Search results for: data databases

Authors: Rokhmatul Insani, Hira Laksmiwati Soemitro

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Business Intelligence is a methodology that exploits the data to produce information and knowledge systematically, business intelligence can support the decision-making process. Some methods in business intelligence are data warehouse and data mining. A data warehouse can store historical data from transactional data. For data modelling in data warehouse, we apply dimensional modelling by Kimball. While data mining is used to extracting patterns from the data and get insight from the data. Data mining has many techniques, one of which is segmentation. For profiling of telecommunication customer, we use customer segmentation according to customer’s usage of services, customer invoice and customer payment. Customers can be grouped according to their characteristics and can be identified the profitable customers. We apply K-Means Clustering Algorithm for segmentation. The input variable for that algorithm we use RFM (Recency, Frequency and Monetary) model. All process in data mining, we use tools IBM SPSS modeller.

Keywords: business intelligence, customer segmentation, data warehouse, data mining

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Authors: Malihe Aliasgari, Yousef Nejatbakhsh

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The era of Big Data and the immensity of real-life datasets compels computation tasks to be performed in a distributed fashion, where the data is dispersed among many servers that operate in parallel. However, massive parallelization leads to computational bottlenecks due to faulty servers and stragglers. Stragglers refer to a few slow or delay-prone processors that can bottleneck the entire computation because one has to wait for all the parallel nodes to finish. The problem of straggling processors, has been well studied in the context of distributed computing. Recently, it has been pointed out that, for the important case of linear functions, it is possible to improve over repetition strategies in terms of the tradeoff between performance and latency by carrying out linear precoding of the data prior to processing. The key idea is that, by employing suitable linear codes operating over fractions of the original data, a function may be completed as soon as enough number of processors, depending on the minimum distance of the code, have completed their operations. The problem of matrix-matrix multiplication in the presence of practically big sized of data sets faced with computational and memory related difficulties, which makes such operations are carried out using distributed computing platforms. In this work, we study the problem of distributed matrix-matrix multiplication W = XY under storage constraints, i.e., when each server is allowed to store a fixed fraction of each of the matrices X and Y, which is a fundamental building of many science and engineering fields such as machine learning, image and signal processing, wireless communication, optimization. Non-secure and secure matrix multiplication are studied. We want to study the setup, in which the identity of the matrix of interest should be kept private from the workers and then obtain the recovery threshold of the colluding model, that is, the number of workers that need to complete their task before the master server can recover the product W. The problem of secure and private distributed matrix multiplication W = XY which the matrix X is confidential, while matrix Y is selected in a private manner from a library of public matrices. We present the best currently known trade-off between communication load and recovery threshold. On the other words, we design an achievable PSGPD scheme for any arbitrary privacy level by trivially concatenating a robust PIR scheme for arbitrary colluding workers and private databases and the proposed SGPD code that provides a smaller computational complexity at the workers.

Keywords: coded distributed computation, private information retrieval, secret sharing, stragglers

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Authors: Younies Saeed Hassan Mahmoud, Mai Mabrouk, Elsayed Sallam

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Analysing DNA microarray data sets is a great challenge, which faces the bioinformaticians due to the complication of using statistical and machine learning techniques. The challenge will be doubled if the microarray data sets contain missing data, which happens regularly because these techniques cannot deal with missing data. One of the most important data analysis process on the microarray data set is feature selection. This process finds the most important genes that affect certain disease. In this paper, we introduce a technique for imputing the missing data in microarray data sets while performing feature selection.

Keywords: DNA microarray, feature selection, missing data, bioinformatics

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Authors: Lutful Karim, Mohammed S. Al-kahtani

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Sensors are being used in various applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control and hence, play the vital role in the growth of big data. However, sensors collect redundant data. Thus, aggregating and filtering sensors data are significantly important to design an efficient big data framework. Current researches do not focus on aggregating and filtering data at multiple layers of sensor-based big data framework. Thus, this paper introduces (i) three layers data aggregation and framework for big data and (ii) a priority-based, dynamic data aggregation scheme (PDDA) for the lowest layer at sensors. Simulation results show that the PDDA outperforms existing tree and cluster-based data aggregation scheme in terms of overall network energy consumptions and end-to-end data transmission delay.

Keywords: big data, clustering, tree topology, data aggregation, sensor networks

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Authors: Amel M. A. Osman, Roy Mahony, Lisa Dann, McKenna S.

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Background: Computed Tomography (CT) is a significant source of radiation in the pediatric population. A new head injury (HI) pathway was introduced in 2021, which altered the previous process of HI being jointly admitted with general pediatrics and surgery to admit these patients under the Emergency Medicine Team. Admitted patients included those with positive CT findings not requiring immediate neurosurgical intervention and those who did not meet current criteria for urgent CT brain as per NICE guidelines but were still symptomatic for prolonged observations. This approach aims to decrease the number of CT scans performed. The main aim is to assess the variation in CT scanning rates since the change in the admitting process. A retrospective review of patients presenting to CHI PECU with HI over 6-month period (01/01/19-31/05/19) compared to a 6-month period post introduction of the new pathway (01/06/2022-31/12/2022). Data was collected from the electronic record databases, symphony, and PACS. Results: In 2019, there were 869 presentations of HI, among which 32 (3.68%) had CT scans performed. 2 (6.25%) of those scanned had positive findings. In 2022, there were 1122 HI presentations, with 47 (4.19%) CT scans performed and positive findings in 5 (10.6%) cases. 57 patients were admitted under the new pathway for observation, with 1 having a CT scan following admission. Conclusion: Quantitative lifetime radiation risks for children are not negligible. While there was no statistically significant reduction in CTs performed amongst HIs presenting to our department, a significant group met the criteria for admission under the PECU consultant for prolonged monitoring. There was also a greater proportion of abnormalities on CT scans performed in 2022, demonstrating improved patient selection for imaging. Further data analysis is ongoing to determine if those who were admitted would have previously been scanned under the old pathway.

