Scientific Research Data Research Articles

Metasynthesis of Strategies Mode for Activating Higher Order Thinking Skills (HOTs) of University Students

Countries around the world are increasingly emphasizing the research and cultivation of higher-order thinking abilities, and have proposed various teaching strategies and methods. Researchers have developed various methods, strategies, and related activities to cultivate higher-order thinking in response to this issue, such as "scaffolding teaching", "heuristic teaching", "project-based teaching", "building collaborative learning", and "diverse evaluation feedback". However, current research on teaching strategies mostly focuses on methodology or teaching principles, lacking a teaching strategy model to guide teachers in specific teaching processes and designs. This study conducted a comprehensive integration of previous scientific research, aiming to summarize the strategic models for activating college students' HOTs in the past three years. By using Scopus, Science Direct, and Springer search agents for in-depth filtering, a total of 6 articles were selected. Data screening, evaluation, and CBA use two keywords, and the article is published from around 2020 to 2023. The research results indicate that in addition to the aforementioned teaching strategies, some emerging teaching models are also being widely explored and applied. The flipped learning teaching model shifts the knowledge imparted in the classroom to independent learning after class, while using classroom time to guide students in discussions and practice. Smart classrooms utilize modern technological means to provide a highly interactive and personalized learning environment. The blended learning model combines online and offline learning, fully utilizing the advantages of technology. The evidence-based teaching method emphasizes guiding teaching decisions and practices through scientific research and empirical data. These teaching strategies can effectively improve the higher-order thinking ability of college students. Therefore, integrated research has investigated some specific methods and strategy models for activating higher-order thinking skills among college students in recent years. These methods and strategy models are of great significance to educational practitioners and can provide reference for the teaching of HOTs.

Recent Books and Journal Articles in Public Opinion, Survey Methods, Survey Statistics, Big Data, Data Science, and User Experience Research. 2023 Update

Recent Books and Journal Articles in Public Opinion, Survey Methods, Survey Statistics, Big Data, Data Science, and User Experience Research. 2023 Update

Will Artificial Intelligence Replace Knowledge Centers? Assessment of the Situation

The study examines the historical journey of information, which started with stone and clay tablets and continued with papyrus, parchment, and paper forms, and ultimately its transformation into digital form with industry 4.0 & 5.0 information technologies (IT), the information age. The developments in IT, that is, computer, network/internet/www, have opened the doors of a new world. Based on these developments, the transformation of information and information resources into digital form, their inclusion in computers and networks, their movement at the global level (www), and their visibility and accessibility have revolutionized the world of information. It has opened the doors of a new world where the information transformed into digital form is used with open source software and internet (w2-w3) supported open data and accredited scientific research data supported by big data, and artificial intelligence software enables the information to think autonomously, produce content, speak and show actionable behaviors. The study tries to find an answer to the question that artificial intelligence (AI) could replace information centers that archive, process, manage, and provide access to accredited information and evaluate the situation.

Advances in precision therapy of low-grade serous ovarian cancer: A review.

Low-grade serous ovarian carcinoma (LGSOC) is a rare subtype of ovarian cancer that accounts for approximately 6% to 10% of serous ovarian cancers. The clinical treatment of LGSOC is similar to that of high-grade serous ovarian carcinoma, however, its clinical and molecular characteristics are different from those of high-grade serous ovarian carcinoma. This article reviews the research on gene diagnosis, surgical treatment, chemotherapy, and biological therapy of LGSOC, providing reference for clinical diagnosis and treatment of LGSOC. Surgery is the cornerstone of LGSOC treatment and maximum effort must be made to achieve R0 removal. Although LGSOC is not sensitive to chemotherapy, postoperative platinum-based combination chemotherapy remains the first-line treatment option for LGSOC. Additional clinical trials are needed to confirm the clinical benefits of chemotherapy and explore new chemotherapy protocols. Hormone and targeted therapies may also play important roles. Some patients, particularly those with residual lesions after treatment, may benefit from hormone maintenance therapy after chemotherapy. Targeted therapies, such as MEKi, show good application prospects and are expected to change the treatment pattern of LGSOC. Continuing to further study the genomics of LGSOC, identify its specific gene changes, and combine traditional treatment methods with precision targeted therapy based on second-generation sequencing may be the direction for LGSOC to overcome the treatment bottleneck. In future clinical work, comprehensive genetic testing should be carried out for LGSOC patients to accumulate data for future scientific research, in order to find more effective methods and drugs for the treatment of LGSOC.

