Such droplet generators can generate individual droplets with high reproducibility regarding droplet diameter, trajectory and cooling while avoiding any interparticle momentum or thermal coupling. Metallography as well as X-ray diffraction investigations for each diameter of the generated metallic droplets where then carried out to obtain information about the microstructural state. To calculate the cooling rate of the droplets, a droplet cooling model was developed and validated using model alloys such as CuSn%6 and AlCu%4.5 for which a correlation of secondary dendrite arm spacing (SDAS) and cooling rate is well-known. Droplets were generated from these alloys and their SDAS was determined using quantitative metallography. The cooling rate was then determined from the SDAS and used to validate the cooling rates obtained from the droplet cooling model. The application of that model on the HEA then leads to the cooling rate dependency and hence to the identification of process windows for the synthesis of these alloys. These process windows were then compared with cooling rates obtained in processes such as powder production, spray forming, selective laser melting and casting to predict if a synthesis is possible with these processes.

Keywords: cooling rate, drop-on-demand, high entropy alloys, microstructure, single droplet generation, X-ray Diffractometry

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Authors: Emanuele A. Solagna, Ricardo S, Tozetto, Roberto dos S. Rabello

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Nowadays, physical processes are becoming digitized by the evolution of communication, sensing and storage technologies which promote the development of smart cities. The evolution of this technology has generated multiple challenges related to the generation of big data and the active participation of electronic devices in society. Thus, devices can send information that is captured and processed over large areas, but there is no guarantee that all the obtained data amount will be effectively stored and correctly persisted. Because, depending on the technology which is used, there are parameters that has huge influence on the full delivery of information. This article aims to characterize the project, currently under development, of a platform that based on data science will perform a performance and effectiveness evaluation of an industrial network that implements LoRaWAN technology considering its main parameters configuration relating these parameters to the information loss.

Keywords: Internet of Things, LoRa, LoRaWAN, smart cities

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Authors: Hillary Onyeka Abugu, Valentine Chinakwugwo Ezea, Janefrances Ngozi Ihedioha, Nwachukwu Romanus Ekere

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The study assessed the spring water quality in Igbo-Etiti, Nigeria, for drinking and irrigation application using Physico-chemical parameters, water quality index, mineral and trace elements, pollution indices and risk assessment. Standard methods were used to determine the physicochemical properties of the spring water in rainy and dry seasons. Trace metals such as Pb, Cd, Zn and Cu were determined with atomic absorption spectrophotometer. The results showed that most of the physicochemical properties studied were within the guideline values set by Nigeria Standard for Drinking Water Quality (NSDWQ), WHO and US EPA for drinking water purposes. However, pH of all the spring water (4.27- 4.73; and 4.95- 5.73), lead (Pb) (0.01-1.08 mg/L) and cadmium (Cd) (0.01-0.15 mg/L) concentrations were above the guideline values in both seasons. This could be attributed to the lithography of the study area, which is the Nsukka formation. Leaching of lead and sulphides from the embedded coal deposits could have led to the increased lead levels and made the water acidic. Two-way ANOVA showed significant differences in most of the parameters studied in dry and rainy seasons. Pearson correlation analysis and cluster analysis showed strong significant positive and negative correlations in some of the parameters studied in both seasons. The water quality index showed that none of the spring water had excellent water status. However, one spring (Iyi Ase) had poor water status in dry season and is considered unsafe for drinking. Iyi Ase was also considered not suitable for irrigation application as predicted by most of the pollution indices, while others were generally considered suitable for irrigation application. Probable cancer and non-cancer risk assessment revealed a probable risk associated with the consumption of the spring in the Igbo-Ettiti area, Nigeria.

Keywords: water quality, pollution index, risk assessment, physico-chemical parameters

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Authors: Nicole Christiansen, Hanne Marie Motzfeldt

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In recent years, more and more HR professionals have been using cyber-vetting in job recruitment in an effort to find the perfect match for the company. These practices are growing rapidly, accessing a vast amount of data from social networks, some of which is privileged and protected information. Thus, there is a risk that the right to privacy is becoming a duty to manage your private data. This paper investigates to which degree a job applicant's fundamental rights are protected adequately in current and future legislation in the EU. This paper argues that current data protection regulations and forthcoming regulations on the use of AI ensure sufficient protection. However, even though the regulation on paper protects employees within the EU, the recruitment sector may not pay sufficient attention to the regulation as it not specifically targeting this area. Therefore, the lack of specific labor and employment regulation is a concern that the social partners should attend to.