Keywords: head injury, CT, admission, guidline

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Authors: Merkineh Markos Lorato, Gedefaw Diress Alene

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Introduction: Visual impairment is any condition of the eye or visual system that results in loss/reduction of visual functioning. It significantly influences the academic routine and social activities of children, and the effect is severe for low-income countries like Ethiopia. So, this study aimed to determine the pooled prevalence of visual impairment among school children in Ethiopia. Methods: Databases such as Medical Literature Analysis and Retrieval System Online, Excerpta Medica dataBASE, World Wide Web of Science, and Cochrane Library searched to retrieve eligible articles. In addition, Google Scholar and a reference list of the retrieved eligible articles were addressed. Studies that reported the prevalence of visual impairment were included to estimate the pooled prevalence. Data were extracted using a standardized data extraction format prepared in Microsoft Excel and analysis was held using STATA 11 statistical software. I² was used to assess the heterogeneity. Because of considerable heterogeneity, a random effect meta-analysis model was used to estimate the pooled prevalence of visual impairment among school children in Ethiopia. Results: The result of 9 eligible studies showed that the pooled prevalence of visual impairment among school children in Ethiopia was 7.01% (95% CI: 5.46, 8.56%). In the subgroup analysis, the highest prevalence was reported in South Nations Nationalities and Tigray region together (7.99%; 3.63, 12.35), while the lowest prevalence was reported in Addis Ababa (5.73%; 3.93, 7.53). Conclusion: The prevalence of visual impairment among school children is significantly high in Ethiopia. If it is not detected and intervened early, it will cause a lifetime threat to visually impaired school children, so that school vision screening program plan and its implementation may cure the life quality of future generations in Ethiopia.

Keywords: visual impairment, school children, Ethiopia, prevalence

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Authors: Stjepka Popović, Lucija Vejmelka

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Background: Cyberbullying has become a prevalent issue worldwide, including in Croatia. However, a comprehensive understanding of the extent and nature of cyberbullying in the Croatian context is lacking. Objective: The objective of this systematic review is to evaluate the quality of current research conducted in Croatia on the subject of cyberbullying, identify any gaps in the research, and provide suggestions for future investigations. It examines the prevalence gender and age differences of cyberbullying in Croatia. Participants and Setting: Research is done on secondary data resources (published studies) of cyberbullying in Croatia. The participants in these studies that were included in systematic review are children and youth of all ages residing in Croatia who have been involved in cyberbullying incidents. The setting includes various environments where cyberbullying may occur, such as social media platforms and educational institutions. Methods: To identify pertinent studies on cyberbullying in Croatia, a comprehensive exploration of both international and domestic electronic databases was systematically undertaken. Relevant studies were chosen according to predefined criteria that determined inclusion and exclusion. Key findings from the selected studies were extracted and synthesized, enabling the identification of patterns in the data. Results: A total of 43 studies that fulfilled the inclusion criteria were identified in the review. The prevalence of cyberbullying victimization in Croatia ranged from 7% - 55.3%, with adolescents being the most affected group. The prevalence of cyberbullying perpetration was ranging from 3.2% - 30.3%. The most prevalent form of cyberbullying included gossiping and mocking others. Gender and age differences are highlighted. Conclusions: The outcomes of this systematic review highlight the pressing need for targeted interventions and preventative measures to address cyberbullying in Croatia. Additionally, it is crucial to conduct further research to investigate the long-term impacts and potential factors that can help mitigate cyberbullying in the context of Croatia.

Keywords: cyberbullying, online risky behavior, Croatia, systematic review

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Authors: Janos Juhasz, Sandor Pongor, Balazs Ligeti

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Next-generation sequencing, especially whole genome shotgun sequencing, is becoming a common approach to gain insight into the microbiomes in a culture-independent way, even in clinical practice. It does not only give us information about the species composition of an environmental sample but opens the possibility to detect antimicrobial resistance and novel, or currently unknown, pathogens. Accurately and reliably detecting the microbial strains is a challenging task. Here we present a sensitive approach for detecting pathogens in metagenomics samples with special regard to detecting novel variants of known pathogens. We have developed a pipeline that uses fast, short read aligner programs (i.e., Bowtie2/BWA) and comprehensive nucleotide databases. Taxonomic binning is based on the lowest common ancestor (LCA) principle; each read is assigned to a taxon, covering the most significantly hit taxa. This approach helps in balancing between sensitivity and running time. The program was tested both on experimental and synthetic data. The results implicate that our method performs as good as the state-of-the-art BLAST-based ones, furthermore, in some cases, it even proves to be better, while running two orders magnitude faster. It is sensitive and capable of identifying taxa being present only in small abundance. Moreover, it needs two orders of magnitude less reads to complete the identification than MetaPhLan2 does. We analyzed an experimental anthrax dataset (B. anthracis strain BA104). The majority of the reads (96.50%) was classified as Bacillus anthracis, a small portion, 1.2%, was classified as other species from the Bacillus genus. We demonstrate that the evaluation of high-throughput sequencing data is feasible in a reasonable time with good classification accuracy.

