Scientific Research Data Research Articles

Implementation of Visual Mind Mapping Strategy for Improving Students’ Performance

The aim of the article was to substantiate the implementation of the visual mind mapping (VMM) strategy for improving the effectiveness of students’ learning in the theoretical and experimental aspect. The focus was the use of VMM to organize learning of students of non-Humanities majors when studying the subjects of the Humanities curriculum, which are mandatory for the undergraduate level. Methods: the following methods were used to collect empirical data and interpret research results: online monitoring through questionnaires in Google Forms; testing with the involvement of the computer programme potential using the EdApp microlearning platform tools; Computer-Assisted Personal Interviewing (CAPI), classic methods of conducting scientific research and statistical data processing. The prospects of further research include a wider integration of VMM with the involvement of special software and determining its impact on the effectiveness of the students’ education.

A decision-making framework utilizing machine learning techniques, based on analysing the sentiment of tweets

Before making any decision, especially a financial one, people use social media to chat to, learn about, and obtain advice from others. How can social media opinions be relied on and utilised economically? Due to social media sites like Facebook and Twitter, where millions of individuals offer real-time thoughts on anything using slang and emoticons, data science researchers have access to more unstructured yet useful information. Social media includes Twitter. 326 million active social media users sent 500 million tweets daily in July 2018, indicating the sector's rapid expansion. Tweets can be viewed, edited, and sent. Knowing how to interpret tweets, which might be facts or views, is helpful. Such a study may predict the stock market, election, response event, news, and subjectivity scales. Tweets are analysed using Sentiment Analysis (SA). On the other hand, Sentiment analysis provides a broad overview of tweet polarity and is not beneficial for decision-making. In this research, we design a natural language processing (NLP) Twitter SA model to overcome the challenges of tweet sentiment recognition and build a hybrid system that can recognise and classify sentiment in Twitter's real-time reactions to any topic. We use a three-classifier machine learning method to analyse tweets from the Sentiment140 dataset (Logistic Regression, Bernoulli Naive Bayes, and SVM). Our findings are supported by the Term Frequency-Inverse Document Frequency analysis (TF-IDF). After that, accuracy and F1 Scores are used to evaluate these classifiers.

Trends and Advances on The K-Hyperparameter Tuning Techniques in High-Dimensional Space Clustering

Clustering is one of the tasks performed during exploratory data analysis with an extensive and wealthy history in a variety of disciplines. Application of clustering in computational medicine is one such application of clustering that has proliferated in the recent past. K-means algorithms are the most popular because of their ability to adapt to new examples besides scaling up to large datasets. They are also easy to understand and implement. However, with k-means algorithms, k-hyperparameter tuning is a long standing challenge. The sparse and redundant nature of the high-dimensional datasets makes the k-hyperparameter tuning in high-dimensional space clustering a more challenging task. A proper k-hyperparameter tuning has a significant effect on the clustering results. A number of state-of-the art k-hyperparameter tuning techniques in high-dimensional space have been proposed. However, these techniques perform differently in a variety of high-dimensional datasets and data-dimensionality reduction methods. This article uses a five-step methodology to investigate the trends and advances on the state of the art k-hyperparameter tuning techniques in high-dimensional space clustering, data dimensionality reduction methods used with these techniques, their tuning strategies, nature of the datasets applied with them as well as the challenges associated with the cluster analysis in high-dimensional spaces. The metrics used in evaluating these techniques are also reviewed. The results of this review, elaborated in the discussion section, makes it efficient for data science researchers to undertake an empirical study among these techniques; a study that subsequently forms the basis for creating improved solutions to this k-hyperparameter tuning problem.

Open datasets in perioperative medicine: a narrative review.

