Popular Field Of Study Research Articles

A Bibliometric Analysis of the Spatial Transcriptomics Literature from 2006 to 2023

In recent years, spatial transcriptomics (ST) research has become a popular field of study and has shown great potential in medicine. However, there are few bibliometric analyses in this field. Thus, in this study, we aimed to find and analyze the frontiers and trends of this medical research field based on the available literature. A computerized search was applied to the WoSCC (Web of Science Core Collection) Database for literature published from 2006 to 2023. Complete records of all literature and cited references were extracted and screened. The bibliometric analysis and visualization were performed using CiteSpace, VOSviewer, Bibliometrix R Package software, and Scimago Graphica. A total of 1467 papers and reviews were included. The analysis revealed that the ST publication and citation results have shown a rapid upward trend over the last 3 years. Nature Communications and Nature were the most productive and most co-cited journals, respectively. In the comprehensive global collaborative network, the United States is the country with the most organizations and publications, followed closely by China and the United Kingdom. The author Joakim Lundeberg published the most cited paper, while Patrik L. Ståhl ranked first among co-cited authors. The hot topics in ST are tissue recognition, cancer, heterogeneity, immunotherapy, differentiation, and models. ST technologies have greatly contributed to in-depth research in medical fields such as oncology and neuroscience, opening up new possibilities for the diagnosis and treatment of diseases. Moreover, artificial intelligence and big data drive additional development in ST fields.Graphical Spatial transcriptomics (ST) technologies and application prospects. (1) Imaging-based approaches, including in situ sequencing (ISS)—where transcripts are amplified and sequenced in tissue—and ISH-based approaches—where imaging probes are sequentially hybridized in tissue. (2) NGS-based techniques, in which positional information is encoded onto transcripts prior to NGS sequencing.

Investigation of Corrosion Inhibition Efficiency of Pyridine-Quinoline Compounds through Machine Learning

Corrosion in materials is a significant concern for the industrial and academic fields because corrosion causes enormous losses in various fields such as the economy, environment, society, industry, security, safety, and others. Currently, material damage control using organic compounds has become a popular field of study. Pyridine and quinoline stand out as corrosion inhibitors among a myriad of organic compounds because they are non-toxic, inexpensive, and effective in a variety of corrosive environments. Experimental investigations in developing various candidate potential inhibitor compounds are time and resource-intensive. In this work, we use a quantitative structure-property relationship (QSPR)-based machine learning (ML) approach to investigate support vector machine (SVR), random forest (RF), and k-nearest neighbors (KNN) algorithms as predictive models of inhibition performance. (Inhibition efficiency) corrosion of pyridine-quinoline derivative compounds as corrosion inhibitors on iron. We found that the RF model showed the best predictive ability based on the coefficient of determination (R2) and root mean squared error (RMSE) metrics. Overall, our study provides new insights regarding the ML model in predicting corrosion inhibition on iron surfaces.

Investigation of the effects of synthetic wind speed parameters and wind speed distribution on system size and cost in hybrid renewable energy system design

The accuracy of wind speed data has an important impact on determining power output from a Hybrid Renewable Energy System including wind turbine. For a better estimation of energy price or payback period, it is crucial to determine both monthly average wind speed values and distribution parameters. Studies evaluating the sensitivity of wind speed distribution parameters concerning the optimal system size are lacking in the literature. This work fills the gap in the literature and for the first time, the effects of wind speed distribution parameters and wind speed distribution on the optimum system are statistically demonstrated. According to this case study, if these parameters are not set correctly, the Cost of Energy may be faulty between −14.024% and +37.804% for the Photovoltaic-Wind-Battery-Inverter system, may be faulty between −19.024% and +97.05% for the Wind-Battery-Inverter system and may be faulty between −56.437% and +65.250% for the Wind-Inverter system. When the default distribution parameters of the HOMER software are used, the errors are −0.609%, −18.30%, −34.215% respectively. This study reveals that in addition to monthly average wind speed values, distribution parameters should be chosen as accurately as possible. The innovative aspect of this work is the statistical presentation of how synthetic wind speed distribution can affect Hybrid Renewable Energy System sizing and at the same time Cost of Energy. Its importance is that it provides more precise results by guiding optimization studies using synthetic meteorological data, which is a popular field of study.

A 30-year bibliometric analysis of the literature in the disciplines of orthopedics, surgery, or oncology on chondrosarcoma from 1993 to 2023.

