Research Interests Research Articles

Личное дело ученого-востоковеда Абсаттара Багисбаевича Дербисали

The article is dedicated to Absattar Bagisbaevich Derbisali—an outstanding religious figure, Kazakh orientalist, Islamic scholar, Doctor of Philology, professor, whose scientific activity deserves special attention and recognition. As an orientalist, Derbisali specialized in Arabic and Islamic studies and made significant contributions to the study of Arab culture and language, as well as Islamic literature and philosophy. The purpose of the study is to determine the range of interests of a young researcher and the influence of a scientific supervisor in shaping his scientific trajectory.The scientific novelty of the research lies in the disclosure of little-known facts and aspects during his postgraduate studies, as well as in theunderstanding of how exactly during these years his scientific worldview was formed in the context of the sociocultural and political situation of that time. The methodological basis of the study is a historical-biographical approach, since the personal file of A.Derbisali is considered. The article applies the principles of objectivity and historicism, taking into account the range of his scientific interests. The use of a historical-systemic approach allows us to consider the object of study in the context of time and space, identifying internal connections and dependencies in the history of Eastern cultures, including Kazakh and Central Asian history.

Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python.

The herein proposed Python package pflacco provides a set of numerical features to characterize single-objective continuous and constrained optimization problems. Thereby, pflacco addresses two major challenges in the area of optimization. Firstly, it provides the means to develop an understanding of a given problem instance, which is crucial for designing, selecting, or configuring optimization algorithms in general. Secondly, these numerical features can be utilized in the research streams of automated algorithm selection and configuration. While the majority of these landscape features are already available in the R package flacco, our Python implementation offers these tools to an even wider audience and thereby promotes research interests and novel avenues in the area of optimization.

UAV-Enabled Integrated Sensing and Communication: Opportunities and Challenges

Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) has attracted growing research interests in the context of sixth-generation (6G) wireless networks, in which UAVs will be exploited as aerial wireless platforms to provide better coverage and enhanced sensing and communication (S&C) services. However, due to the size, weight, and power (SWAP) constraints of UAVs, their controllable mobility, and the line-of-sight (LoS) air-ground channels, UAVenabled ISAC introduces new opportunities and challenges. This article provides an overview of UAV-enabled ISAC and proposes various solutions for optimizing the S&C performance. In particular, we first introduce UAV-enabled joint S&C and discuss UAV motion control, wireless resource allocation, and interference management for ISAC systems employing single and multiple UAVs. Then, we present two application scenarios for exploiting the synergy between S&C, namely sensing-assisted UAV communication and communication-assisted UAV sensing. Finally, we highlight several interesting research directions to guide and motivate future work.

A Reinforcement Learning Approach for Flexible Job Shop Scheduling Problem With Crane Transportation and Setup Times.

Flexible job shop scheduling problem (FJSP) has attracted research interests as it can significantly improve the energy, cost, and time efficiency of production. As one type of reinforcement learning, deep Q-network (DQN) has been applied to solve numerous realistic optimization problems. In this study, a DQN model is proposed to solve a multiobjective FJSP with crane transportation and setup times (FJSP-CS). Two objectives, i.e., makespan and total energy consumption, are optimized simultaneously based on weighting approach. To better reflect the problem realities, eight different crane transportation stages and three typical machine states including processing, setup, and standby are investigated. Considering the complexity of FJSP-CS, an identification rule is designed to organize the crane transportation in solution decoding. As for the DQN model, 12 state features and seven actions are designed to describe the features in the scheduling process. A novel structure is applied in the DQN topology, saving the calculation resources and improving the performance. In DQN training, double deep Q-network technique and soft target weight update strategy are used. In addition, three reported improvement strategies are adopted to enhance the solution qualities by adjusting scheduling assignments. Extensive computational tests and comparisons demonstrate the effectiveness and advantages of the proposed method in solving FJSP-CS, where the DQN can choose appropriate dispatching rules at various scheduling situations.

A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging.

Magnetic resonance imaging (MRI) is an essential diagnostic tool that suffers from prolonged scan time. To alleviate this limitation, advanced fast MRI technology attracts extensive research interests. Recent deep learning has shown its great potential in improving image quality and reconstruction speed. Faithful coil sensitivity estimation is vital for MRI reconstruction. However, most deep learning methods still rely on pre-estimated sensitivity maps and ignore their inaccuracy, resulting in the significant quality degradation of reconstructed images. In this work, we propose a Joint Deep Sensitivity estimation and Image reconstruction network, called JDSI. During the image artifacts removal, it gradually provides more faithful sensitivity maps with high-frequency information, leading to improved image reconstructions. To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results. Results on in vivo datasets and radiologist reader study demonstrate that, for both calibration-based and calibrationless reconstruction, the proposed JDSI achieves the state-of-the-art performance visually and quantitatively, especially when the acceleration factor is high. Additionally, JDSI owns nice robustness to patients and autocalibration signals.