Keywords: metagenomics, taxonomy binning, pathogens, microbiome, B. anthracis

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Authors: Javkhlan Nyamjav, Simona Fischer, Lauren Garner, Veronica McCracken

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Current whole building life cycle assessments (LCA) primarily focus on structural and major architectural elements to measure building embodied carbon. Most of the interior finishes and fixtures are available on digital tools (such as Tally); however, furniture is still left unaccounted for. Due to its repeated refreshments and its complexity, furniture embodied carbon can accumulate over time, becoming comparable to structure and envelope numbers. This paper presents a method to calculate the Global Warming Potential (GWP) of furniture elements in commercial buildings. The calculator uses the quantity takeoff method with GWP averages gathered from environmental product declarations (EPD). The data was collected from EPD databases and furniture manufacturers from North America to Europe. A total of 48 GWP numbers were collected, with 16 GWP coming from alternative EPD. The finalized calculator shows the average GWP of typical commercial furniture and helps the decision-making process to reduce embodied carbon. The calculator was tested on MSR Design projects and showed furniture can account for more than half of the interior embodied carbon. The calculator highlights the importance of adding furniture to the overall conversation. However, the data collection process showed a) acquiring furniture EPD is not straightforward as other building materials; b) there are very limited furniture EPD, which can be explained from many perspectives, including the EPD price; c) the EPD themselves vary in terms of units, LCA scopes, and timeframes, which makes it hard to compare the products. Even though there are current limitations, the emerging focus on interior embodied carbon will create more demand for furniture EPD. It will allow manufacturers to represent all their efforts on reducing embodied carbon. In addition, the study concludes with recommendations on how designers can reduce furniture-embodied carbon through reuse and closed-loop systems.

Keywords: furniture, embodied carbon, calculator, tenant improvement, interior design

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Authors: Adel Khorramrouz, Monireh Ahmadi Bani, Ehsan Norouzi

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Background:-Due to the essential role of the foot in movement, foot disorders (FDs) have significant impacts on activity and quality of life. Many studies confirmed the association between FDs and demographic characteristics. On the other hand, recent advances in data collection and statistical analysis led to an increase in the volume of databases. Analysis of patient’s data through the decision tree can be used to explore the relationship between demographic characteristics and FDs. Significance of the study: This study aimed to investigate the relationship between demographic characteristics with common FDs. The second purpose is to better inform foot intervention, we classify FDs based on demographic variables. Methodologies: We analyzed 2323 subjects with pes-planus (PP), pes-cavus (PC), hallux-valgus (HV) and plantar-fasciitis (PF) who were referred to a foot therapy clinic between 2015 and 2021. Subjects had to fulfill the following inclusion criteria: (1) weight between 14 to 150 kilogram, (2) height between 30 to 220, (3) age between 3 to 100 years old, and (4) BMI between 12 to 35. Medical archives of 2323 subjects were recorded retrospectively and all the subjects examined by an experienced physician. Age and BMI were classified into five and four groups, respectively. 80% of the data were randomly selected as training data and 20% tested. We build a decision tree model to classify FDs using demographic characteristics. Findings: Results demonstrated 981 subjects from 2323 (41.9%) of people who were referred to the clinic with FDs were diagnosed as PP, 657 (28.2%) PC, 628 (27%) HV and 213 (9%) identified with PF. The results revealed that the prevalence of PP decreased in people over 18 years of age and in children over 7 years. In adults, the prevalence depends first on BMI and then on gender. About 10% of adults and 81% of children with low BMI have PP. There is no relationship between gender and PP. PC is more dependent on age and gender. In children under 7 years, the prevalence was twice in girls (10%) than boys (5%) and in adults over 18 years slightly higher in men (62% vs 57%). HV increased with age in women and decreased in men. Aging and obesity have increased the prevalence of PF. We conclude that the accuracy of our approach is sufficient for most research applications in FDs. Conclusion:-The increased prevalence of PP in children is probably due to the formation of the arch of the foot at this age. Increasing BMI by applying high pressure on the foot can increase the prevalence of this disorder in the foot. In PC, the Increasing prevalence of PC from women to men with age may be due to genetics and innate susceptibility of men to this disorder. HV is more common in adult women, which may be due to environmental reasons such as shoes, and the prevalence of PF in obese adult women may also be due to higher foot pressure and housekeeping activities.

Keywords: decision tree, demographic characteristics, foot disorders, machine learning

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Authors: Nguyen J. M., Lucas G., Ruan S., Digonnet H., Antonioli D.

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Introduction: Deep Learning (DL) is a very powerful tool for analyzing image data. But for tabular data, it cannot compete with machine learning methods like XGBoost. The research question becomes: can tabular data be transformed into images that can be analyzed by simple CNNs (Convolutional Neuron Networks)? Will DL be the absolute tool for data classification? All current solutions consist in repositioning the variables in a 2x2 matrix using their correlation proximity. In doing so, it obtains an image whose pixels are the variables. We implement a technology, DeepNIC, that offers the possibility of obtaining an image for each variable, which can be analyzed by simple CNNs. Material and method: The 'ROP' (Regression OPtimized) model is a binary and atypical decision tree whose nodes are managed by a new artificial neuron, the Neurop. By positioning an artificial neuron in each node of the decision trees, it is possible to make an adjustment on a theoretically infinite number of variables at each node. From this new decision tree whose nodes are artificial neurons, we created the concept of a 'Random Forest of Perfect Trees' (RFPT), which disobeys Breiman's concepts by assembling very large numbers of small trees with no classification errors. From the results of the RFPT, we developed a family of 10 statistical information criteria, Nguyen Information Criterion (NICs), which evaluates in 3 dimensions the predictive quality of a variable: Performance, Complexity and Multiplicity of solution. A NIC is a probability that can be transformed into a grey level. The value of a NIC depends essentially on 2 super parameters used in Neurops. By varying these 2 super parameters, we obtain a 2x2 matrix of probabilities for each NIC. We can combine these 10 NICs with the functions AND, OR, and XOR. The total number of combinations is greater than 100,000. In total, we obtain for each variable an image of at least 1166x1167 pixels. The intensity of the pixels is proportional to the probability of the associated NIC. The color depends on the associated NIC. This image actually contains considerable information about the ability of the variable to make the prediction of Y, depending on the presence or absence of other variables. A basic CNNs model was trained for supervised classification. Results: The first results are impressive. Using the GSE22513 public data (Omic data set of markers of Taxane Sensitivity in Breast Cancer), DEEPNic outperformed other statistical methods, including XGBoost. We still need to generalize the comparison on several databases. Conclusion: The ability to transform any tabular variable into an image offers the possibility of merging image and tabular information in the same format. This opens up great perspectives in the analysis of metadata.