With the growing interest of researchers in machine learning and artificial intelligence (AI) based on large data, their roles in medical research have become increasingly prominent. Despite the proliferation of predictive models in perioperative medicine, external validation is lacking. Open datasets, defined as publicly available datasets for research, play a crucial role by providing high-quality data, facilitating collaboration, and allowing an objective evaluation of the developed models. Among the available datasets for surgical patients, VitalDB has been the most widely used, with the Medical Informatics Operating Room Vitals and Events Repository recently launched and the Informative Surgical Patient dataset for Innovative Research Environment expected to be released soon. For critically ill patients, the available resources include the Medical Information Mart for Intensive Care, the eICU Collaborative Research Database, the Amsterdam University Medical Centers Database, and the High time Resolution ICU Dataset, with the anticipated release of the Intensive Care Network with Million Patients' information for the AI Clinical decision support system Technology dataset. This review presents a detailed comparison of each to enrich our understanding of these open datasets for data science and AI research in perioperative medicine.

Economic policy to support lifelong learning system development & SDG4 achievement: Bibliometric analysis

In order to set economic policy goals, it is important to understand the difference between adult education and lifelong learning, and how much research on SDG 4 combines lifelong learning and economic policy. The purpose of the article is to determine the main directions for justifying the lifelong learning system development, including for achieving sustainable development goal 4 (SDG 4). Based on scientific research data from the Scopus database using the VOSviewer software, this article analyzed the theoretical foundations for substantiating the temporal and geographical interrelationships of the categorical-conceptual system of such terms as “SDG 4”, “adult education”, “lifelong learning” and “economic”. This made it possible to identify the main trends in scientific research and cluster directions of international research on the relationship between adult education, lifelong learning and economic policy. The following trends were obtained: adult education as a driver of economic development; as a social phenomenon and as a source of innovation. The following clusters were identified: adult education as a part of life-long education; adult education under the influence of physical and age-related changes; adult education as part of professional education; the learning process, which is not related to professional activity. As a result of the analysis, an insufficient level of attention among scientific studies devoted to adult education within the framework of SDG 4 was revealed. The article confirmed the need for economic policy to support the lifelong learning system, as well as the difference between the concepts of adult education and lifelong learning. Acknowledgment The educational outcomes in this publication were created with the support of the EU Erasmus+ program within the framework of projects ERASMUS-JMO-2021-HEI-TCH-RSCH-101048055 – «AICE – With Academic integrity to EU values: step by step to common Europe» and ERASMUS-JMO-2022-HEI-TCH-RSCH-101085198«OSEE – Open Science and Education in Europe: success stories for Ukrainian academia». This study was funded by the grant from the Ministry of Education and Science of Ukraine “Modelling educational transformations in wartime to preserve the intellectual capital and innovative potential of Ukraine” (registration number 0123U100114). “Convergence of economic and educational transformations in the digital society: modeling of the impact on regional and national security” (state registration number 0121U109553).

The Stanford Medicine data science ecosystem for clinical and translational research.

To describe the infrastructure, tools, and services developed at Stanford Medicine to maintain its data science ecosystem and research patient data repository for clinical and translational research. The data science ecosystem, dubbed the Stanford Data Science Resources (SDSR), includes infrastructure and tools to create, search, retrieve, and analyze patient data, as well as services for data deidentification, linkage, and processing to extract high-value information from healthcare IT systems. Data are made available via self-service and concierge access, on HIPAA compliant secure computing infrastructure supported by in-depth user training. The Stanford Medicine Research Data Repository (STARR) functions as the SDSR data integration point, and includes electronic medical records, clinical images, text, bedside monitoring data and HL7 messages. SDSR tools include tools for electronic phenotyping, cohort building, and a search engine for patient timelines. The SDSR supports patient data collection, reproducible research, and teaching using healthcare data, and facilitates industry collaborations and large-scale observational studies. Research patient data repositories and their underlying data science infrastructure are essential to realizing a learning health system and advancing the mission of academic medical centers. Challenges to maintaining the SDSR include ensuring sufficient financial support while providing researchers and clinicians with maximal access to data and digital infrastructure, balancing tool development with user training, and supporting the diverse needs of users. Our experience maintaining the SDSR offers a case study for academic medical centers developing data science and research informatics infrastructure.

A data science practicum to introduce undergraduate students to bioinformatics for research.