Keywords: AI, cyber vetting, data protection, job recruitment, online privacy

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Authors: Rezky Rifaini, Raden Bagus Fajriya Hakim

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This research was conducted at the Bolo primary health Care in Bima Regency. The purpose of the research is to find out the association pattern that is formed of medical record database from Bolo Primary health care’s patient. The data used is secondary data from medical records database PHC. Sequential pattern mining technique is the method that used to analysis. Transaction data generated from Patient_ID, Check_Date and diagnosis. Sequential Pattern Discovery Algorithms Using Equivalent Classes (SPADE) is one of the algorithm in sequential pattern mining, this algorithm find frequent sequences of data transaction, using vertical database and sequence join process. Results of the SPADE algorithm is frequent sequences that then used to form a rule. It technique is used to find the association pattern between items combination. Based on association rules sequential analysis with SPADE algorithm for minimum support 0,03 and minimum confidence 0,75 is gotten 3 association sequential pattern based on the sequence of patient_ID, check_Date and diagnosis data in the Bolo PHC.

Keywords: diagnosis, primary health care, medical record, data mining, sequential pattern mining, SPADE algorithm

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Authors: Reda Abdel Azim, Tariq Shehab

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The main objective of this study is to develop a subsurface fracture map of naturally fractured reservoirs by overcoming the limitations associated with different data sources in characterising fracture properties. Some of these limitations are overcome by employing a nested neuro-stochastic technique to establish inter-relationship between different data, as conventional well logs, borehole images (FMI), core description, seismic attributes, and etc. and then characterise fracture properties in terms of fracture density and fractal dimension for each data source. Fracture density is an important property of a system of fracture network as it is a measure of the cumulative area of all the fractures in a unit volume of a fracture network system and Fractal dimension is also used to characterize self-similar objects such as fractures. At the wellbore locations, fracture density and fractal dimension can only be estimated for limited sections where FMI data are available. Therefore, artificial intelligence technique is applied to approximate the quantities at locations along the wellbore, where the hard data is not available. It should be noted that Artificial intelligence techniques have proven their effectiveness in this domain of applications.

Keywords: naturally fractured reservoirs, artificial intelligence, fracture intensity, fractal dimension

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Authors: Tim Nedyalkov

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A business decision to move to the cloud brings fundamental changes in how an organization develops and delivers its Information Technology solutions. The accelerated pace of digital transformation across businesses and government agencies increases the reliance on cloud-based services. They are collecting, managing, and retaining large amounts of data in cloud environments makes information security and data privacy protection essential. It becomes even more important to understand what key factors drive successful cloud adoption following the commencement of the Privacy Amendment Notifiable Data Breaches (NDB) Act 2017 in Australia as the regulatory changes impact many organizations and industries. This quantitative correlational research investigated the governance, risk management, and compliance factors contributing to cloud security success. The factors influence the adoption of cloud computing within an organizational context after the commencement of the NDB scheme. The results and findings demonstrated that corporate information security policies, data storage location, management understanding of data governance responsibilities, and regular compliance assessments are the factors influencing cloud computing adoption. The research has implications for organizations, future researchers, practitioners, policymakers, and cloud computing providers to meet the rapidly changing regulatory and compliance requirements.

Keywords: cloud compliance, cloud security, data governance, privacy protection

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Authors: Amirhossein Moghanian, Morteza Elsa, Mehrnaz Aminitabar