A thorough bibliometric analysis of publications published in the field of chondrosarcoma research has not yet been performed using the Web of Science database, especially for publications published between 1993 and 2023. This study, with a focus on the fields of orthopedics, surgery, and oncology, aims to fill this knowledge gap by providing a thorough analysis of current knowledge in the field of chondrosarcoma. In this bibliometric study, a literature search was performed using the Web of Science database to find all publications on chondrosarcoma. A bibliometric software program was used for data visualization and analysis (opensource visualization application, Vosviewer). The Web of Science Core Collection data used for this retrospective bibliometric study, which covers the period from January 1993 to September 2023, revealed interesting trends in chondrosarcoma research. As the most popular fields of study, orthopedics, surgery, and oncology account for a sizable portion of publications. A noteworthy increase in research output from 2014 to 2023, accounting for 41.74% of the papers, reflects the thriving research environment. The leading countries for publication were China, Japan, and the United States, demonstrating cross-border cooperation in chondrosarcoma research. Their contributions were highlighted by their important affiliations with institutions such as Harvard University, Leiden University, and China Medical University Taiwan. A thorough keyword mapping analysis also highlighted research priorities and encouraged interdisciplinary cooperation. The field's scholarly importance and ongoing relevance are highlighted by the study's high citation count (30,076) and highly cited articles. Overall, this study offers crucial insights into the development and collaborative nature of the chondrosarcoma research landscape and its long-lasting influence on academic research and clinical practice.

Percepcja własnych kompetencji przez studentów psychologii – raport z badania pilotażowego

Psychology has been one of the most popular fields of study among high school graduates for several years. It is worth revising the most desirable competences of a modern psychologist in order to ensure high quality of education and, in the long run, to respond to the contemporary needs of clients in the very dynamically changing conditions of work. They are affected, among others, by the rapid development of new technologies, the coronavirus pandemic, and the war going on abroad. With such intensified socio-economic changes and the ever-increasing demand for psychological services, it is worth devoting time to discussing the most important competences of a modern psychologist. The aim of this study is both to attempt to determine the competences of a modern psychologist and to check whether – and if so, how – the mode of study (full-time and part-time) differentiates the self-assessment of these competences in the group of 239 psychology students surveyed. The data obtained as a result of the pilot study with the author’s questionnaire confirmed the existence of significant differences between the compared groups in the vast majority of the distinguished competences.

Peran farmasis di industri obat di Indonesia sebagai gambaran lapangan kerja alumni farmasi Universitas Mataram

The unemployment rate rises every year, leading people to seek information for the purpose of gaining and providing jobs, which can be achieved by attending university. Given its bright future prospects, pharmacy is a popular field of study. A pharmacist may work in the industrial sector; according to Ministry of Health data from 2019, Indonesia had up to 230 industry pharmaceuticals. The rise of Indonesia's pharmaceutical industry will require a huge number of employees, especially experienced pharmaceutical professionals. Unfortunately, the results of tracer studies for Pharmacy graduates from the University of Mataram who work in the industrial sector are still quite low. According to a similar survey, a lack of interest in pharmaceutical graduates can be related to a lack of knowledge about the pharmaceutical industry. The purpose of this community service activity is to encourage pharmacy students to work in the pharmaceutical industry. Service activities can be carried out in three stages: preparation, implementation, monitoring, and evaluation. The results of the activity showed that the participants were enthusiastic about the program, as seen in the way they engaged in asking questions and responding to the question from the committee. The result of the evaluation revealed that 93.2% of the participants planned to work in the pharmaceutical industry. As a result, this community service activity is seen to have the potential to help inspire and encourage pharmacy students to pursue careers in the pharmaceutical industry.

Analysis and Experimental Investigation of Sound Absorbing Materials for Industrial Noise Control

Abstract These days, noise pollution is a major issue that affects the environment. The high amount of noise present in the industries has adverse impact on the workers, operators and people associated in those fields. The consequences could be as hazardous as heart attack or even lifelong hearing loss. Sound absorption through barriers, mufflers are some popular fields of study. Some natural extracted materials show impressive SAC at lower and mid frequencies. Moreover, natural materials usage in this field is being popular as they emit almost zero amount of greenhouse gases and their manufacturing process requires a little amount of energy to process for ready to usage. A galvanized steel pipe as the impedance tube was chosen because it is cheaper than the impedance tube available in the market. We have tried to measure sound absorption coefficient of some combinations of different materials as an acoustic barrier material.

