Role In Scientific Research Research Articles

Why Early Career Researchers Escape the Ivory Tower: The Role of Environmental Perception in Career Choices

As early career researchers, postdocs play an irreplaceable and crucial role in scientific research, especially in highly competitive fields. Given the importance of the postdoc community, it is essential to explore their engagement in the academic labor market. Employment in academic departments should be the ideal career choice for postdocs, but this is not always the case. In recent years, an increasing number of postdocs have chosen to leave the ivory tower, which is often the result of a dynamic integration of the work environment and individual cognition. This study is based on the public data from the “Nature 2023 Postdoc Survey” and empirically analyzes the predictive relationship between different dimensions of environmental perception and postdoc academic career choices, as well as whether there are differences in predictive relationships across various fields. Difference analysis suggested that males and those working in their native country tend to have greater satisfaction in their environment perception. Correlation analysis revealed that postdocs’ environmental perception is significantly and positively correlated with academic career choices. Regression results indicated that institutional environment, organizational environment, living environment, and support environment can all predict postdoc academic career choices, with significant disciplinary differences in these predictive effects. It is recommended that mental health, salary and benefits, job security, and professional training be focused on to improve the working environment for early career researchers. Additionally, it is necessary to increase inclusive support for vulnerable postdoc groups and enhance their expectations for academic careers.

Review of hTERT-Immortalized Cells: How to Assess Immortality and Confirm Identity

Cell immortalization has an important role in scientific research, as well as increasing significance in the context of cell therapy and biotechnology. Over the years, many immortalized cell lines have been produced using human telomerase reverse transcriptase (hTERT) alone or in a combination with viral oncogenes. Different hTERT-immortalized cells are commercially available, and numerous papers about obtaining immortalized cell lines have also been published. However, no specific list of characteristics that need to be checked to confirm successful immortalization exists. Most researchers evaluate only a few parameters, while different articles contain various opinions on the assessment of these characteristics. Results also vary significantly between different cell types, which have their own traits depending on their origin and functions. In the current paper, we raise these questions and discuss controversial issues concerning currently available testing methods for immortalization evaluation and the value and the limitations of the approaches. In addition, we propose a protocol for evaluation of hTERT immortalization success consisting of the following important steps: the assessment of the proliferation rate and dividing capacity, cell morphology, phenotype, karyotype stability, telomerase activity, the expression of cell-specific markers, and tumorigenicity. To our opinion, the hTERT expression level, telomere length, and senescence-associated β-galactosidase staining are controversial with regard to the implemented methods, so these parameters may be optional. For all the evaluation steps, we recommend to pay attention to the necessity of comparing the traits of the obtained immortalized and parent cells.

Ways to improve the system of prevention and treatment of environmental diseases

International experience, as well as many Russian research works, prove the necessity to concentrate efforts of healthcare professionals and researchers on development of healthcare technologies for diagnosis, prevention and treatment (including health resort treatment and rehabilitation) of so-called environmental diseases caused by unhealthy environments and adverse environmental factors (physical, chemical, biological, social ones). This paper proposes the integration of research areas in environmental medicine into profiles of various research specialties (Occupational Medicine, Ecology, etc.). It is also proposed to transform the healthcare specialty of Doctor of Occupational Medicine into Doctor of Environmental Medicine. Finding ways to improve the system of prevention and treatment of environmental diseases, discussing the issues of further integration of environmental medicine, public health, ecology, occupational medicine, restorative medicine and other biomedical, preventive and clinical disciplines may improve the diagnosis and treatment of human diseases caused by adverse effects of environmental factors, as well as contribute to the development of preventive strategies. The methods of epidemiologic (including prenosologic) diagnostics based on the assessment of functional reserves of the body should play a key role in scientific research with the implementation of its results in the practice of health professionals of various profiles, including environmental occupational pathologists, in order to improve clinical diagnostics and develop personalized treatment and rehabilitation programs for patients exposed to adverse environmental factors.