Keywords: tabular data, CNNs, NICs, DeepNICs, random forest of perfect trees, classification

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Authors: Trilochana Dash, Chandan Kumar Sahoo

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In today's environment of intensive industrialization, the role of business in societal modernization is critical. The concept of corporate social responsibility (CSR) arose due to rising societal awareness of company conduct. Corporations that practice CSR devote a portion of their profits to society’s sustainable development (SD). The concept of CSR and SD has increased the impact of industries on society. In this study, bibliometric analysis was conducted using the “R” programming language to determine the comprehensiveness of CSR and SD. From 2003 to 2022, bibliometric data was collected from two databases: Scopus and Web of Science (WOS). According to the findings, CSR and SD research has risen exponentially in the past two decades, and “Corporate Social Responsibility and Environment Management” emerged as the most influential journal in this field. The findings also show that relatively very few researchers collaborate in CSR and SD research in the last twenty years. It is widely acknowledged that most CSR and SD research is conducted in developed countries and developing countries undergoing fast industrialization. Thematic evolution and cluster analysis clearly show that the notion of CSR and SD among scholars has been quite popular over the last two decades. Finally, limitations and future directions are discussed.

Keywords: corporate social responsibility, sustainable development, bibliometric analysis, “R” programming language, visualization, holistic picture

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Authors: Sayna Bahraini, Wendy Gifford, Ian Graham, Liquaa Wazni, Suzettee Bremault-Phillips, Rebekah Hackbusch, Catrine Demers, Mary Egan

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Objective: Guidelines for palliative and spiritual care emphasize the importance of screening patients for spiritual suffering. The aim of this review was to synthesize the research evidence on the accuracy of measures used to screen adults for spiritual suffering. Methods: A systematic review has been conducted. We searched five scientific databases to identify relevant articles. Two independent reviewers screened extracted data and assessed study methodological quality. Results: We identified five articles that yielded information on 24 spiritual screening measures. Among all identified measures, the 2-item Meaning/Joy & Self-Described Struggle has the highest sensitivity (82-87%), and the revised Rush protocol has the highest specificity (81-90%). The methodological quality of all included studies was low. Significance of Results: While most of the identified spiritual screening measures are brief (comprise 1 to 12 number of items), few have sufficient accuracy to effectively screen patients for spiritual suffering. We advise clinicians to use their critical appraisal skills and clinical judgment when selecting and using any of the identified measures to screen for spiritual suffering.

Keywords: screening, suffering, spirituality, diagnostic test accuracy, systematic review

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Authors: Rasha Ahmadi

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Glioblastoma is one of the most frequent brain malignancies, having a high mortality rate and limited survival in individuals with this malignancy. Despite different treatments and surgery, recurrence of glioblastoma cancer stem cells may arise as a subsequent tumor. For this reason, it is crucial to research the markers associated with glioblastoma stem cells and specifically their microenvironment. In this study, using bioinformatics analysis, we analyzed and nominated genes in the microenvironment pathways of glioblastoma stem cells. In this study, an appropriate database was selected for analysis by referring to the GEO database. This dataset comprised gene expression patterns in stem cells derived from glioblastoma patients. Gene clusters were divided as high and low expression. Enrichment databases such as Enrichr, STRING, and GEPIA were utilized to analyze the data appropriately. Finally, we extracted the potential genes 2700 high-expression and 1100 low-expression genes are implicated in the metabolic pathways of glioblastoma cancer progression. Cellular senescence, MAPK, TNF, hypoxia, zimosterol biosynthesis, and phosphatidylinositol metabolism pathways were substantially expressed and the metabolic pathways were downregulated. After assessing the association between protein networks, MSMP, SOX2, FGD4 ,and CNTNAP3 genes with high expression and DMKN and SBSN genes with low were selected. All of these genes were observed in the survival curve, with a survival of fewer than 10 percent over around 15 months. hsa-mir-192-5p, hsa-mir-129-5p, hsa-mir-215-5p, hsa-mir-335-5p, and hsa-mir-340-5p played key function in glioblastoma cancer stem cells microenviroments. We introduced critical genes through integrated and regular bioinformatics studies by assessing the amount of gene expression profile data that can play an important role in targeting genes involved in the energy and microenvironment of glioblastoma cancer stem cells. Have. This study indicated that hsa-mir-192-5p, and hsa-mir-129-5p are appropriate candidates for this.