An explosion of data available in the life sciences has shifted the discipline toward genomics and quantitative data science research. Institutions of higher learning have been addressing this shift by modifying undergraduate curriculums resulting in an increasing number of bioinformatics courses and research opportunities for undergraduates. The goal of this study was to explore how a newly designed introductory bioinformatics seminar could leverage the combination of in-class instruction and independent research to build the practical skill sets of undergraduate students beginning their careers in the life sciences. Participants were surveyed to assess learning perceptions toward the dual curriculum. Most students had a neutral or positive interest in these topics before the seminar and reported increased interest after the seminar. Students had increases in confidence level in their bioinformatic proficiency and understanding of ethical principles for data/genomic science. By combining undergraduate research with directed bioinformatics skills, classroom seminars facilitated a connection between student's life sciences knowledge and emerging research tools in computational biology.

Beyond mathematics, statistics, and programming: data science, machine learning, and artificial intelligence competencies and curricula for clinicians, informaticians, science journalists, and researchers

ABSTRACT Data science, machine learning and artificial intelligence applications impact clinicians, informaticians, science journalists, and researchers. Most biomedical data science training focuses on learning a programming language in addition to higher mathematics and advanced statistics. This approach is appropriate for graduate students but greatly reduces the number of individuals in healthcare who can be involved in data science. To serve these four stakeholder audiences, we describe several curricular strategies focusing on solving real problems of interest to these audiences. Relevant competencies for these audiences include using intuitive programming tools that facilitate data exploration with minimal programming background, creating data models, evaluating results of data analyses, and assessing data science research reports, among others. Offering the curricula described here more broadly could broaden the stakeholder groups knowledgeable about and engaged in data science.

Biological Importance of Flavonoid Bavachinin in the Medicine: Perspectives of Medicinal Importance, Pharmacological Activities and Analytical Techniques

Backgrounds: Phytochemicals are an important class of natural compounds present in vegetables, herbs, fruits, and seeds. Phytochemicals have been used in medicine for the treatment of human disorders in the form of drugs, medicine, and Nutraceuticals. Flavonoid class secondary metabolites were found to be present in medicinal plants and some food materials derived from plants. Flavonoid class phytochemicals have beneficial health aspects and numerous pharmacological activities in the medicine. Psoralea corylifolia has been widely used in medicine for the treatment of skin diseases, including vitiligo, alopecia areata, leukoderma, and psoriasis. More than 90 different types of phytocompounds have been separated and isolated from Psoralea corylifolia. Bavachinin is a flavonoid class phytochemical, found to be present in the seeds of Psoralea corylifolia. Bavachinin possesses anti-bacterial, anti-oxidative, anti-inflammatory, α-glucosidase and nitric oxide inhibitory potential. Method: Scientific data on bavachinin have been collected from different literature databases such as Google, Google Scholar, PubMed, Science Direct and Scopus in the present work and analyzed to know the biological importance of bavachinin. Scientific research data on bavachinin have been collected in the present work for their medicinal importance, pharmacological activities and analytical aspects. Further, all the collected scientific data have been separated into different sub-sections i.e., Medicinal importance, pharmacological activities and analytical aspects of bavachinin. Detailed pharmacological activity data of bavachinin have been analyzed in the present work to know the therapeutic potential of bavachinin in medicine. Analytical data of bavachinin have been collected and analyzed in the present work to know the biological importance of bavachinin in modern medicine for the standardization of Psoralea corylifolia. Results: Literature data analysis of different scientific research works revealed the biological importance of flavonoids in medicine. Flavonoid class phytochemicals have anti-inflammatory, antioxidant, anti-viral, anti-cancer and anti-ageing properties in medicine. Scientific data analysis revealed the effectiveness of bavachinin in cancer, blood glucose, Alzheimer's disease, Parkinson's disease, inflammation, immune system, T cell differentiation, oxidative damage and enzymes. However, therapeutic efficacy, metabolism, biotransformation, pharmaceutical product development and pharmacokinetic parameters of bavachinin have also been discussed in the present work. Analytical data signified the importance of modern analytical tools for the separation, isolation and identification of bavachinin. Conclusion: Scientific data analysis of different research work revealed the biological importance and therapeutic benefit of bavachinin in medicine.