How journalism adapted the Internet in Germany: Results of six newsroom surveys (1997–2014)

Based on six newsroom surveys, this article analyzes the history of digital German journalism. The surveys cover a period of 17 years (1997–2014). Periodizing the history of digital journalism into three phases, this article considers the interplay between journalism and journalism research. The results show how journalistic digital media define their role in the relationships between old media and the Internet, digital media and other outlets, and digital media and their audiences. Furthermore, the results substantiate how digital editorial staff define their journalistic identities regarding tasks, rules, and skills. During the first period (surveys conducted in 1997 and 2000), the view from old mass media to the Internet dominated, also in scholarship where the mass media paradigm was extended to the Internet. The second period (surveys conducted in 2006 and 2007) was characterized by clarifying the relationships between journalism and newly emerged outlets. These studies focused on how participative formats (such as Wikipedia and blogs) and search engines could be used for journalistic purposes without compromising quality. These new outlets were not regarded then as much of a threat. This attitude did not change during the third period (surveys conducted in 2010 and 2014). In this phase, too, the studies focused on how editorial staff utilized the ever-increasing number of social media. The six surveys’ different research interests reveal that the reviewed journalism research primarily addressed changing demands in journalistic practice. Therefore, exogenous factors (“the sector”) had a greater impact than endogenous factors (the “scholarship”) on research interests.

Understanding unique employability skill sets of autistic individuals: A systematic review

AbstractIn recent years, several publications and media outlets have highlighted how the skills and interests of autistic individuals may benefit organizations. However, there is scant empirical research on the topic. The present study's authors conducted a systematic review to find which potential employability skills, strengths, and interests of autistic individuals available research has highlighted. Data extraction methods identified 51 papers related to skills in this population. The skill sets autistic individuals may possess and the research behind these findings were organized, evaluated, and summarized. Based on these findings, investigators discuss implications for employment counseling and future research.

Benefits of Citizen Science for Libraries

Participating in collaborative scientific research through citizen science, a component of open science, holds significance for both citizen scientists and professional researchers. Yet, the advantages for those orchestrating citizen science initiatives are often overlooked. Organizers encompass a diverse range, including governmental entities, non-governmental organizations, corporations, universities, and institutions like libraries. For libraries, citizen science holds importance by fostering heightened civic and research interests, promoting scientific publishing, and contributing to overall scientific progress. This paper aims to provide a comprehensive understanding of the specific ways in which citizen science can benefit libraries and how libraries can effectively utilize citizen science to achieve their goals. The paper is based on a systematic review of peer-reviewed articles that discuss the direct benefits of citizen science on libraries. A list of the main benefits of citizen science for libraries has been compiled from the literature. Additionally, the reasons why it is crucial for libraries to communicate the benefits of citizen science for their operations have been highlighted, particularly in terms of encouraging other libraries to actively engage in citizen science projects.

Meeting Report for the Phenoscape TraitFest 2023 with Comments on Organising Interdisciplinary Meetings

The Phenoscape project has developed ontology-based tools and a knowledge base that enables the integration and discovery of phenotypes across species from the scientific literature. The Phenoscape TraitFest 2023 event aimed to promote innovative applications that adopt the capabilities supported by the data in the Phenoscape Knowledgebase and its corresponding semantics-enabled tools, algorithms and infrastructure. The event brought together 26 participants, including domain experts in biodiversity informatics, taxonomy and phylogenetics and software developers from various life-sciences programming toolkits and phylogenetic software projects, for an intense four-day collaborative software coding event. The event was designed as a hands-on workshop, based on the Open Space Technology methodology, in which participants self-organise into subgroups to collaboratively plan and work on their shared research interests. We describe how the workshop was organised, the projects developed and outcomes resulting from the workshop, as well as the challenges in bringing together a diverse group of participants to engage productively in a collaborative environment.

Understanding Our Research Interests: A Synthesis of Publications by Members of a Research Institute

This paper contains a research synthesis of publications by members of the Research Institute for Learner Autonomy Education (RILAE) over a 5-year period. We followed a systematic review process to analyse 163 items and identified the most common kinds of publications. In addition, we conducted an inductive thematic analysis of research themes to determine the main areas of research interest for members. The main areas were psychological constructs related to language learning, self-access and advising. The results are useful for RILAE members when proposing new projects, incoming members or visitors interested in seeing at a glance the kind of works that RILAE researchers publish. In addition, conducting regular research syntheses can monitor how research interests shift over time.