Keywords: Glioblastoma, Cancer Stem Cells, Biomarker Discovery, Gene Expression Profiles, Bioinformatics Analysis, Tumor Microenvironment

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Authors: R. Tamborrino, F. Rinaudo

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Digital technologies offer possibilities to effectively convert historical archives into instruments of knowledge able to provide a guide for the interpretation of historical phenomena. Digital conversion and management of those documents allow the possibility to add other sources in a unique and coherent model that permits the intersection of different data able to open new interpretations and understandings. Urban history uses, among other sources, the inventories that register human activities in a specific space (e.g. cadastres, censuses, etc.). The geographic localisation of that information inside cartographic supports allows for the comprehension and visualisation of specific relationships between different historical realities registering both the urban space and the peoples living there. These links that merge the different nature of data and documentation through a new organisation of the information can suggest a new interpretation of other related events. In all these kinds of analysis, the use of GIS platforms today represents the most appropriate answer. The design of the related databases is the key to realise the ad-hoc instrument to facilitate the analysis and the intersection of data of different origins. Moreover, GIS has become the digital platform where it is possible to add other kinds of data visualisation. This research deals with the industrial development of Turin at the beginning of the 20th century. A census of factories realized just prior to WWI provides the opportunity to test the potentialities of GIS platforms for the analysis of urban landscape modifications during the first industrial development of the town. The inventory includes data about location, activities, and people. GIS is shaped in a creative way linking different sources and digital systems aiming to create a new type of platform conceived as an interface integrating different kinds of data visualisation. The data processing allows linking this information to an urban space, and also visualising the growth of the city at that time. The sources, related to the urban landscape development in that period, are of a different nature. The emerging necessity to build, enlarge, modify and join different buildings to boost the industrial activities, according to their fast development, is recorded by different official permissions delivered by the municipality and now stored in the Historical Archive of the Municipality of Turin. Those documents, which are reports and drawings, contain numerous data on the buildings themselves, including the block where the plot is located, the district, and the people involved such as the owner, the investor, and the engineer or architect designing the industrial building. All these collected data offer the possibility to firstly re-build the process of change of the urban landscape by using GIS and 3D modelling technologies thanks to the access to the drawings (2D plans, sections and elevations) that show the previous and the planned situation. Furthermore, they access information for different queries of the linked dataset that could be useful for different research and targets such as economics, biographical, architectural, or demographical. By superimposing a layer of the present city, the past meets to the present-industrial heritage, and people meet urban history.

Keywords: digital urban history, census, digitalisation, GIS, modelling, digital humanities

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Authors: Shizra Waris, Saleem Akhtar

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Big data is a research area receiving attention from academia and IT communities. In the digital world, the amounts of data produced and stored have within a short period of time. Consequently this fast increasing rate of data has created many challenges. In this paper, we use functionalism and structuralism paradigms to analyze the genesis of big data applications and its current trends. This paper presents a complete discussion on state-of-the-art big data technologies based on group and stream data processing. Moreover, strengths and weaknesses of these technologies are analyzed. This study also covers big data analytics techniques, processing methods, some reported case studies from different vendor, several open research challenges and the chances brought about by big data. The similarities and differences of these techniques and technologies based on important limitations are also investigated. Emerging technologies are suggested as a solution for big data problems.

Keywords: computer, it community, industry, big data

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Authors: Lubisi L. V., Madumo M. B., Mudau N. P., Makhuvele L., Sibuyi M. M.

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Background and Aims: Learners with disabilities can be integrated into mainstream schools, whereas there are those learners that are accommodated in special schools based on the support needs they require. These needs, among others, pertain to access to high-intensity therapeutic support by physiotherapists, occupational therapists, and speech therapists. However, access to physiotherapists in low- and middle-income countries is limited, and this creates a knowledge gap in identifying, to the best of our knowledge, best practice patterns aligned with physiotherapy at special schools. This gap compromises the quality of support to be rendered towards strengthening rehabilitation and optimising the participation of learners with disabilities in special schools. The aim of the scoping review was to map the evidence on practice patterns employed by physiotherapists at special schools for learners with disabilities. Methods: The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines were followed. Key terms regarding physiotherapy practice patterns for learners with disabilities at special schools were used to search the literature on the databases. Literature was sourced from Google Scholar, EBSCO, PEDro, PubMed, and Research Gate from 2013 to 2023. A total of 28 articles were initially retrieved and after a process of screening and exclusion, nine articles were included. All the researchers reviewed the articles for eligibility. Articles were initially screened based on the titles, followed by full text. Articles written in English or translated into English mentioned physical / physiotherapy interventions in special schools, both published and unpublished, were included. A qualitative data extraction template was developed and an inductive approach to thematic data analysis was used for included articles to see which themes emerged. Results: Three themes emerged after inductive thematic data analysis. 1. Collaboration with educators, parents, and therapists 2. Family Centred Approach 3. Telehealth. Conclusion: Collaboration is key in delivering therapeutic support to learners with disabilities at special schools. Physiotherapists need to be collaborators at the level of interprofessional and transprofessional. In addition, they need to explore technology to work remotely, especially when learners become absent physically from school.

Keywords: learners with disabilities, special school, physiotherapists, therapeutic support

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Authors: Karolina Wieczorek, Sophie Wiliams

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Introduction: Patient data is often collected in Electronic Health Record Systems (EHR) for purposes such as providing care as well as reporting data. This information can be re-used to validate data models in clinical trials or in epidemiological studies. Manual validation of automated tools is vital to pick up errors in processing and to provide confidence in the output. Mentioning a disease in a discharge letter does not necessarily mean that a patient suffers from this disease. Many of them discuss a diagnostic process, different tests, or discuss whether a patient has a certain disease. The COVID-19 dataset in this study used natural language processing (NLP), an automated algorithm which extracts information related to COVID-19 symptoms, complications, and medications prescribed within the hospital. Free-text patient clinical patient notes are rich sources of information which contain patient data not captured in a structured form, hence the use of named entity recognition (NER) to capture additional information. Methods: Patient data (discharge summary letters) were exported and screened by an algorithm to pick up relevant terms related to COVID-19. Manual validation of automated tools is vital to pick up errors in processing and to provide confidence in the output. A list of 124 Systematized Nomenclature of Medicine (SNOMED) Clinical Terms has been provided in Excel with corresponding IDs. Two independent medical student researchers were provided with a dictionary of SNOMED list of terms to refer to when screening the notes. They worked on two separate datasets called "A” and "B”, respectively. Notes were screened to check if the correct term had been picked-up by the algorithm to ensure that negated terms were not picked up. Results: Its implementation in the hospital began on March 31, 2020, and the first EHR-derived extract was generated for use in an audit study on June 04, 2020. The dataset has contributed to large, priority clinical trials (including International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) by bulk upload to REDcap research databases) and local research and audit studies. Successful sharing of EHR-extracted datasets requires communicating the provenance and quality, including completeness and accuracy of this data. The results of the validation of the algorithm were the following: precision (0.907), recall (0.416), and F-score test (0.570). Percentage enhancement with NLP extracted terms compared to regular data extraction alone was low (0.3%) for relatively well-documented data such as previous medical history but higher (16.6%, 29.53%, 30.3%, 45.1%) for complications, presenting illness, chronic procedures, acute procedures respectively. Conclusions: This automated NLP algorithm is shown to be useful in facilitating patient data analysis and has the potential to be used in more large-scale clinical trials to assess potential study exclusion criteria for participants in the development of vaccines.