GENETIC BASES FOR IMPROVING THE REPRODUCTIVE AND PRODUCTIVE QUALITIES OF THE SOUTH-KAZAKH MERINOES BRED IN THE DESERT CONDITIONS CHU-ILI LOW MOUNTAINS AND THE MOIYN-KUM SANDS

The article reflects the data of scientific research that can serve the practice of selection and breeding work under conditions of various forms of ownership, as well as the improvement of selection and breeding work and the technology of production of high-quality and competitive sheep products in the conditions of year-round use of the foothill-steppe and desert pastures of the south of Kazakhstan. The research of polymorphism systems of serum proteins of sheep's blood of different intra-breed types by gender and age groups revealed the presence of certain allele combinations and the ratio of transferrin and hemoglobin genotypes. It has been established that these animals have their own specific range of frequencies of occurrence of alleles and combinations of genotypes. Based on the results obtained, the possibility of using genetic blood markers in the early assessment of the productive qualities of animals has been established. As a result of scientifically based breeding methods, in the “Batai-Shu” LLP in Zhambyl region created a breeding australized herd of the South Kazakh Merino breed with a white wool yolk, a cut of washed wool of 3.1 kg with a yield of 48-50%.

Pyogenic granuloma — hypotheses of etiopathogenesis, clinical forms and features, modern diagnostic methods and choice of rational treatment tactics

Objective — to analyze modern hypotheses of etiopathogenesis and clinical features of pyogenic granulomas (PG) and to determine the importance of ultrasound of these neoplasms for the choice of individualized tactics of their treatment, taking into account the stage of tumor proliferation. Materials and methods. During 2020—2022, 94 patients with a diagnosis of «pyogenic granuloma» (PG) underwent outpatient treatment under our supervision at Dr. Bogomolets Institute of Dermatology and Cosmetology (Kyiv). The largest number of patients with PG examined by us was aged from 1 to 12 years (72 %), which correlates with the data of scientific research by other authors on the relevant problem. Anamnesis was collected in all examined patients (according to the patients and their parents) regarding the possible causes of neoplasms and the nature of their development. All the patients underwent a complex diagnostic instrumental examination of PG (dermoscopy, ultrasound, histopathology). The choice of treatment tactics was carried out individually in each specific clinical case, taking into account the results of the examination and the stage of tumor proliferation (radio wave removal or treatment with a 595­nm pulsed dye laser was carried out). Results and discussion. A total of 94 patients with PG were examined and treated on an outpatient basis. By age, children from 1 to 8 years old (48 %) and from 8 to 12 years old (24 %) prevailed among the examined. There were 6 % of teenagers and young adults (12—18 years old), 22 % over 18 years old. In 45 patients, neoplasms were localized on the skin of the face, in 7 — on the neck, in 9 — on the upper limbs, in 6 — on the lower limbs, in 27 — on the skin of the trunk. In 87 clinical cases, radio wave removal of neoplasms with their subsequent pathohistological diagnosis was carried out taking into account the results of a comprehensive examination of patients with PG, including the data of ultrasound of the skin in the areas affected by PG, which makes it possible to determine the depth of tumor invasion together with the exophytic part. In 6 clinical cases, PG removal therapy was selected and performed using a pulsed vascular dye laser with a wavelength of 595 nm. In one clinical case, taking into account the sufficiently large size of the tumor (more than 2 cm) and the significant vascularization of the neoplasm revealed by ultrasound, a diagnostic excisional biopsy was performed within healthy tissues. Recurrences of PG after treatment were observed after 2 months in 2 of 94 patients, in particular, after pulsed vascular laser therapy. Subsequently, in these two patients, PG was removed using the radio wave method. No recurrences of the disease were registered. Conclusions. The analysis of modern literature on the problem of PG and the results of own clinical observation and treatment of patients with the corresponding neoplasms indicate that ultrasound of these skin tumors is a sufficiently informative and promising diagnostic method that allows identifying the stage of proliferation and choosing the rational individualized treatment tactics. Considering that the use of ultrasound of skin tumors is a relatively new methods in dermatology, further research in the relevant direction is appropriate, which will increase its diagnostic informativeness.