Different kinds of data: samples and the relational framework

This paper proposes an original definition of samples as a kind of data within the relational framework of data. The distinction between scientific objects (e.g., samples, data, models) often needs to be clarified in the philosophy of science to understand their role in the scientific inquiry. The relational framework places data at the forefront of knowledge construction. Their epistemic status depends on their evaluation as potential evidence in a research situation and their ability to circulate among researchers. While samples are significant in data-generating science, their role has been underexplored in the philosophy of data literature. I draw on a case study from data-centric microbiology, viz. amplicon sequencing, to introduce specifications of the relational framework. These specifications capture the distinctive epistemic role of samples, allowing the discussion of their significance in the inquiry process. I argue that samples are necessarily transformed to be considered as evidence, portable in the limits of a situation, and they act as world anchors for claims about a phenomenon. I compare these specifications with other data and evidence frameworks and suggest they are compatible. The paper concludes by considering the extension of these criteria in the context of biobanking. The specifications proposed here help analyze other life sciences cases and deepen our understanding of samples and their epistemological role in scientific research.

Begum Rokeya’s Sultana’s Dream: A Marxist Reading

Begum Rokeya’s Sultana’s dream is considered worldwide as a feminist utopian story. However, Sultana’s Dream does not talk aboutwomen’s empowerment and women’s issues only. The story talks about a complete social transformation. It visualizes an ideal country and social system free of hunger, theft, and untimely death. This is a country where people live in harmony with nature. A country whose economic and political affairs are based, not on profit mongering, but on ethical standards. A country which is uncompromising to the imperialists and revolutionary in ideals. Most importantly, this is a country where women take active roles in scientific research, economical activities, country planning and political decisions. Marxism is a philosophy which calls for the transformation of the worn-out society and women’s active participation in all the state’s affairs. The paper aims to show that many Marxist thoughts have been implemented in Begum Rokeya’s imaginary country, ‘Ladyland,’ showcasing a complete social transformation based on perceived Marxist ideals that emphasize equality, justice, and the well-being of all members of society. The article presents an analysis of the Marxist elements in the text through a close reading and comparison to Marxist theory. The article also explores the historical and social context of the author and the text to gain a deeper understanding of the Marxist elements present in the work. Through this analysis, the article presents Begum Rokeya as a Marxist feminist.

A high-precision and near-zero residue laser-controlled gel propellant

Micro-nano satellites, with their advantages of small size, low power consumption, short development cycles, and capability for formation flying and networking, play a significant role in scientific research, national defense, and commercial applications. However, currently developed micro-nano satellites globally possess very limited maneuverability and lack effective attitude and orbit propulsion control. Solid propellants can offer some advantages such as high energy density and low cost, and they are extensively used in micro-and nano satellite propulsion systems. However, most of these propellants exhibit poor controllability and produce massive amounts of residual by-products. Laser-controlled gel propellants have been confirmed to be the most effective solution to address these issues in this study. The present study exhibits the preparation of ADN-based laser-controlled gel propellants using the freeze–thaw method and their controllable combustion characteristics under laser irradiation. The results show that the ignition delay time of gel propellants decreases from 93.0 ms to 34.6 ms, the extinction delay time is stable at around 2 ms, and the average burning rate increases from 0.6 mm/s to 1.5 mm/s under continuous laser irradiation with the power density ranging from 0.6 to 1.3 W/mm2. The average utilization rate of gel propellant is as high as 99.2 %. Upon testing, the residual solid matter was identified as unreacted propellant that had not been irradiated by the laser. Laser-controlled gel propellants represent a new type of energetic material that can be modulated to control its ignition, extinction, and burning rate. These capabilities overcome the challenges associated with the inability of traditional chemical propellants to extinguish and restart, thereby fulfilling the propulsion system requirements of micro-nano satellites.

Comparative analysis of electrophoresis and interferometric optical detection method for molecular weight determination of proteins

Analytical detection methods play a pivotal role in scientific research, enabling the identification and quantification of specific analytes in various disciplines. This scientific report aims to compare two very different methodologies for determining the Molecular Mass (MM, also known as Molecular Weight, MW) of proteins: electrophoresis gel and the Interferometric Optical Detection Method (IODM). For this purpose, several proteins with different MM were selected.The electrophoresis technique was employed to validate the structure and MM of different parts or fragments of the Matrix Metallopeptidase 9 antibody (anti-MMP9), antibody against S100 calcium binding protein A6 (anti-S100A6) and Cystatin S4 antibody (anti-CST4) by examining the presence of bands with expected sizes. The IODM was applied to study the above-mentioned proteins (part of the antibodies) together with the protein G, as a reference to correlate the MM and protein sizes with the measured signal.We report the evidence of IODM as a competitive analytical approach for the determination of the MM of proteins for the first time. This innovative method allows for accurate MM determination using minimal sample volumes and concentrations, employing a simple experimental procedure that eliminates the requirement for protein denaturation.