Medical Education Electives Can Promote Teaching and Research Interests Among Medical Students.

Although all residents routinely teach medical students, not all residents are involved in teaching or trained in teaching during undergraduate medical school, as accreditation bodies do not mandate the promotion of teaching skills to undergraduate medical students. With relatively inadequate formal training and residents' intrinsic time constraints, tactically incorporating formal medical education elective experiences in medical school curricula is understandable. This study explores if medical education electives at Avalon University School of Medicine (AUSOM) can enhance medical students' interest in teaching and research. The medical education elective at AUSOM was developed to give interested medical students an elective experience. The course modules include accreditation/regulation, curriculum development, learning theories, assessments, and research methodology. Students can choose any one of the modules. We offered the medical education elective to twenty-five students in the year 2021. All of them gave feedback at the end of the elective. The data was analyzed qualitatively through framework analysis, which includes familiarization, generating initial codes, searching for themes, reviewing, and defining and naming themes. Different themes emerged, enhancing the interest in academic medicine, understanding research methodologies, supporting learners, and awareness of learning theories. Doing medical education electives at AUSOM enhanced students' interest in teaching, and students reported that they could understand research methodologies, especially those related to medical education. Medical students should have opportunities for electives in medical education, and more research is required to evaluate the effectiveness of medical education electives across medical schools.

Bibliometric Analysis of the Relationship between Gut Microbiota and Chronic Kidney Disease from 2001–2022

ABSTRACT Background: The gut microbiota is closely associated with chronic kidney disease (CKD) development and prognosis. However, a comprehensive understanding of publication trends and research focuses within this domain remains limited. This study aimed to elucidate the relationship between the gut microbiota and CKD through a bibliometric analysis. Methods: Literature published between 2001 and 2022 on gut microbiota and CKD was obtained from the Web of Science Core Collection database. Bibliometric analysis tools such as VOSviewer and CiteSpace were used to analyze research trends in this area. Results: We extracted 758 articles for further analysis. China ranked first with 240 published documents, followed by the USA with 211. However, the USA ranked first regarding citation frequency with 11,380 citations, while China ranked second. The University of California Irvine and Kidney International emerged as the leading institution and journal, respectively, with the former leading in document production and citations. Although Kidney International featured fewer documents than other journals, it exhibited the highest citation count. The keywords representing the research interests mainly included: (1) gut microbiota influences the development of CKD, (2) application of probiotics in the treatment of CKD, and (3) potential mechanism of gut microbiota in CKD. Conclusion: Our analysis indicates significant advancements in understanding the association between gut microbiota and CKD. It also provides a comprehensive overview of publication trends, research hotspots, and frontiers, which may provide guidelines for future research.

Facile synthesis of nanosized spinel high entropy oxide (FeCoNiCrMn)3O4 for efficient oxygen evolution reaction

The sluggish reaction kinetics of oxygen evolution reaction (OER) and the high price of noble metal catalysts hinder the wide application of water electrolysis for hydrogen generation. High-entropy oxides (HEOs) with multi-components and high entropy stabilized structures have attracted great research interests due to their efficient and durable performance in electrolytic water splitting reactions. However, the development of efficient HEO electrocatalysts are often hindered by the limited surface exposed active sites because high temperature is usually required to form a high entropy stabilized structure. Herein, a flaky high-entropy oxide with a spinel structure, (FeCoNiCrMn)3O4, was synthesized by using the sacrificial layered carbon template in situ prepared by the volatile reaction between ammonium sulfate and molten glucose. High-resolution TEM results show the as-prepared (FeCoNiCrMn)3O4 flakes are composed of nanosized HEO particles. The nanosized (FeCoNiCrMn)3O4 HEO electrocatalysts exhibit excellent OER activity, with an overpotential of 239 mV at 10 mA/cm2 and a Tafel slope of 52.4 mV/dec. The electrocatalyst has excellent stability. Even at a high current density of 100 mA/cm2, the activity remains unchanged during the stability test for 24 h. The results here shed a new light in the design and fabrication of highly efficient HEO electrocatalysts.