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Modified bioactive glass has been considered as a promising multifunctional candidate in bone repair and regeneration due to its attractive properties. The present study mainly aims to evaluate how the individual substitution of lithium (L-BG) and magnesium (M-BG) for calcium can affect the in vitro bioactivity of sol-gel derived substituted 58S bioactive glass (BG); and to present one composition in both of the 60SiO2–(36-x)CaO–4P₂O₅–(x)Li₂O and 60SiO₂ –(36-x)CaO–4P₂O₅–(x)MgO quaternary systems (where x= 0, 5, 10 mol.%) with improved biocompatibility, enhanced alkaline phosphatase (ALP) activity, and the most efficient antibacterial activity against methicillin-resistant staphylococcus aureus bacteria. To address these aims, and study the effect of CaO/Li₂O and CaO/MgO substitution up to 10 mol % in 58S-BGs, the samples were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, inductively coupled plasma atomic emission spectrometry and scanning electron microscopy after immersion in simulated body fluid up to 14 days. Results indicated that substitution of either CaO/ Li₂O and CaO/ MgO had a retarding effect on in vitro hydroxyapatite (HA) formation due to the lower supersaturation degree for nucleation of HA compared with 58s-BG. Meanwhile, magnesium had a more pronounced effect. The 3-(4,5dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and alkaline phosphatase (ALP) assays showed that both substitutions of CaO/ Li₂O and CaO/ MgO up to 5mol % in 58s-BGs led to increased biocompatibility and stimulated proliferation of the pre-osteoblast MC3T3 cells with respect to the control. On the other hand, the substitution of either Li or Mg for Ca in the 58s BG composition resulted in improved bactericidal efficiency against MRSA bacteria. Taken together, sample 58s-BG with 5 mol % CaO/Li₂O substitution (BG-5L) was considered as a multifunctional biomaterial in bone repair/regeneration with improved biocompatibility, enhanced ALP activity as well as significant antibacterial activity against methicillin-resistant staphylococcus aureus (MRSA) bacteria.

Keywords: alkaline, alkaline earth, bioactivity, biomedical applications, sol-gel processes

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Authors: Muhammad Minhaj Khan, Jaemin Lee, Suhong Lee, Jinyoung Chung, Johoo Whang

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The Republic of Korea has been facing strong storage crisis from nuclear waste generation as At Reactor (AR) temporary storage sites are about to reach saturation. Since the country is densely populated with a rate of 491.78 persons per square kilometer, Construction of High-level waste repository will not be a feasible option. In order to tackle the storage waste generation problem which is increasing at a rate of 350 tHM/Yr. and 380 tHM/Yr. in case of 20 PWRs and 4 PHWRs respectively, the study strongly focuses on the advancement of current nuclear power plants to GEN-IV sustainable and ecological nuclear systems by burning TRUs (Pu, MAs). First, Calculations has made to estimate the generation of SNF including Pu and MA from PWR and PHWR NPPS by using the IAEA code Nuclear Fuel Cycle Simulation System (NFCSS) for the period of 2016, 2030 (including the saturation period of each site from 2024~2028), 2089 and 2109 as the number of NPPS will increase due to high import cost of non-nuclear energy sources. 2ndly, in order to produce environmentally sustainable nuclear energy systems, 4 scenarios to burnout the Plutonium and MAs are analyzed with the concentration on burning of MA only, MA and Pu together by utilizing SFR, LFR and KALIMER-600 burner reactor after recycling the spent oxide fuel from PWR through pyro processing technology developed by Korea Atomic Energy Research Institute (KAERI) which shows promising and sustainable future benefits by minimizing the HLW generation with regard to waste amount, decay heat, and activity. Finally, With the concentration on front and back end fuel cycles for open and closed fuel cycles of PWR and Pyro-SFR respectively, an overall assessment has been made which evaluates the quantitative as well as economical combativeness of SFR metallic fuel against PWR once through nuclear fuel cycle.

Keywords: GEN IV nuclear fuel cycle, nuclear waste, waste sustainability, transmutation

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Authors: Muhammad Waheed Mushtaq, Shahid bashir, Atta Ur Rehman

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In the past decade, the growing demand for capital and the increased utilization of supercapacitors reflect advancements in energy-producing systems and energy storage devices. Metal oxides and ferrites have emerged as promising candidates for supercapacitors and batteries. In our current study, we synthesized Lithium manganese nanoferrite, denoted as LixMnxFe₂O₄, using the hydrothermal technique. Subsequently, we treated it with sodium dodecyl benzene sulphonate (SDBS) surfactant to create nanocomposites of Lithium manganese nano ferrite (LMFe) with poly pyrrole (LixMnxFe₂O₄-PPY). We employed Powder X-ray diffraction (XRD) to confirm the crystalline nature and spinel phase structure of LMFe nanoparticles, which exhibited a single-phase crystal structure, indicating sample purity. To assess the surface topography, morphology, and grain size of both synthesized LixMnxFe₂O₄ and LixMnxFe₂O₄-PPY, we used atomic force microscopy and scanning electron microscopy (SEM). The average particle size of pure ferrite was found to be 54 nm, while that of its nanocomposite was 71 nm. Energy dispersive X-ray (EDX) analysis confirmed the presence of all required elements, including Li, Mn, Fe, and O, in the appropriate proportions. Saturation magnetization (32.69 emu), remanence (Mr), and coercive force (Hc) were measured using a Vibrating Sample Magnetometer (VSM). To assess the electrochemical performance of the material, we conducted Cyclic Voltammetry (CV) measurements for both pure LMFe and LMFe-PPY. The CV results for LMFe-PPY demonstrated that specific capacitance decreased with increasing scan rate while the area of the current-voltage loop increased. These findings are promising for the development of supercapacitors and lithium-ion batteries (LIBs).