A moderate-Ti lunar mare soil simulant: IGG-01

Lunar soil simulants play a crucial role in scientific research, payload tests and in-situ resource utilization (ISRU) experiments. However, the currently available lunar soil simulants are either low in TiO2 content (≤3 wt%) or high in TiO2 content (≥6.7 wt%), despite orbital observations indicating lunar soils with moderate TiO2 content being widely distributed on the lunar surface. In December 2020, China's Chang'e−5 (CE-5) mission successfully returned a new mare soil sample characterized by high FeO content (22.5 wt%) and moderate TiO2 content (5.5 wt%), providing an opportunity to develop moderate-TiO2 lunar soil simulants. This study presents a newly developed simulant of the CE-5 lunar soil named IGG-01, which closely replicates the major physical and chemical properties of the CE-5 lunar soil. The median particle size of IGG-01 is 68.69 ± 4.44 μm, and its estimated specific surface area is 0.34 m2/g. It contains 17.6 wt% FeO and 4.8 wt% TiO2. It consists of pyroxene (49.3 wt%), plagioclase (40.9 wt%), ilmenite (6.1 wt%), and olivine (3.7 wt%), with specific gravity and bulk density are 3.33 g/cm³ and 1.86 g/cm³, respectively. The friction angle is 41.8°, and the cohesion is 19.2 kPa. It exhibits strong and weak reflectance absorption features in the 1 and 2 μm band centers. The physical properties of IGG-01 are also comparable to those of Apollo samples and typical lunar mare soil simulants. The unique chemical composition of IGG-01 makes it suitable for various scientific and engineering experiments, including but not limited to the study of dusty plasma migration, water formation via solar wind injection, oxygen preparation via hydrogen reduction, and tests involving the movement of rovers and excavation equipment.

Design and Application of an Onboard Particle Identification Platform Based on Convolutional Neural Networks

Space radiation particle detection plays a crucial role in scientific research and engineering practice, especially in particle species identification. Currently, commonly used in-orbit particle identification techniques include telescope methods, electrostatic analysis time of flight (ESA × TOF), time-of-flight energy (TOF × E), and pulse shape discrimination (PSD). However, these methods usually fail to utilize the full waveform information containing rich features, and their particle identification results may be affected by the random rise and fall of particle deposition and noise interference. In this study, a low-latency and lightweight onboard FPGA real-time particle identification platform based on full waveform information was developed utilizing the superior target classification, robustness, and generalization capabilities of convolutional neural networks (CNNs). The platform constructs diversified input datasets based on the physical features of waveforms and uses Optuna and Pytorch software architectures for model training. The hardware platform is responsible for the real-time inference of waveform data and the dynamic expansion of the dataset. The platform was utilized for deep learning training and the testing of the historical waveform data of neutron and gamma rays, and the inference time of a single waveform takes 4.9 microseconds, with an accuracy rate of over 97%. The classification expectation FOM (figure-of-merit) value of this CNN model is 133, which is better than the traditional pulse shape discrimination (PSD) algorithm’s FOM value of 0.8. The development of this platform not only improves the accuracy and efficiency of space particle discrimination but also provides an advanced tool for future space environment monitoring, which is of great value for engineering applications.

Scientific honesty and activating its role in scientific research

Scientific plagiarism is considered one of the most common pitfalls that researchers and students alike fall into, and this exposes them to falling into scientific errors, whether spontaneously or intentionally. Despite many legal procedures and technical measures approved by laws and university charters, they have not been able to eliminate this phenomenon completely.

Two types of anti-noise integral enhanced recurrent neural networks for solving time-varying complex quadratic programming

The time-varying complex quadratic programming problem plays an important role in scientific research and engineering applications, and has received extensive attention. In this paper, by solving the problem in different number domains, two types of novel anti-noise integral enhanced recurrent neural networks (IERNNs) are proposed and analyzed. Specifically, the IERNN-A model solves the problems in the complex number domain, while the IERNN-B model solves the problems by converting the problems from the complex number domain into the real number domain. Both the IERNN-A and IERNN-B have the same design paradigm, i.e., an integral-type error function is first defined to obtain good anti-noise capability, and then substitute it into a differential-driven design formula to ensure convergence. Theory analysis and simulation results verify the global convergence and robustness of the proposed IERNNs. Furthermore, if a linear activation is used, the exponential convergence rate is obtained. Meanwhile, when time-varying bounded noise exists, the IERNNs are input-to-state stable. In addition, compared with other art-of-the-state methods, the proposed methods have faster convergence speed, stronger noise-suppression ability, and no overshoot, which are verified by the simulation and application of manipulator trajectory tracking.