Tunable wrinkle patterns in Moiré pattern of interlayer-bonding strained bilayer graphene

Moiré patterns are important structures in two-dimensional (2D) materials, showcasing unique electronic correlated states, which have garnered considerable research interests in recent years. However, current researches on moiré patterns primarily focus on honeycomb patterns induced by lattice mismatches at 2D material hetero-interfaces. In this work, we have demonstrated the ability to generate diverse and tunable wrinkle patterns in interlayer bonding strained bilayer graphene (IB-SBG) through a “stretch-crosslink-release” strategy, facilitated by the interlayer bonding that provides effective in-plane load transfer and interlayer constraint. Three energetically favorable wrinkle patterns are identified, including herringbone, irregular hexagon and honeycomb patterns. In general, wrinkle patterns are determined by two parameters, i.e., pre-strain and the portion of interface with interlayer bonding, and the phase diagram of wrinkle patterns corresponding to the two parameters is provided. Furthermore, strain engineering can be employed in IB-SBG to generate a series of new patterns by precisely adjusting the biaxial strain applied to IB-SBG. The present strategy to achieve tunable wrinkle patterns holds the potential to open new avenues for novel electronic applications based on bilayer graphene systems.

Cluster Preface: Biomimetic Synthesis

Jun Deng (left) received his B.Sc. from Lanzhou University and his Ph.D. from the Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences (2014) with Professor Ang Li. After a short period of postdoctoral research in the lab of Professor Andrew Myers at Harvard University (2014–2016), he was appointed a Pioneer ‘Hundred Talents Program’ Researcher at Kunming Institute of Botany, Chinese Academy of Sciences in 2017. In December 2020, he moved to Nankai University where he is a professor at the College of Chemistry. He received the Thieme Journals Award (2020). His research focuses on natural product total synthesis and medicinal chemistry. Yefeng Tang (right) earned his B.S. from Lanzhou University (1995–1999) and his M.S. from the Institute of Materia Medica, Chinese Academy of Medical Science (1999–2002). From 2003 to 2006, he pursued his Ph.D. under the guidance of Professor Zhen Yang and Professor Jiahua Chen at Peking University. Subsequently, he conducted postdoctoral research with Prof. K. C. Nicolaou at The Scripps Research Institute between 2006 and 2009. In 2010, he commenced his independent career at the School of Pharmaceutical Sciences, Tsinghua University, where he is now a tenured professor. His primary research interests encompass the total synthesis of natural products, the development of synthetic methodologies, and the discovery of antiviral and anti-aging drugs.

Crystalline and amorphous Fe2O3 nanocubes grown on electrospun carbon nanofibers for lithium-ion batteries and lithium-sulfur batteries: A comparative study

Amorphous transition metal oxides have recently received particular research interests in electrochemical energy storage. However, there is still a lack of direct comparisons between amorphous materials and their crystalline counterparts. Here, we demonstrate the rational synthesis of crystalline and amorphous Fe2O3 nanocubes uniformly grown on carbon nanofibers (denoted as CNFs@C-Fe2O3 and CNFs@A-Fe2O3, respectively) for lithium-ion batteries (LIBs) and lithium-sulfur batteries (LSBs). In such a structure, the Fe2O3 nanocubes possess strong interfacial bonding with CNFs, which can ensure rapid electron transportation. Besides, these Fe2O3 nanocubes are highly porous, which can effectively alleviate the volume change, enlarge the surface area, increase active sites and facilitate ion diffusion. When employed as freestanding anode for LIBs, the CNFs@C-Fe2O3 electrode delivers much improved lithium ion storage performance compared to that of CNFs@A-Fe2O3. When evaluated as interlayers for LSBs, instead, the batteries with CNFs@A-Fe2O3 exhibit better rate performance cycling stability than that of with CNFs@C-Fe2O3. Moreover, theoretical calculations elucidate the amorphous Fe2O3 has stronger adsorption ability toward the soluble lithium polysulfides. This work would provide new insights into the reasonably development of crystalline and amorphous transition metal oxides toward electrochemical energy storage.

Unveiling Insights: A Bibliometric Analysis of Artificial Intelligence in Teaching

The penetration of intelligent applications in education is rapidly increasing, posing a number of questions of a different nature to the educational community. This paper is coming to analyze and outline the influence of artificial intelligence (AI) on teaching practice which is an essential problem considering its growing utilization and pervasion on a global scale. A bibliometric approach is applied to outdraw the “big picture” considering gathered bibliographic data from scientific databases Scopus and Web of Science. Data on relevant publications matching the query “artificial intelligence and teaching” over the past 5 years have been researched and processed through Biblioshiny in R environment in order to establish a descriptive structure of the scientific production, to determine the impact of scientific publications, to trace collaboration patterns and to identify key research areas and emerging trends. The results point out the growth in scientific production lately that is an indicator of increased interest in the investigated topic by researchers who mainly work in collaborative teams as some of them are from different countries and institutions. The identified key research areas include techniques used in educational applications, such as artificial intelligence, machine learning, and deep learning. Additionally, there is a focus on applicable technologies like ChatGPT, learning analytics, and virtual reality. The research also explores the context of application for these techniques and technologies in various educational settings, including teaching, higher education, active learning, e-learning, and online learning. Based on our findings, the trending research topics can be encapsulated by terms such as ChatGPT, chatbots, AI, generative AI, machine learning, emotion recognition, large language models, convolutional neural networks, and decision theory. These findings offer valuable insights into the current landscape of research interests in the field.