Keywords: automated, algorithm, NLP, COVID-19

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Authors: Branci Sarra, Branci Saadia

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Because of the multiplied data growth, many computer science tools have been developed to process and analyze these Big Data. High-performance computing architectures have been designed to meet the treatment needs of Big Data (view transaction processing standpoint, strategic, and tactical analytics). The purpose of this article is to provide a historical and global perspective on the recent trend of high-performance computing architectures especially what has a relation with Analytics and Data Mining.

Keywords: high performance computing, HPC, big data, data analysis

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Authors: Prashant Shrivastava

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The purpose of this study is to explore re3dat.org registry to identify research data repositories registration workflow process. Further objective is to depict a graph for present development of research data repositories in India. Preliminarily with an approach to understand re3data.org registry framework and schema design then further proceed to explore the status of research data repositories of India in re3data.org registry. Research data repositories are getting wider relevance due to e-research concepts. Now available registry re3data.org is a good tool for users and researchers to identify appropriate research data repositories as per their research requirements. In Indian environment, a compatible National Research Data Policy is the need of the time to boost the management of research data. Registry for Research Data Repositories is a crucial tool to discover specific information in specific domain. Also, Research Data Repositories in India have not been studied. Re3data.org registry and status of Indian research data repositories both discussed in this study.

Keywords: research data, research data repositories, research data registry, re3data.org

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Authors: Ilgin Gökaşar, Saman Ghaffarian

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The need for fast processed big data of transportation ridership (eg., smartcard data) and traffic operation (e.g., traffic detectors data) which requires a lot of computational power is incontrovertible in Intelligent Transportation Systems. Nowadays cloud computing is one of the important subjects and popular information technology solution for data processing. It enables users to process enormous measure of data without having their own particular computing power. Thus, it can also be a good selection for transportation big data processing as well. This paper intends to examine how the cloud computing can enhance transportation big data process with contrasting its advantages and disadvantages, and discussing cloud computing features.

Keywords: big data, cloud computing, Intelligent Transportation Systems, ITS, traffic data processing

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Authors: Nino Chikhladze, Tamar Dochviri, Nato Pitskhelauri, Maia Bitskhinashvili

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Population aging is a key social and demographic trend of the 21st century. Falls represent a prevalent geriatric syndrome that poses significant risks to the health and independence of older adults. The World Health Organization notes a lack of comprehensive data on falls in low- and middle-income countries, complicating the creation of effective prevention programs. To the authors’ best knowledge, no such studies have been conducted in Georgia. The aim of the study is to explore the epidemiology of falls in the elderly population. The hospitalization database of the National Center for Disease Control and Public Health of Georgia was used for the retrospective study. Falls-related injuries were identified using ICD-10 classifications using the class XIX (S and T codes) and class XX for the type of injury (V-Y codes). Statistical data analyses were done using SPSS software version 23.0. The total number of fall-related hospitalizations for individuals aged 65 and older from 2015 to 2021 was 29,697. The study revealed that falls accounted for an average of 63% (ranging from 59% to 66%) of all hospitalizations and 68% (ranging from 65% to 70%) of injury-related hospitalizations during this period. The 69% of all patients were women and 31%-men (Chi2=4482.1, p<0.001). The highest rate of hospitalization was in the age groups 80-84 and 75-79. The probability of fall-related hospitalization was significantly higher in women (p<0.001) compared to men in all age groups except 65-69 years. In the target age group of 65 years and older, the probability of hospitalization increased significantly with an increase in age (p<0.001). The study's results can be leveraged to create evidence-based awareness programs, design targeted multi-domain interventions addressing specific risk factors, and enhance the quality of geriatric healthcare services in Georgia.

Keywords: elderly population, falls, geriatric patients, hospitalization, injuries

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Authors: E. Y. Igde, S. Aydogan, F. E. Boran, D. Akay

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Patent data have an increasingly important role in economic growth, innovation, technical advantages and business strategies and even in countries competitions. Analyzing of patent data is crucial since patents cover large part of all technological information of the world. In this paper, we have used the linguistic summarization technique to prove the validity of the hypotheses related to patent data stated in the literature.

Keywords: data mining, fuzzy sets, linguistic summarization, patent data

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Authors: M. Kebisek, L. Spendla, M. Kopcek, T. Skulavik

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In our paper we describe the security capabilities of data collection. Data are collected with probes located in the near and distant surroundings of the company. Considering the numerous obstacles e.g. forests, hills, urban areas, the data collection is realized in several ways. The collection of data uses connection via wireless communication, LAN network, GSM network and in certain areas data are collected by using vehicles. In order to ensure the connection to the server most of the probes have ability to communicate in several ways. Collected data are archived and subsequently used in supervisory applications. To ensure the collection of the required data, it is necessary to propose algorithms that will allow the probes to select suitable communication channel.