Analysis and Prediction of Student Performance by Using A Hybrid Optimized BFO-ALO Based Approach

Data mining offers effective solutions for a variety of industries, including education. Research in the subject of education is expanding rapidly because of thebigquantityof student data that can be utilized to uncover valuable learning behavior patterns. This research presents a method for forecasting the academic presentation of students in Portuguese as well as math subjects, and it is describing with the help of 33 attributes. Forecasting the educationalattainment of students is the most popular field of study in the modern period. Previous research has employed a variety of categorization algorithms to forecast student performance. Educational data mining is a topic that needs a lot of research to improve the precision of the classification technique and predict how well students will do in school. In this study, we made a method to predict how well a student will do that uses a mix of optimization techniques. BFO and ALO-based popular optimization techniques were applied to the data set. Python was used to process all the files and conduct a performance comparison analysis. In this study, we compared our model's performance with various existing baseline models and examined the accuracy with which the hybrid algorithm predicted the student data set. To verify the expected classification accuracy, a calculation was performed. The experiment's findings indicate that the BFO-ALO Based hybrid model, which, out of all the methods, with a 94.5 percent success rate, is the preferred choice.

Dataset: Digital Forensic Literature with a Focus on Software: Trends, Institutions, and Authors (2012-2022)

This data article presents a thorough examination of digital forensic literature with a focus on software from 2012 to 2022, retrieved from lens.org, with the aim of identifying the most influential institutions, authors, and research trends. Using keywords "digital forensic AND software," the data collection process involved filtering by date range, document type, and subject matter, resulting in 427 journal articles. The analysis highlights the most productive institutions, years, fields of study, active authors, and publishers. This data is valuable for researchers and practitioners in digital forensics, particularly those interested in software applications, providing insights into the most significant contributions and aiding future research development.
 Highlights:
 
 Research in the field of digital forensics is growing rapidly, with the Université de Lausanne being the leading institution in the number of publications.
 Computer science is the most popular field of study in digital forensics, with a focus on analysing software and file systems.
 Elsevier B.V. is the leading publisher in digital forensics, with the journal Digital Investigation as the main source of publications.
 

Algorithms and Bounds for Complex and Quaternionic Lattices With Application to MIMO Transmission

Lattices are a popular field of study in mathematical research, but also in more practical areas like cryptology or multiple-input/multiple-output (MIMO) transmission. In mathematical theory, most often lattices over real numbers are considered. However, in communications, complex-valued processing is usually of interest. Besides, by the use of dual-polarized transmission as well as by the combination of two time slots or frequencies, four-dimensional (quaternion-valued) approaches become more and more important. Hence, to account for this fact, well-known lattice algorithms and related concepts are generalized in this work. To this end, a brief review of complex arithmetic, including the sets of Gaussian and Eisenstein integers, and an introduction to quaternion-valued numbers, including the sets of Lipschitz and Hurwitz integers, are given. On that basis, generalized variants of two important algorithms are derived: first, of the polynomial-time LLL algorithm, resulting in a reduced basis of a lattice by performing a special variant of the Euclidean algorithm defined for matrices, and second, of an algorithm to calculate the successive minima—the norms of the shortest independent vectors of a lattice—and its related lattice points. Generalized bounds for the quality of the particular results are established and the asymptotic complexities of the algorithms are assessed. These findings are extensively compared to conventional real-valued processing. It is shown that the generalized approaches outperform their real-valued counterparts in complexity and/or quality aspects. Moreover, the application of the generalized algorithms to MIMO communications is studied, particularly in the field of lattice-reduction-aided and integer-forcing equalization.

MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers

While Math Word Problem (MWP) solving has emerged as a popular field of study and made great progress in recent years, most existing methods are benchmarked solely on one or two datasets and implemented with different configurations. In this paper, we introduce the first open-source library for solving MWPs called MWPToolkit, which provides a unified, comprehensive, and extensible framework for the research purpose. Specifically, we deploy 17 deep learning-based MWP solvers and 6 MWP datasets in our toolkit. These MWP solvers are advanced models for MWP solving, covering the categories of Seq2seq, Seq2Tree, Graph2Tree, and Pre-trained Language Models. And these MWP datasets are popular datasets that are commonly used as benchmarks in existing work. Our toolkit is featured with highly modularized and reusable components, which can help researchers quickly get started and develop their own models. We have released the code and documentation of MWPToolkit in https://github.com/LYH-YF/MWPToolkit.