The data science discovery program: A model for data science consulting in higher education

As one of the largest data science research incubator initiatives in the country, the University of California, Berkeley's Data Science Discovery Program serves as a case study for a scalable and sustainable model of data science consulting in higher education. This case contributes to the broader literature on data science consulting in higher education by analysing the programme's development, institutional influences; staffing and structural model; and defining features, which may prove instructive to similar programmes at other institutions. The programme is characterised by a unique structure of undergraduate consultations led by graduate student mentorship and governance; a streamlined, multidepartmental model that facilitates scalability and sustainability; and diverse modes for undergraduate consulting—including one‐on‐one ad‐hoc data science consultations, extended data science project development and management, peer mentorship and data science workshop instruction. This case demonstrates that universities may be able to initiate a low‐stakes, small‐scale data science consulting initiative and then progressively scale up the project in collaboration with multiple departments and organisations across campus.

ESM Cloud Toolkit: A Copilot for Energy Storage Material Research

Searching and designing new materials play crucial roles in the development of energy storage devices. In today’s world where machine learning technology has shown strong predictive ability for various tasks, the combination with machine learning technology will accelerate the process of material development. Herein, we develop ESM Cloud Toolkit for energy storage materials based on MatElab platform, which is designed as a convenient and accurate way to automatically record and save the raw data of scientific research. The ESM Cloud Toolkit includes multiple features such as automatic archiving of computational simulation data, post-processing of experimental data, and machine learning applications. It makes the entire research workflow more automated and reduces the entry barrier for the application of machine learning technology in the domain of energy storage materials. It integrates data archive, traceability, processing, and reutilization, and allows individual research data to play a greater role in the era of AI.

Employing Algorithms in Cosmetic Facial Surgery

The aim is to evaluate the scientific literature on the creation and application of algorithms in clinical settings, with a focus on their efficacy. Material and methods. The literature search was performed using the databases Scopus, Medline (PubMed), eLibrary (in total: 32 scientific papers). Results. The acquisition of data pertaining to the principles of algorithm development was accomplished, and the notion that algorithms will increasingly be adopted across a range of plastic surgery specialties was validated. Conclusions. Based on scientific research data, it can be inferred that the development and implementation of algorithms play a significant role in an aesthetic surgeon’s work in surgical practice. All authors unanimously support the effectiveness of integrating algorithms into clinical practice, some of which were developed by them for the first time. When planning and executing surgical procedures, it is emphasized to base on individual patient data, as well as the relevance of such developments in improving the achievement of better surgical correction outcomes. In some instances, the implementation of algorithms in clinical practice has led to a significant improvement in patients' quality of life after the interventions. Each correction method has its unique effectiveness. The positive results of the work on the development and implementation of algorithms in clinical practice suggest that the development of this type for aesthetic interventions on the lower third of the face would allow for: 1) improved aesthetic outcomes, 2) enhanced intervention quality, 3) a reduction in the number of interventions necessary to achieve a positive and long-lasting effect, 4) a decrease in the likelihood of short-term and long-term postoperative complications, and 5) lower treatment costs and shorter treatment duration.