Keywords: lithium manganese ferrite, poly pyrrole, nanocomposites, cyclic voltammetry, cathode

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Authors: U. Ali Rahoma, Nabil Esawy, Fawzia Ibrahim Moursy, A. H. Hassan, Samy A. Khalil, Ashraf S. Khamees

Abstract:

The design of any solar energy conversion system requires the knowledge of solar radiation data obtained over a long period. Satellite data has been widely used to estimate solar energy where no ground observation of solar radiation is available, yet there are limitations on the temporal coverage of satellite data. Reanalysis is a “retrospective analysis” of the atmosphere parameters generated by assimilating observation data from various sources, including ground observation, satellites, ships, and aircraft observation with the output of NWP (Numerical Weather Prediction) models, to develop an exhaustive record of weather and climate parameters. The evaluation of the performance of reanalysis datasets (ERA-5) for North Africa against high-quality surface measured data was performed using statistical analysis. The estimation of global solar radiation (GSR) distribution over six different selected locations in North Africa during ten years from the period time 2011 to 2020. The root means square error (RMSE), mean bias error (MBE) and mean absolute error (MAE) of reanalysis data of solar radiation range from 0.079 to 0.222, 0.0145 to 0.198, and 0.055 to 0.178, respectively. The seasonal statistical analysis was performed to study seasonal variation of performance of datasets, which reveals the significant variation of errors in different seasons—the performance of the dataset changes by changing the temporal resolution of the data used for comparison. The monthly mean values of data show better performance, but the accuracy of data is compromised. The solar radiation data of ERA-5 is used for preliminary solar resource assessment and power estimation. The correlation coefficient (R2) varies from 0.93 to 99% for the different selected sites in North Africa in the present research. The goal of this research is to give a good representation for global solar radiation to help in solar energy application in all fields, and this can be done by using gridded data from European Centre for Medium-Range Weather Forecasts ECMWF and producing a new model to give a good result.

Keywords: solar energy, solar radiation, ERA-5, potential energy

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Authors: Fan Gao, Lior Pachter

Abstract:

The primary tool currently used to pre-process 10X Chromium single-cell ATAC-seq data is Cell Ranger, which can take very long to run on standard datasets. To facilitate rapid pre-processing that enables reproducible workflows, we present a suite of tools called scATAK for pre-processing single-cell ATAC-seq data that is 15 to 18 times faster than Cell Ranger on mouse and human samples. Our tool can also calculate chromatin interaction potential matrices, and generate open chromatin signal and interaction traces for cell groups. We use scATAK tool to explore the chromatin regulatory landscape of a healthy adult human brain and unveil cell-type specific features, and show that it provides a convenient and computational efficient approach for pre-processing single-cell ATAC-seq data.

Keywords: single-cell, ATAC-seq, bioinformatics, open chromatin landscape, chromatin interactome

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Authors: Selim M. Khan, Aaron A. Goodarzi, Joshua M. Taron, Tryggve Rönnqvist

Abstract:

Indoor air quality, a prime determinant of health, is strongly influenced by the presence of hazardous radon gas within the built environment. As a health issue, dangerously high indoor radon arose within the 20th century to become the 2nd leading cause of lung cancer. While the 21st century building metrics and human behaviors have captured, contained, and concentrated radon to yet higher and more hazardous levels, the issue is rapidly worsening in Canada. It is established that Canadians in the Prairies are the 2nd highest radon-exposed population in the world, with 1 in 6 residences experiencing 0.2-6.5 millisieverts (mSv) radiation per week, whereas the Canadian Nuclear Safety Commission sets maximum 5-year occupational limits for atomic workplace exposure at only 20 mSv. This situation is also deteriorating over time within newer housing stocks containing higher levels of radon. Deep machine learning (LSTM) algorithms were applied to analyze multiple quantitative and qualitative features, determine the most important contributory factors, and predicted radon levels in the known past (1990-2020) and projected future (2021-2050). The findings showed gradual downwards patterns in Sweden, whereas it would continue to go from high to higher levels in Canada over time. The contributory factors found to be the basement porosity, roof insulation depthness, R-factor, and air dynamics of the indoor environment related to human window opening behaviour. Building codes must consider including these factors to ensure adequate indoor ventilation and healthy living that can prevent lung cancer in non-smokers.