Oral History Bridges the Gap between Academic and Public History

Abstract This paper focuses on the importance of oral history and its role in connecting public history and academic history. Oral history since 1948 has become a popular field of study in history. In this manner, the main question of this research is how oral history is to bring public history into the world of academic history. The purpose of this paper is to give attention to collecting public history by researchers and oral historians through the oral history process to save history in an academic and organised way in the way of perhaps converting the history of the public or non-academic works to academic and providing them into libraries and archives.

Relative wage premium as a result of residence relocation of university graduates

Changing the place of residence after obtaining a university degree is one of the components of a broader issue of internal migration. The migration of people with higher education is a popular object of research, but until recently there were no data on migration immediately following graduation. The aim of the article is to demonstrate the occurrence of a relative wage premium related to this migration within a year after graduating, i.e. an increase in earnings resulting from moving to a different city. The study focused on groups of people who obtained their diploma in 2018 in the most popular fields of study within economics and technical sciences. An appropriate aggregation of data was made from the Polish Graduate Tracking System using the Social Insurance Institution (ZUS) data. It has been shown that the values of the relative earnings index for the analysed fields of study determined in this system were always higher for people who after obtaining their diploma in 2018 lived in 2019 in a voivodship other than their place of study, compared to those who remained in the powiat of their alma mater. This indicates the economic determinants of migration, i.e. the wage premium, related to the relocation of residence after obtaining a diploma.

A Survey Study for The Comparison of Meta-Analysis Softwares

In recent years, meta-analysis studies have become a popular field of study in order to quantitatively analyze the results obtained from more than one study carried out independently from the same subject and to interpret the results from a holistic perspective. Because of this, different licensed and open source software has been developed in the last 20 years. With this study, it is aimed to compare the existing programs and software in terms of meta-analysis in terms of quantitative and qualitative aspects. For this purpose, the results obtained from Jamovi, CMA, RStudio and Meta-Essentials programs were compared within the determined criterias. As a result of filtering the 1906 studies obtained from databases in accordance with the flow diagram, the number of studies to be included in the meta-analysis was determined to be 32. As a result of the study, forest plots obtained from different programs do not differ according to the program used. In addition, the open source and free of R, Jamovi and Meta-Essentials programs have been determined as the superiority of the programs compared to CMA. According to the findings, Jamovi has much more test for heterogeneity compaired to the other softwares. At the same time, the number of model prediction options is much higher for Jamovi compared to Meta-Essentials and CMA. While other software does not provide any information regarding model fit index and information criterion, Jamovi offers a rich output in this regard. It can be said that these features make Jamovi advantageous over other software.

Improving the low-temperature properties of an exo-inulinase via the deletion of a loop fragment located in its catalytic pocket

Engineering thermal adaptations of enzymes is a popular field of study. Enzymes active at low temperature have been used in many industries; however, reports seldom describe improvements in enzyme activity at low temperatures using protein engineering. Multiple amino acid sequence alignment of glycoside hydrolase (GH) family 32 showed an unconserved region located in the catalytic pocket. The exo-inulinase InuAGN25 showed the highest frequency of charged amino acid residues (47.4%) in this region among these GH 32 members. Notably, five consecutive charged amino acid residues (137EEDRK141) were modeled as a loop fragment in this region of InuAGN25. Deletion of the loop fragment broke two salt bridges, one cation–π interaction, and the α-helix–loop–310-helix structure at the N-terminal tail. The mutant exo-inulinase RfsMutE137Δ5 without the loop fragment was expressed in Escherichia coli, digested using human rhinovirus 3C protease for removal of the fused sequence at the N-terminus, and purified using immobilized metal affinity chromatography. Compared to the wild-type enzyme, the optimum temperature and t1/2 at 50°C of purified RfsMutE137Δ5 decreased by 10°C and 31.7 min, respectively, and the activities at 20°C and 30°C increased by 11% and 18%, respectively. In this study, we engineered the loop to obtain the mutant exo-inulinase that showed an improved performance at low temperatures. These findings suggest that the loop may be a useful target in formulating rational designs for engineering thermal adaptations of GH 32 exo-inulinases. How to cite: He L, Zhang R, Shen J. et al. Improving the low-temperature properties of an exo-inulinase via the deletion of a loop fragment located in its catalytic pocket. Electron J Biotechnol 2022;55. https://doi.org/10.1016/j.ejbt.2021.09.004

Offensiveness of controversial products for students of three different fields of study in Brazil

Purpose: investigate the offensiveness of controversial products in Brazil, especially the influence of the field of study. Design/methodology/approach: a questionnaire with 11 controversial products was conventionally answered by a sample of 368 college students from the three most popular fields of study in Brazil (i.e. Human Sciences, Biological and Health Sciences, and Exact and Technological Sciences). The offensiveness of each controversial product was measured and compared across those fields of study through t-tests. Findings: Controversial products did not easily offend respondents being guns, weight-loss medicine, and female underwear the most offensive ones. In general, students of Human Sciences are the most easily offended and those of Exact and Technological Sciences the least offended. In addition, results showed differences and similarities between people from each field of study. Originality/value: Improvement of our knowledge about controversial products in Brazil, especially about the influence of the field of study in the offensiveness of controversial products.