A Practical Use Case: Lesson Learned From Social Science Research Data Centers

A Practical Use Case: Lesson Learned From Social Science Research Data Centers

Spatiotemporal extended homogeneous field correction method for reducing complex interference in OPM-MEG

Novel magnetoencephalography (MEG) systems based on optically pumped magnetometers (OPMs) have undergone rapid development in recent years. However, environmental interference significantly degrades data quality. When the number of sensors in the OPM-MEG system is small, the traditional subspace projection denoising algorithms reliant on sensor space oversampling will be difficult to apply. Although the recently proposed homogeneous field correction (HFC) method resolves this problem by constructing a low-rank spatial model, it lacks the ability to suppress complex environmental interference such as nonhomogeneous fields. Therefore, this paper proposes a novel OPM-MEG environmental interference suppression method based on HFC. We first use a projection operator constructed from a sensor orientation matrix to project original data and empty-room noise data onto the null space of the homogeneous field; this enables dimensionality reduction to eliminate homogeneous field interference. The remaining interference is then suppressed through subspace projection in the space and time domains. We compare our method to four benchmark algorithms based on simulations and somatosensory-evoked experiments. The experimental results demonstrate that the proposed method has better interference suppression performance than the benchmark algorithms. Therefore, our method can provide high signal-to-noise ratio data for subsequent clinical applications and brain scientific research.

Multiperspective Islamic Study Methods

The purpose of this research is to describe various examples of the application of Islamic study methods in psychology. The author uses a descriptive qualitative method with a type of literature study approach. The data were obtained from documentation originating from various books, research journals, as well as various other documents or records that are worthy of being used as supporting material for obtaining scientific research data. In the process of data analysis, the writer took several stages that had to be carried out, namely (1) reading and examining various sources of data that had been obtained, (2) defining the nature of Islamic and psychological study methods, (3) analysing the relevance of Islamic study methods to psychology, and (4) concludes several applications of Islamic methods in psychological review. The results of the research found describe that the application of Islamic study methods in a psychological review consists of three points of discussion, namely (1) the 'iddah period for women, (2) the obligation to earn a living for men, and (3) the principle of child education based on the period development.

A Learnable Discrete-Prior Fusion Autoencoder with Contrastive Learning for Tabular Data Synthesis

The actual collection of tabular data for sharing involves confidentiality and privacy constraints, leaving the potential risks of machine learning for interventional data analysis unsafely averted. Synthetic data has emerged recently as a privacy-protecting solution to address this challenge. However, existing approaches regard discrete and continuous modal features as separate entities, thus falling short in properly capturing their inherent correlations. In this paper, we propose a novel contrastive learning guided Gaussian Transformer autoencoder, termed GTCoder, to synthesize photo-realistic multimodal tabular data for scientific research. Our approach introduces a transformer-based fusion module that seamlessly integrates multimodal features, permitting for mining more informative latent representations. The attention within the fusion module directs the integrated output features to focus on critical components that facilitate the task of generating latent embeddings. Moreover, we formulate a contrastive learning strategy to implicitly constrain the embeddings from discrete features in the latent feature space by encouraging the similar discrete feature distributions closer while pushing the dissimilar further away, in order to better enhance the representation of the latent embedding. Experimental results indicate that GTCoder is effective to generate photo-realistic synthetic data, with interactive interpretation of latent embedding, and performs favorably against some baselines on most real-world and simulated datasets.

Neurocognitive disorders and chronic rhinosinusitis. A modern view of the problem (literary review)

The purpose of the study: to review the results of scientific research on combined etiopathogenetic, clinical, and functional manifestations of chronic rhinosinusitis and a number of neurological diseases classified as neurocognitive disorders (NCDs). The interdisciplinary approach in the analysis was based on the modern understanding of neurocognitive disorders and induced neuroinflammatory reactions, the commonality between the infectious agents in the pathogenesis of chronic rhinosinusitis and diseases characterized by NCDs, and the validity of the involvement of central brain structures in the process of chronic inflammation of the nasal cavity and paranasal sinuses. The analogy of the formation of such manifestations as nasal obstruction, changes in the olfactory tract and visual function, and disorders of the microbiota of the upper respiratory tract, and their impact on brain structures with impaired neurocognitive functions was drawn. The systematization of scientific research data characterizing various chronic forms of rhinosinusitis and NCDs was a confirmation of the regulatory role of the autonomic nervous system and the hypothalamic-limbic-reticular complex in understanding the common pathophysiological mechanisms occurring in these nosologies.