A cyclical route linking fundamental mechanism and AI algorithm: An example from tuning Poisson's ratio in amorphous networks

“AI for science” is widely recognized as a future trend in the development of scientific research. Currently, although machine learning algorithms have played a crucial role in scientific research with numerous successful cases, relatively few instances exist where AI assists researchers in uncovering the underlying physical mechanisms behind a certain phenomenon and subsequently using that mechanism to improve machine learning algorithms' efficiency. This article uses the investigation into the relationship between extreme Poisson's ratio values and the structure of amorphous networks as a case study to illustrate how machine learning methods can assist in revealing underlying physical mechanisms. Upon recognizing that the Poisson's ratio relies on the low-frequency vibrational modes of the dynamical matrix, we can then employ a convolutional neural network, trained on the dynamical matrix instead of traditional image recognition, to predict the Poisson's ratio of amorphous networks with a much higher efficiency. Through this example, we aim to showcase the role that artificial intelligence can play in revealing fundamental physical mechanisms, which subsequently improves the machine learning algorithms significantly.

Active Learning for Data Quality Control: A Survey

Data quality plays a vital role in scientific research and decision-making across industries. Thus, it is crucial to incorporate the data quality control (DQC) process, which comprises various actions and operations to detect and correct data errors. The increasing adoption of machine learning (ML) techniques in different domains has raised concerns about data quality in the ML field. Conversely, ML’s capability to uncover complex patterns makes it suitable for addressing challenges involved in the DQC process. However, supervised learning methods demand abundant labeled data, while unsupervised learning methods heavily rely on the underlying distribution of the data. Active learning (AL) provides a promising solution by proactively selecting data points for inspection, thus reducing the burden of data labeling for domain experts. Therefore, this survey focuses on applying AL to DQC. Starting with a review of common data quality issues and solutions in the ML field, we aim to enhance the understanding of current quality assessment methods. We then present two scenarios to illustrate the adoption of AL into the DQC systems on the anomaly detection task, including pool-based and stream-based approaches. Finally, we provide the remaining challenges and research opportunities in this field.

Spatiotemporal Fusion Prediction of Sea Surface Temperatures Based on the Graph Convolutional Neural and Long Short-Term Memory Networks

Sea surface temperature (SST) prediction plays an important role in scientific research, environmental protection, and other marine-related fields. However, most of the current prediction methods are not effective enough to utilize the spatial correlation of SSTs, which limits the improvement of SST prediction accuracy. Therefore, this paper first explores spatial correlation mining methods, including regular boundary division, convolutional sliding translation, and clustering neural networks. Then, spatial correlation mining through a graph convolutional neural network (GCN) is proposed, which solves the problem of the dependency on regular Euclidian space and the lack of spatial correlation around the boundary of groups for the above three methods. Based on that, this paper combines the spatial advantages of the GCN and the temporal advantages of the long short-term memory network (LSTM) and proposes a spatiotemporal fusion model (GCN-LSTM) for SST prediction. The proposed model can capture SST features in both the spatial and temporal dimensions more effectively and complete the SST prediction by spatiotemporal fusion. The experiments prove that the proposed model greatly improves the prediction accuracy and is an effective model for SST prediction.

Particle accelerator power system early fault diagnosis based on deep learning and multi-sensor feature fusion

Particle accelerators play a crucial role in scientific research and industrial applications, and enhancing their reliability, ensuring stable operation, and reducing downtime caused by faults are essential for achieving research goals. This paper introduces a novel particle accelerator fault diagnosis method based on deep learning and multi-sensor feature fusion. The approach employs one-dimensional convolution to extract signals from multiple sensors and achieves comprehensive feature fusion of multi-sensor data, effectively overcoming the limitations of individual sensors. It combines 1d convolutional neural networks (1D-CNN) and long short-term memory networks (LSTM) to enhance spatiotemporal feature extraction. This approach efficiently extracts features from multiple sensors while concurrently reducing data length and training time. The study uses real signals from particle accelerators and aims to achieve early detection of faults by identifying abnormal signals preceding accelerator malfunctions. Comparative analysis with other machine learning models and the use of multiple evaluation metrics validate the effectiveness and generalizability of this method. The research provides a better performance fault diagnosis model with significant implications for reducing downtime, troubleshooting faults, and improving the reliability of particle accelerators.