A new sequential homogeneous pixel selection algorithm for distributed scatterer InSAR

ABSTRACT Distributed scatterer interferometric synthetic aperture radar (DS InSAR) technology has been widely used in various fields. Homogeneous pixel selection is a crucial step in the use of DS InSAR, directly affecting the estimation precision and reliability of subsequent parameter calculations. The existing algorithms for selecting homogeneous pixels have inherent limitations, such as requiring many heterogeneous samples and strict requirements surrounding the required number of synthetic aperture radar (SAR) images. To address these problems, a new sequential selection algorithm for homogeneous pixels is proposed, based on the Baumgartner – Weiss–Schindler (BWS) test algorithm and dynamic interval estimation (DIE) theory. According to Monte Carlo simulation experiments, the average standard deviation (STD) of the mean of the rejection of the BWS-DIE algorithm under six sample conditions is 0.014. Compared with three existing algorithms, including the Kolmogorov‒Smirnov (KS), BWS and fast statistically homogeneous pixel selection (FaSHPS) algorithms, the BWS-DIE algorithm improves homogeneous pixel selection precision by 64.3%, 69.4% and 25.3%, respectively. In the real data experiment, 12 scenes of Advanced Land Observing Satellite-1 Phased Array type L-band Synthetic Aperture Radar (ALOS-1 PALSAR) data from February 2007 to March 2011 were used and the BWS-DIE multitemporal InSAR (MT InSAR) method based on the BWS-DIE algorithm was applied to surface subsidence monitoring in the western mining area of Xuzhou, Jiangsu Province, China. The experimental results show that, compared with the Stanford Method for Persistent Scatterers (StaMPS), the BWS-DIE MT InSAR method improves the ability to monitor the maximum subsidence by 12.3%, increases the point density by 5.7 times and decreases the root mean square error (RMSE) by 50%. In addition, new surface deformation patterns are found in the spatial-temporal evolution. The above experimental results show that the proposed BWS-DIE algorithm exhibits remarkable advantages in selection power and selection precision and is not limited by the number of SAR images. The proposed algorithm can further broaden the application scenarios for DS InSAR and provide high-quality and reliable monitoring data for subsequent scientific research.

Health Research with Data in a Time of Privacy: Which Information do Patients Want?

When hospitals ask broad consent for the secondary use of patient data for scientific research, it is unknown for which studies the data will be used. We investigated what patients at a cancer hospital consider to be an adequate level and most suitable method of information provision using questionnaires (n = 71) and interviews (n = 24). A part of the respondents indicated that they would feel sufficiently informed by either being notified about potential further use, or by receiving a general brochure before being asked for consent. Others stated that additional information would be interesting and appreciated. Yet, when discussing required resources needed to provide additional information, interviewees lowered the bar of what they considered minimally required, voicing the importance of spending resources on research.