Ukrainian Thoughts and Feelings based on One Year War Content Analysis

In the age of remarkable technological innovations such as AI, quantum computing, IoT, and blockchain, one could argue that the world is becoming an increasingly connected, advanced, enlightened, and peaceful place. Nevertheless, paradoxically, conflicts and wars are still born deeply rooted in complex strategic, historical, and economic dynamics, and continue to arise finding a direct representation in both physical and digital battlefields through powerful and dynamic environments like social media. This environment enabled unprecedented connectivity and ideas, thoughts, ideologies, and experiences exchange shaping the narrative, amplifying ideas, feelings, and experiences while directly addressing the underlying causes, dynamics, and implications of conflicts and war through a multifaceted and multi-nuanced approach. The outbreak and ongoing war in Ukraine took the international community off guard representing a significant turning point in the contemporary era that received immediately intensive global attention, united global humanitarian, and strategic efforts to support and help the ones in need, and reflected its geopolitical and economic global complexities, dynamics, and implications. At the same time, a high degree of disinformation and misinformation exists surrounding different aspects about this war e.g., actions taken, and impact produced on civilians, which contributes to creating an altered perspective of reality. While different conventional media and social media outlets together with research, governmental, and practitioner efforts revealed and reflected on the nature, major incidents, and their known impact on civilians and civilian objects as well as military personnel and military objects, yet limited attention and a lower voice is given through dedicated studies to thoughts and emotions of Ukrainian users in unconventional social media platforms like Telegram. This represents the knowledge gap tackled in this research by building a set of Machine Learning-based solutions for analysing the discourses and sentiments of Ukrainian users using the Design Science Research methodology in a Data Science research approach. From this analysis, reflection is provided to important days and incidents experienced by Ukrainian population since February 2022 in the first year of war.

Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review.

Patient health data collected from a variety of nontraditional resources, commonly referred to as real-world data, can be a key information source for health and social science research. Social media platforms, such as Twitter (Twitter, Inc), offer vast amounts of real-world data. An important aspect of incorporating social media data in scientific research is identifying the demographic characteristics of the users who posted those data. Age and gender are considered key demographics for assessing the representativeness of the sample and enable researchers to study subgroups and disparities effectively. However, deciphering the age and gender of social media users poses challenges. This scoping review aims to summarize the existing literature on the prediction of the age and gender of Twitter users and provide an overview of the methods used. We searched 15 electronic databases and carried out reference checking to identify relevant studies that met our inclusion criteria: studies that predicted the age or gender of Twitter users using computational methods. The screening process was performed independently by 2 researchers to ensure the accuracy and reliability of the included studies. Of the initial 684 studies retrieved, 74 (10.8%) studies met our inclusion criteria. Among these 74 studies, 42 (57%) focused on predicting gender, 8 (11%) focused on predicting age, and 24 (32%) predicted a combination of both age and gender. Gender prediction was predominantly approached as a binary classification task, with the reported performance of the methods ranging from 0.58 to 0.96 F1-score or 0.51 to 0.97 accuracy. Age prediction approaches varied in terms of classification groups, with a higher range of reported performance, ranging from 0.31 to 0.94 F1-score or 0.43 to 0.86 accuracy. The heterogeneous nature of the studies and the reporting of dissimilar performance metrics made it challenging to quantitatively synthesize results and draw definitive conclusions. Our review found that although automated methods for predicting the age and gender of Twitter users have evolved to incorporate techniques such as deep neural networks, a significant proportion of the attempts rely on traditional machine learning methods, suggesting that there is potential to improve the performance of these tasks by using more advanced methods. Gender prediction has generally achieved a higher reported performance than age prediction. However, the lack of standardized reporting of performance metrics or standard annotated corpora to evaluate the methods used hinders any meaningful comparison of the approaches. Potential biases stemming from the collection and labeling of data used in the studies was identified as a problem, emphasizing the need for careful consideration and mitigation of biases in future studies. This scoping review provides valuable insights into the methods used for predicting the age and gender of Twitter users, along with the challenges and considerations associated with these methods.