Academic freedom restrictions in the practice of the European Court of Human Rights

Academic freedom restrictions in the practice of the European Court of Human Rights

A bibliometric analysis of the literature on goat breeding

Background Bibliometric analysis plays an essential role in scientific research. However, some research areas still have several gaps. This is also the case in the animal sciences. Therefore, the status of current studies can be analyzed to provide guidance for future research through scientific studies that will be conducted. The aim of this study was to perform a bibliometric analysis of goat breeding to evaluate current trends, gaps in knowledge, and future perspectives. Methods A large dataset was extracted from the Web of Science (WoS), a popular database for scientific studies. According to the WoS, we used 1000 scientific papers (1991–2023) that contained articles, book chapters, data papers, proceedings papers, meeting abstracts, and notes. All bibliometric analyses were performed using the bibliometrix package. Results and Conclusions In 2023, there were 34 studies available. Average citations per year showed that the highest average citation count occurred in 2006 (31.35). In total, 265 scientific journals in the WoS database have published goat breeding studies. Small Ruminant Research led to the remaining 145 articles. In conclusion, scientific studies on goat breeding have become popular in recent years. The authors may gravitate towards popular journals in the future.

Preparation of Two-Dimensional Polyaniline Sheets with High Crystallinity via Surfactant Interface Self-Assembly and Their Encryption Application.

In recent years in the field of traditional materials, traditional polyaniline has faced a number of scientific problems such as an irregular morphology, high difficulty in crystallization, and difficulty in forming an ordered structure compared to the corresponding inorganic materials. In response to these urgent issues, this study determines how to prepare a highly ordered structure in polyaniline formed at the gas-liquid interface. By dynamically arranging aniline monomers into a highly ordered structure with sodium dodecyl benzene sulfonate (SDBS) surfactant, aniline polymerization is initiated at the gas-liquid interface, resulting in two-dimensional polyaniline crystal sheets with a highly ordered structure. By elucidating the microstructure, crystallization process, and molecular structure of the two-dimensional polyaniline crystal sheets, the practical application of polyaniline as an encryption label in the field of electrochromism has been further expanded, thus making polyaniline widely used in the field of information encryption. Therefore, the synthesis of flaky polyaniline crystal sheets has a role in scientific research and practical application, which will arouse the interest and exploration of researchers.

Graph learning for particle accelerator operations.

Particle accelerators play a crucial role in scientific research, enabling the study of fundamental physics and materials science, as well as having important medical applications. This study proposes a novel graph learning approach to classify operational beamline configurations as good or bad. By considering the relationships among beamline elements, we transform data from components into a heterogeneous graph. We propose to learn from historical, unlabeled data via our self-supervised training strategy along with fine-tuning on a smaller, labeled dataset. Additionally, we extract a low-dimensional representation from each configuration that can be visualized in two dimensions. Leveraging our ability for classification, we map out regions of the low-dimensional latent space characterized by good and bad configurations, which in turn can provide valuable feedback to operators. This research demonstrates a paradigm shift in how complex, many-dimensional data from beamlines can be analyzed and leveraged for accelerator operations.

The role of artificial intelligence in training ENT residents: a survey on ChatGPT, a new method of investigation.

The primary focus of this study was to analyze the adoption of ChatGPT among Ear, Nose, and Throat (ENT) trainees, encompassing its role in scientific research and personal study. We examined in which year ENT trainees become involved in clinical research and how many scientific investigations they have been engaged in. An online survey was distributed to ENT residents employed in Italian University Hospitals. Out of 609 Italian ENT trainees, 181 (29.7%) responded to the survey. Among these, 67.4% were familiar with ChatGPT, and 18.9% of them used artificial intelligence as a tool for research and study. In all, 32.6% were not familiar with ChatGPT and its functions. Within our sample, there was an increasing trend of participation by ENT trainees in scientific publications throughout their training. ChatGPT remains relatively unfamiliar and underutilised in Italy, even though it could be a valuable and efficient tool for ENT trainees, providing quick access for study and research through both personal computers and smartphones.