Keywords: communication, computer network, data collection, probe

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Authors: Manahil Alfuraydan, Jodie Croxall, Lisa Hurt, Mike Kerr, Sinead Brophy

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Autism Spectrum Disorder (ASD) is a developmental condition characterised by impairment in terms of social communication, social interaction, and a repetitive or restricted pattern of interest, behaviour, and activity. There is a significant delay between seeking help and a confirmed diagnosis of ASD. This may result in delay in receiving early intervention services, which are critical for positive outcomes. The long wait times also cause stress for the individuals and their families. Telehealth potentially offers a way of improving the diagnostic pathway for ASD. This review of the literature aims to examine which telehealth approaches have been used in the diagnosis and assessment of autism in children and adults, whether they are feasible and acceptable, and how they compare with face-to-face diagnosis and assessment methods. A comprehensive search of following databases- MEDLINE, CINAHL Plus with Full text, Business Sources Complete, Web of Science, Scopus, PsycINFO and trail and systematic review databases including Cochrane Library, Health Technology Assessment, Database of Abstracts and Reviews of Effectiveness and NHS Economic Evaluation was conducted, combining the terms of autism and telehealth from 2000 to 2018. A total of 10 studies were identified for inclusion in the review. This review of the literature found there to be two methods of using telehealth: (a) video conferencing to enable teams in different areas to consult with the families and to assess the child/adult in real time and (b) a video upload to a web portal that enables the clinical assessment of behaviours in the family home. The findings were positive, finding there to be high agreement in terms of the diagnosis between remote methods and face to face methods and with high levels of satisfaction among the families and clinicians. This field is in the very early stages, and so only studies with small sample size were identified, but the findings suggest that there is potential for telehealth methods to improve assessment and diagnosis of autism used in conjunction with existing methods, especially for those with clear autism traits and adults with autism. Larger randomised controlled trials of this technology are warranted.

Keywords: assessment, autism spectrum disorder, diagnosis, telehealth

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Authors: Mai Ashraf Talaat

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Background: Vitamin C (ascorbic acid) is an essential nutrient for the development and repair of all body tissues. It can be obtained from a healthy diet or through supplementation. Due to its importance, vitamin C has become a mainstay in the treatment and prevention of many diseases and in maintaining immune, skin, bone and overall health. This review article aims to discuss the studies and case reports conducted to evaluate the effect of Vitamin C on oral health and the recent advances in oral medicine that involve the use of vitamin C. Data/Sources: The review was conducted for clinical studies, case reports and published literature in the English language that addresses this topic. An extensive search in the electronic databases of PubMed, PubMed Central, Web of Science, National Library of Medicine and ResearchGate was performed. Conclusion: Vitamin C is thought to treat periodontal diseases and gingival enlargement. It also affects biofilm formation and therefore, it helps in reducing caries incidence. Recently, vitamin C mesotherapy has been used to treat inflamed gingiva, bleeding gums and gingival hyperpigmentation. More research and randomized controlled trials are needed on this specific topic for more accurate judgment. Clinical significance: A minimally invasive approach - the usage of vitamin C in dental care could drastically reduce the need for surgical intervention.

Keywords: oral health, periodontology, vitamin C, Gingivitis

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Authors: Panagiotis Platanitis

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Purpose: This research paper seeks to review the empirical studies in the field of expatriate adjustment in East Asia in order to produce a thematic understanding of the current adjustment challenges, thus enabling practitioners to enrich their knowledge. Background: Learning to live, work, and function in a country and culture vastly different from that of one’s upbringing can pose some unique challenges in terms of adaptation and adjustment. This has led to a growing body of research about the adjustment of expatriate workers. Adjustment itself has been posited as a three-dimensional construct; work adjustment, interaction adjustment and general or cultural adjustment. Methodology: This qualitative systematic review has been conducted on all identified peer-reviewed empirical studies related to expatriate adjustment in East Asia. Five electronic databases (PsychInfo, Emerald, Scopus, EBSCO and JSTOR) were searched to December 2015. Out of 625 identified records, thorough evaluation for eligibility resulted in 15 relevant studies being subjected to data analysis. The quality of the identified research was assessed according to the Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields. The data were analysed by means of thematic synthesis for systematic reviews of qualitative research. Findings: Data analysis revealed five key themes. The themes developed were: (1) personality traits (2) types of adjustment, (3) language, (4) culture and (5) coping strategies. Types of adjustment included subthemes such as: Interaction, general, work, psychological, sociocultural and cross-cultural adjustment. Conclusion: The present review supported previous literature on the different themes of adjustment and it takes the focus from work and general adjustment to the psychological challenges and it introduces the psychological adjustment. It also gives a different perspective about the use of cross-cultural training and the coping strategies expatriates use when they are abroad. This review helps counselling psychologists to understand the importance of a multicultural approach when working with expatriates and also to be aware of what expatriates might face when working and living in East Asia.

Keywords: adjustment, counselling psychology, East Asia, expatriates

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Authors: Nay Myo Sandar

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With the growth of technologies and applications, a large amount of data has been producing at increasing rate from various resources such as social media networks, sensor devices, and other information serving devices. This large collection of massive, complex and exponential growth of dataset is called big data. The traditional database systems cannot store and process such data due to large and complexity. Consequently, cloud computing is a potential solution for data storage and processing since it can provide a pool of resources for servers and storage. However, moving large amount of data to and from is a challenging issue since it can encounter a high latency due to large data size. With respect to big data movement problem, this paper reviews the literature of previous works, discusses about research issues, finds out approaches for dealing with big data movement problem.