Shifting professional and personal identities of the cisgender scholar doing LGBT research

Background: Scholars researching the issues of lesbian, gay, bisexual, transgender and other sexual and gender minorities (LGBT) are being denounced as performers of ‘dirty’ work and promotors of ‘immorality’. Such scholars face obstacles in the workplace, such as difficulties in obtaining ethical approval, scarcity of funding, bullying, denial of promotion and unacknowledged scholarship, among other struggles.Objectives: This article intends to highlight the unintended, unanticipated and often overlooked impact that certain LGBT research interests have on the professional and personal identities of the scholar.Method: This exploratory qualitative study uses a reflexive dialogical single case study approach to understand how Katlego (a pseudonym), a cisgender heterosexual researcher, is affected by his interest in LGBT studies. Continuous supervision notes that enable thoughtful reflexive practice in qualitative research shaped the basis of this article.Results: This article shows how Katlego’s LGBT research interest resulted in him being policed, his professional intentions questioned, his bodily expression scrutinised and personal conflict being blamed on his allegedly ‘disgraceful’ research interest. Furthermore, it demonstrates how the conflation of Katlego’s interest in LGBT research with pervasive negative assumptions of non-heteronormative sexual orientations, gender identities and expressions has resulted in a shift in his professional and personal identities.Conclusion: This article calls for awareness of the issues faced by all researchers interested in LGBT research and support strategies for navigating the professional identity, workplace environment and social ecologies within a frowned-upon research field. Failure to do so could compromise their career trajectory, well-being and safety in compulsory heterosexual environments.Contribution: This article illustrated how gender regimes position the researcher as an active creator of knowledge; therefore, the research process experiences can neither be made invisible nor neutralised. Researchers must overtly consider and prepare for situational and unanticipated ethical issues.

Independent and dynamic manipulation of surface waves radiation for quadruplex polarization channels enabled by programmable coding metasurface

Abstract Flexible manipulation of surface waves (SWs) radiation has been continuously intriguing enormous interests of researchers due to its promising application prospects, and metasurfaces exhibit unparalleled capability to efficiently control SWs radiation. However, existing schemes still suffer from the bottlenecks of single radiation channel and immutable radiation pattern, which are difficult to satisfy the requirements of high-integration intelligent metadevices. Herein, an ingenious strategy of the SWs radiation metadevice is proposed to independently and dynamically manipulate SWs directional radiation in four polarization channels. The waveguide port and the guided wave structure are designed to excite and propagate the desired SWs, and the programmable coding metasurface can independently convert SWs into x-polarized radiation waves, y-polarized radiation waves, left-handed circular polarized radiation waves and right-handed circular polarized radiation waves and dynamically control the corresponding radiation angles by adjusting the ON/OFF states of two positive-intrinsic-negative diodes in each spin-decoupled meta-atom. Numerous simulation and experimental results of the proof-of-concept prototype are in good agreement with the theoretical predictions, which verify the feasibility of our proposed methodology. The innovative design of four-channel SWs radiation metadevice with high radiation efficiency and broad radiation bandwidth offers an excellent platform for flexibly manipulating SWs radiation, and possesses tremendous potential in engineering application.

Research on text classification based on neural networks

With the rapid development of information technology, the processing of massive text data has become increasingly important. As a common computer information processing task, text classification has attracted a wide range of research interests. This paper aims to explore the text classification method based on neural network and analyze the key technologies. In order to solve the problem of text time series data classification, this paper uses the text time series data of occupancy detection and applies these neural network models in deep learning, including recurrent neural network, long short-term memory and gated recurrent unit, and trains the neural network through supervised learning. Inputting the room attribute data to these trained neural network models and judging the occupancy of the room. At the same time, observing the experimental results of these neural network models, including training loss, test loss and accuracy, to further study the performance of neural network in processing text time series data classification. This papers experiment aims to evaluate the performance of neural network in text classification and makes a detailed analysis through the experimental results. The goal of the research is to find an effective solution based on neural networks for the classification of text sequence data. Through the analysis of the experimental results, it can be concluded that the method based on neural network is feasible and effective in text sequence data classification. These analysis results will help to further promote the development of text classification technology and provide guidance and reference for practical application.