Open and FAIR: Trends in scientific publishing and the implications for official statistics

The FAIR data principles have emerged as a major focus in the world of scientific research data, but have not had as large an impact on official statistics. While there are good reasons for this, FAIR developments within the research community may be of interest to official statistical organizations. These include the increased availability of research data, improvements in the area of machine-actionable metadata, and a focus on provenance information which could lead to increased transparency and data quality. Some activities of interest are described as a starting point for those in official statistics who may wish to follow these developments.

Medicinal Importance and Therapeutic Benefit of Bioactive Flavonoid Eriocitrin: An Update on Pharmacological Activity and Analytical Aspects

Background: Polyphenols are micronutrients and an important class of secondary metabolites that are naturally present in various types of plants and their derived byproducts. Humans obtain polyphenols and phytochemicals from different vegetables, fruits, spices, and teas. More than 8,000 different types of polyphenols have been isolated from different natural sources. Polyphenols play an important role in the plants’ resistance against pests and diseases. Plants from citrus class contain flavonoids, carotenoids, and ascorbic acid. Eriocitrin is a flavanone glycoside present in lemon juice and other citrus plants. Methods: Scientific research data on eriocitrin have been collected from Google, Google Scholar, Pub- Med, and Scopus databases, and analyzed in the present paper to determine the biological importance of eriocitrin in medicine. The medicinal importance and therapeutic benefit of eriocitrin in medicine have been investigated through literature data analysis of various research works. Pharmacological activity data have been thoroughly collected in the present work and analyzed in order to assess the health-beneficial aspects of eriocitrin against human disorders. Results: Scientific database analysis has revealed the medicinal importance and therapeutic benefit of eriocitrin in medicine. Scientific data analysis has revealed the anti-inflammatory, anti-nociceptive, and anti-obesity activities of eriocitrin. Further scientific data analysis has revealed the biological effect of eriocitrin on osteoarthritis, hepatocellular carcinoma, oral carcinogenesis, hepatic steatosis, oxidative damage, and different types of enzymes. In addition, eriocitrin has also been reported to exert potent antioxidant and suppressive effects on oxidative stress. Scientific data analysis has also revealed the significance of analytical techniques for the separation, isolation, and identification of eriocitrin. Conclusion: The present work indicates the biological importance of eriocitrin in medicine and other allied health sectors.

Biological Importance, Pharmacological Activities, and Nutraceutical Potential of Capsanthin: A Review of Capsicum Plant Capsaicinoids.

Carotenoids are natural hydrocarbons that play an important role in photomorphogenesis, photosynthesis, photoprotection, development, and defense mechanism of plants. Carotenoids have good anti-oxidants and provitamin A contents with their additional colorant nature, which are indispensable to plants and human diets. Capsicum species are well known for their culinary uses worldwide; they are not only cultivated as vegetables but used in numerous medicinal preparations as well due to their medicinal aspects. This article aims to collect data on the beneficial aspects of capsaicinoids with a major emphasis on capsanthin. In order to instigate the biological potential and therapeutic benefit of capsanthin in medicine, in the present work, scientific research data on capsanthin were collected from different literature sources and analyzed. The biological potential of Capsicum annuum in medicine was also investigated through literature data analysis of different scientific research work. Scientific data on capsanthin were collected from Google, Google Scholar, PubMed, Science Direct, and Scopus using the term capsanthin and capsicum in the present work. Detailed pharmacological activities of capsanthin were presented and discussed in the present work through scientific data analysis of research work. Analytical techniques for the separation, isolation, and identification of capsanthin were taken into consideration in this work. Scientific data analysis revealed the biological importance and therapeutic benefit of capsanthin and capsicum in medicine. Capsicum annuum is a member of the Solanaceae family, which is one of the most cultivated spices worldwide. Capsaicinoids are one of the main classes of phytochemicals found in chili peppers, i.e., Capsicum annuum, and are mainly responsible for the pungent and spicy flavor of chili peppers. Capsanthin is a crystalline red color pigment found as the main component of Capsicum annuum fruits during ripening. Capsanthin is also found in Lilium, Aesculus, Berberis, and Asparagus officinalis. Chemically, capsanthin contains a cyclopentane ring, 11 conjugated double bonds, and a conjugated keto group. Capsanthin is a powerful antioxidant, exhibits anti-tumor activities, attenuates obesity-induced inflammation, and raises plasma HDL cholesterol levels. Scientific studies have proven the pharmacological benefits of capsanthin in medicine as it is helpful in pain relief, cardioprotection, weight loss, and body temperature regulation. Moreover, it also has anti-inflammatory, anticancer, antioxidant, and antimicrobial activities. In the literature database, numerous extraction and isolation techniques have been documented for capsanthin. In addition, the analytical techniques and other bioanalytical tools for the isolation and identification of capsanthin were also discussed in the present article. Medicinal importance and pharmacological activities of capsanthin were reviewed and discussed in this paper. This review aimed to highlight the literature on capsanthin in drug discoveries with their analytical development.