Collateral Victim or Rescue Worker?-The Role of Histone Methyltransferases in DNA Damage Repair and Their Targeting for Therapeutic Opportunities in Cancer.

Disrupted DNA damage signaling greatly threatens cell integrity and plays significant roles in cancer. With recent advances in understanding the human genome and gene regulation in the context of DNA damage, chromatin biology, specifically biology of histone post-translational modifications (PTMs), has emerged as a popular field of study with great promise for cancer therapeutics. Here, we discuss how key histone methylation pathways contribute to DNA damage repair and impact tumorigenesis within this context, as well as the potential for their targeting as part of therapeutic strategies in cancer.

Sentiment Analysis of Covid-19 Related Contents in Microblogs

With the emergence of Web 2.0, internet users share their feelings, thoughts and ideas with other people using social networks. Understanding people's thought analysis is important for examining marketing and user feedback in social networks. For this reason, sentiment analysis on social networks with machine learning algorithms is a popular field of study. Our study is based on thesentiment analysis of people against the new coronavirus, which affects the world. People can have different moods due to pandemia. The governance of mental issues must be observed to manage the pandemic time period more successfully. In this article, we retrieved 387,953 tweets due to the ten most frequently used COVID-19 related keywords. The most frequently used keywords about COVID-19 which enable to obtain and assess the reaction of Twitter users are investigated. Even if COVID-19 is a health issue and tweets about COVID-19 is expected to contain negative content, we found positive, negative and neutral tweets to analyze texts using sentiment analysis and machine learning approaches. We applied four classifiers like logistic regression, multinomial naive Bayes, support vector machines and decision tree. These classifiers are well studied and utilized in many studies which we mentioned in our study. The performance of the support vector machine, decision tree and logistic regression classifiers are close to each other. The lowest F-score is obtained from multinominal naive Bayes classifier. The classification results for each negative, neutral and positive class were compared separately in our study.

EasyRL: A Simple and Extensible Reinforcement Learning Framework

In recent years, Reinforcement Learning (RL), has become a popular field of study as well as a tool for enterprises working on cutting-edge artificial intelligence research. To this end, many researchers have built RL frameworks such as openAI Gym, and KerasRL for ease of use. While these works have made great strides towards bringing down the barrier of entry for those new to RL, we propose a much simpler framework called EasyRL, by providing an interactive graphical user interface for users to train and evaluate RL agents. As it is entirely graphical, EasyRL does not require programming knowledge for training and testing simple built-in RL agents. EasyRL also supports custom RL agents and environments, which can be highly beneficial for RL researchers in evaluating and comparing their RL models.

The decision to pursue a degree in immunology may be influenced by the timing at which students are introduced to the field

Abstract The decision to pursue an academic plan of study is influenced by several factors. Educational and personal experiences through childhood, adolescence, and young adulthood can have a powerful impact on the academic preferences of students. Immunology, a popular field of study at the graduate level, is largely ignored at the undergraduate level. Identifying the type and timing of experiences that influence the choice of current undergraduate and graduate students to pursue immunology is critical for developing and refining immunology curricula. To identify potential influencing factors, students were surveyed about their exposure to the immune system or immunology-related topics. There were 283 total respondents (45% immunology majors, 55% other science majors) of which 39% were graduate students and 61% were undergraduate students. There were no significant differences in the demographics of immunology majors compared to non-majors. Although there is no difference between immunology majors and non-majors in the number of respondents having an educational or personal experience involving immunology, an educational experience was more influential than a personal experience for both graduate (85%) and undergraduate (66%) immunology majors. However, more undergraduates (33.7%) pursue immunology based upon a personal experience compared to graduate students (14.6%). Importantly, students who chose to pursue an immunology degree were introduced to immunology at a later academic stage compared to students who are not pursuing an immunology degree. These data suggest that timing may be more influential than the type of exposure in influencing the choice to pursue immunology.