An Enhanced Data Transmission in Mobile Banking Using LSB-AES Algorithm

The advent of mobile banking applications has transformed the way customers’ access banking services from brick-and-mortar to remote banking. The ubiquitous nature of this innovation has encouraged its adoption. This is because of improved banking services and accessibility to the services on a 24/7 basis using the internet. However, mobile banking applications are susceptible to numerous security threats and vulnerabilities that adversaries take advantage of to siphon money from bank customers. The aim of this study is to design and evaluate least significant bit and advanced encryption standard cryptography (LSB-AES) hybrid algorithm to protect data on transmission in mobile banking. This study employs data science research design, and performance evaluations demonstrated through simulations using MATLAB. Findings of this study can be applied to banks offering mobile banking across the world. This study utilized six color images from University of Southern California’s Signal and Image Processing Institute (USC-SIPI) dataset which were stored in Tagged Image File Format (TIFF). Contemporary steganographic systems utilize a minimum of 2 to 10 images for test simulations. Visual quality analysis of cover images and stego images was done using the following evaluation metrics: Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and histogram analysis. Security of the proposed algorithm was done using entropy analysis. MSE values of cover and stego images should be closer to zero and indicate that cover image is of good quality for embedding data. On the other hand, PSNR values should be more than 40 decibel (dB) which indicates good imperceptibility. Histogram analysis should demonstrate no visible distortions between the cover and stego image for it to be free from statistical attacks. Entropy analysis should yield values close to 8 for the algorithm to be robust. Results from the proposed LSB-AES hybrid algorithm evaluation metrics reveals that Mean Squared Error (MSE) values ranges from 0.0001297 to 0.0005646 while Peak Signal-to-Noise Ratio (PSNR) values ranges from 80.65 to 87.71 and entropy values ranges from 6.295 to 7.762. Histogram analysis reveals that the cover and stego images are almost similar. These results infer that the proposed algorithm has good quality images with good imperceptibility and that the proposed algorithm is reliable, robust and secure for mobile banking. Entropy and histogram analysis results show that the proposed algorithm is resistant to Man-in-the-Middle attacks. This study recommends that legislation evaluates and amends security of mobile banking policies so that the proposed LSB-AES hybrid algorithm can be adopted as a secure solution for mobile banking.

The Quora website for students in learning the skill of Arabic Reading on the Internet (an analytical and descriptive study) | موقع كورا (Quora) للطلبة في تعلم مهارة القراءة على الوسائل الانترنت (دراسة تحليلية وصفية)

Electronic or computerized information sources have become an important part of libraries, especially those that aim to keep pace with modern developments to keep users up to date on scientific research, seminars and conferences covering all fields. This study aims to describe the Quora site as a site for students to learn in Internet-based Arabic reading skills. This research is a type of qualitative literature study with descriptive and analytical methods. Library sources are used to obtain data for scientific research. The object of this research is the Quora site. The results showed that 1) The idea of creating a Quora site is based on the fact that most human experience, knowledge, and opinions are still not available on the Internet, but can be found in people's books and experiences; 2) Quora aims to share all the knowledge available in the world through people's contributions to this knowledge base; 3) Quora allows everyone to access it.

EMOJIFIER

Emojis are small images that are commonly included in social media text messages. The combination of visual and textual content in the same message builds up a modern way of communication. Emojis or avatars are ways to indicate nonverbal cues. These cues have become an essential part of online chatting, product review, brand emotion, and many more. It also led to increasing data science research dedicated to emoji-driven storytelling. With advancements in computer vision and deep learning, it is now possible to detect human emotions from images. Here we will classify human facial expressions to filter and map corresponding emojis or avatars. Emojifier is software which deals with the classification of facial expressions, text and speech into Emojis or Avatars.

Data science research in sub-Saharan Africa: Ethical considerations in crowdsourcing for community engagement.

The growth in data science research in sub-Saharan Africa raises important ethical questions for the collection and use of 'big data' in this context, with particularly disparate implications for the most vulnerable and marginalised populations. While enhanced public involvement may be able to mitigate some of these risks, data science presents some unique barriers to community engagement efforts, including limited data literacy, lack of transparency in data collection and use, and little opportunity to 'opt out' from participation. The participatory approach of crowdsourcing offers a promising solution to address the critical need for community engagement. Crowdsourcing involves inviting a group to contribute solutions to a problem, and then publicly sharing the results for implementation. By crowdsourcing stakeholder ideas for innovative ways to enhance public involvement in data science research, the Research for Ethical Data Science in Southern Africa (REDSSA) project is leading the efforts to close the community engagement gap. Promising strategies that emerge from these efforts will ultimately help to shape more ethical and equitable data science research in Africa as this field continues to grow.

The role of an ethics advisory committee in data science research in sub-Saharan Africa.

The role of an ethics advisory committee in data science research in sub-Saharan Africa.

Setting up data science research in Africa and engagement of stakeholders

Setting up data science research in Africa and engagement of stakeholders

Revisiting community engagement methods in the context of data science research and big data use in South Africa.

Revisiting community engagement methods in the context of data science research and big data use in South Africa.