Keywords: Big Data, Cloud Computing, Big Data Movement, Network Techniques

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Authors: Xu Feifei

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The prevalence of COVID-19 currently is the biggest challenge to improving people's quality of life. Its impact on the health-related quality of life (HRQoL) is highly uncertain and has not been summarized so far. The aim of the present systematic review was to assess and provide an up-to-date analysis of the impact of the COVID-19 pandemic on the HRQoL of participants who have been infected, have not been infected but isolated, frontline, with different diseases, and the general population. Therefore, an electronic search of the literature in PubMed databases was performed from 2019 to July 2022 (without date restriction). PRISMA guideline methodology was employed, and data regarding the HRQoL were extracted from eligible studies. Articles were included if they met the following inclusion criteria: (a) reports on the data collection of the health state utility values (HSUVs) related to COVID-19 from 2019 to 2021; (b) English language and peer-reviewed journals; and (c) original HSUV data; (d) using EQ-5D tool to quantify the HRQoL. To identify studies that reported the effects on COVID-19, data on the proportion of overall HSUVs of participants who had the outcome were collected and analyzed using a one-group meta-analysis. As a result, thirty-two studies fulfilled the inclusion criteria and, therefore, were included in the systematic review. A total of 45295 participants and provided 219 means of HSUVs during COVID-19 were included in this systematic review. The range of utility is from 0.224 to 1. The study included participants from Europe (n=16), North America (n=4), Asia (n=10), South America (n=1), and Africa (n=1). Twelve articles showed that the HRQoL of the participants who have been infected with COVID-19 (range of overall HSUVs from 0.6125 to 0.863). Two studies reported the population of frontline workers (the range of overall HSUVs from 0.82 to 0.93). Seven of the articles researched the participants who had not been infected with COVID-19 but suffered from morbidities during the pandemic (range of overall HSUVs from 0.5 to 0.96). Thirteen studies showed that the HRQoL of the respondents who have not been infected with COVID-19 and without any morbidities (range of overall HSUVs from 0.64 to 0.964). Moreover, eighteen articles reported the outcomes of overall HSUVs during the COVID-19 pandemic in different population groups. The estimate of overall HSUVs of direct COVID-19 experience population (n=1333) was 0.751 (95% CI 0.670 - 0.832, I2 = 98.64%); the estimate of frontline population (n=610) was 0.906 ((95% CI 0.854 – 0.957, I2 = 98.61%); participants with different disease (n=132) were 0.768 (95% CI 0.515 - 1.021, I2= 99.26%); general population without infection history (n=29,892) was 0.825 (95% CI 0.766 - 0.885, I2 =99.69%). Conclusively, taking into account these results, this systematic review might confirm that COVID-19 has a negative impact on the HRQoL of the infected population and illness population. It provides practical value for cost-effectiveness model analysis of health states related to COVID-19.

Keywords: COVID-19, health-related quality of life, meta-analysis, systematic review, utility value

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Authors: Mohammad Javaherian, Siamak Bashardoust Tajali, Monavvar Hadizadeh

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Objective: This systematic review study was conducted to evaluate the effects of Pulsed Electromagnetic (PEMF) and Static Magnetic Fields (SMG) on pain relief and functional improvement in patients with musculoskeletal Low Back Pain (LBP). Methods: Seven electronic databases were searched by two researchers independently to identify the published Randomized Controlled Trials (RCTs) on the efficacy of pulsed electromagnetic, static magnetic, and therapeutic nuclear magnetic fields. The identified databases for systematic search were Ovid Medline®, Ovid Cochrane RCTs and Reviews, PubMed, Web of Science, Cochrane Library, CINAHL, and EMBASE from 1968 to February 2016. The relevant keywords were selected by Mesh. After initial search and finding relevant manuscripts, all references in selected studies were searched to identify second hand possible manuscripts. The published RCTs in English would be included to the study if they reported changes on pain and/or functional disability following application of magnetic fields on chronic musculoskeletal low back pain. All studies with surgical patients, patients with pelvic pain, and combination of other treatment techniques such as acupuncture or diathermy were excluded. The identified studies were critically appraised and the data were extracted independently by two raters (M.J and S.B.T). Probable disagreements were resolved through discussion between raters. Results: In total, 1505 abstracts were found following the initial electronic search. The abstracts were reviewed to identify potentially relevant manuscripts. Seventeen possibly appropriate studies were retrieved in full-text of which 48 were excluded after reviewing their full-texts. Ten selected articles were categorized into three subgroups: PEMF (6 articles), SMF (3 articles), and therapeutic nuclear magnetic fields (tNMF) (1 article). Since one study evaluated tNMF, we had to exclude it. In the PEMF group, one study of acute LBP did not show significant positive results and the majority of the other five studies on Chronic Low Back Pain (CLBP) indicated its efficacy on pain relief and functional improvement, but one study with the lowest sessions (6 sessions during 2 weeks) did not report a significant difference between treatment and control groups. In the SMF subgroup, two articles reported near significant pain reduction without any functional improvement although more studies are needed. Conclusion: The PEMFs with a strength of 5 to 150 G or 0.1 to 0.3 G and a frequency of 5 to 64 Hz or sweep 7 to 7KHz can be considered as an effective modality in pain relief and functional improvement in patients with chronic low back pain, but there is not enough evidence to confirm their effectiveness in acute low back pain. To achieve the appropriate effectiveness, it is suggested to perform this treatment modality 20 minutes per day for at least 9 sessions. SMFs have not been reported to be substantially effective in decreasing pain or improving the function in chronic low back pain. More studies are necessary to achieve more reliable results.

Keywords: pulsed electromagnetic field, static magnetic field, magnetotherapy, low back pain

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