Keywords: radon, building metrics, deep learning, LSTM prediction model, lung cancer, canada, sweden

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Authors: Jiangbo Shi, Zeyu Gao, Chen Li

Abstract:

Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks by using a small amount of clean meta-data. Then the corrected masks are used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. In particular, in some noise scenarios, it even exceeds the performance of training on supervised data.

Keywords: deep learning, histopathological image, meta-learning, nuclei segmentation, weak annotations

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

Abstract:

Manufacturing companies are facing global competition and enormous cost pressure. The use of machine learning applications can help reduce production costs and create added value. Predictive quality enables the securing of product quality through data-supported predictions using machine learning models as a basis for decisions on test results. Furthermore, machine learning methods are able to process large amounts of data, deal with unfavourable row-column ratios and detect dependencies between the covariates and the given target as well as assess the multidimensional influence of all input variables on the target. Real production data are often subject to highly fluctuating boundary conditions and unbalanced data sets. Changes in production data manifest themselves in trends, systematic shifts, and seasonal effects. Thus, Machine learning applications require intensive pre-processing and feature selection. Data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets. Within the used real data set of Bosch hydraulic valves, the comparability of the same production conditions in the production of hydraulic valves within certain time periods can be identified by applying the concept drift method. Furthermore, a classification model is developed to evaluate the feature importance in different subsets within the identified time periods. By selecting comparable and stable features, the number of features used can be significantly reduced without a strong decrease in predictive power. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. In this research, the ada boosting classifier is used to predict the leakage of hydraulic valves based on geometric gauge blocks from machining, mating data from the assembly, and hydraulic measurement data from end-of-line testing. In addition, the most suitable methods are selected and accurate quality predictions are achieved.

Keywords: classification, achine learning, predictive quality, feature selection

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Authors: Kenneth Harper

Abstract:

The secure sharing of Electronic Health Records (EHRs) is a critical challenge in modern healthcare, demanding solutions to enhance interoperability, privacy, and data integrity. Traditional standards like Health Information Exchange (HIE) and HL7 have made significant strides in facilitating data exchange between healthcare entities. However, these approaches rely on centralized architectures that are often vulnerable to data breaches, lack sufficient privacy measures, and have scalability issues. This paper proposes a framework for secure, decentralized sharing of EHRs using blockchain technology, cryptographic tokens, and Non-Fungible Tokens (NFTs). The blockchain's immutable ledger, decentralized control, and inherent security mechanisms are leveraged to improve transparency, accountability, and auditability in healthcare data exchanges. Furthermore, we introduce the concept of tokenizing patient data through NFTs, creating unique digital identifiers for each record, which allows for granular data access controls and proof of data ownership. These NFTs can also be employed to grant access to authorized parties, establishing a secure and transparent data sharing model that empowers both healthcare providers and patients. The proposed approach addresses common privacy concerns by employing privacy-preserving techniques such as zero-knowledge proofs (ZKPs) and homomorphic encryption to ensure that sensitive patient information can be shared without exposing the actual content of the data. This ensures compliance with regulations like HIPAA and GDPR. Additionally, the integration of Fast Healthcare Interoperability Resources (FHIR) with blockchain technology allows for enhanced interoperability, enabling healthcare organizations to exchange data seamlessly and securely across various systems while maintaining data governance and regulatory compliance. Through real-world case studies and simulations, this paper demonstrates how blockchain-based EHR sharing can reduce operational costs, improve patient outcomes, and enhance the security and privacy of healthcare data. This decentralized framework holds great potential for revolutionizing healthcare information exchange, providing a transparent, scalable, and secure method for managing patient data in a highly regulated environment.

Keywords: blockchain, electronic health records (ehrs), fast healthcare interoperability resources (fhir), health information exchange (hie), hl7, interoperability, non-fungible tokens (nfts), privacy-preserving techniques, tokens, secure data sharing,

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