A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms

Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to identify and classify abnormal cardiac rhythms accurately. However, the primary drawback of the current AI models is that most of these models are heavy, computationally intensive, and inefficient in terms of cost for real-time implementation. In this review, we first discuss the current state-of-the-art AI models utilized for ECG-based cardiac rhythm classification. Next, we present some of the upcoming modeling methodologies which have the potential to perform real-time implementation of AI-based heart rhythm diagnosis. These models hold significant promise in being lightweight and computationally efficient without compromising the accuracy. Contemporary models predominantly utilize 12-lead ECG for cardiac rhythm classification and cardiovascular status prediction, increasing the computational burden and making real-time implementation challenging. We also summarize research studies evaluating the potential of efficient data setups to reduce the number of ECG leads without affecting classification accuracy. Lastly, we present future perspectives on AI's utility in precision medicine by providing opportunities for accurate prediction and diagnostics of cardiovascular status in patients.

BIBLIOMETRIC ANALYSIS OF RESEARCH ON PUBLIC ADMINISTRATION REFORM

This article complements the initial studies based on bibliometric analysis. The relevance of biblio-metric analysis is the study of a large amount of data in scientific research. The purpose of the study is to conduct a bibliometric analysis to determine the state of research toward reforming public administra-tion and future areas of research and mapping.In our research strategy, 151 articles were selected on the reforms carried out in the civil service in 1991-2023. With VOSviewer software, bibliometric networks were created and visualized. The biblio-metric analysis of the study used the Scopus academic base to reform the public administration system. The conclusions of these studies were drawn and presented in this article providing an opportunity for further understanding of the systematic development of the study.The success of the study informed the dynamics of research development in the public administra-tion system. The best articles, publications, and studies of the most cited authors and organizations on the management of the state system are identified, and they contribute to the scientific literature, de-scribing its current state. In studies using bibliometric analysis, elements of transparency and productivity have been identi-fied that can be used in public administration and e-government reforms. In addition, the study demon-strated the experience of developing public system management reforms in peer-reviewed publications around the world. In Kazakhstan, these studies have been intensively conducted since 2020, while in developed countries such as the United States and the United Kingdom, a high rate of use has been observed since 2010.

Intelligent Monitoring of Data Quality Based on Multiple Data Structures

In the current context of big data, data quality management has become a hot topic in data science research. This article focused on the intelligent monitoring needs of complex data structures and explored data quality evaluation indicators and monitoring methods for complex environments. Key technologies such as data fusion, cleaning, and quality evaluation were focused on research, and a data quality monitoring system for multi-dimensional data environments was constructed to verify its application effectiveness in multi-source data environments, as well as specific measures to improve data quality. On this basis, a data quality evaluation model based on data mining was utilized, and a real-time monitoring system was established for application research. When the number of consistency issues was 30, the response time was 300ms, and the success rate of processing was 95%. The lowest processing success rate was 88%, and the highest was 100%. The overall processing success rate of the system was very good. The research results of this article provided theoretical and methodological support for enterprises to effectively carry out data quality management, which has important theoretical significance and application value.