Field Of Science Research Articles

Accurate Flow Decomposition via Robust Integer Linear Programming.

Minimum flow decomposition (MFD) is a common problem across various fields of Computer Science, where a flow is decomposed into a minimum set of weighted paths. However, in Bioinformatics applications, such as RNA transcript or quasi-species assembly, the flow is erroneous since it is obtained from noisy read coverages. Typical generalizations of the MFD problem to handle errors are based on least-squares formulations or modelling the erroneous flow values as ranges. All of these are thus focused on error handling at the level of individual edges. In this paper, we interpret the flow decomposition problem as a robust optimization problem and lift error-handling from individual edges to solution paths. As such, we introduce a new minimum path-error flow decomposition problem, for which we give an Integer Linear Programming formulation. Our experimental results reveal that our formulation can account for errors significantly better, by lowering the inaccuracy rate by 30-50% compared to previous error-handling formulations, with computational requirements that remain practical.

Impact of Robotic Surgery Objective Performance Indicators: A Systematic Review.

Objective intra-operative performance metrics are an new resource for the field of surgical data science. In this study, we conducted a comprehensive review to analyze the existing literature on robotic surgical objective performance indicators (OPIs). OPIs have been used to assess surgical skill and provide automated, objective feedback in multiple settings. Clinically, OPIs have been utilized to predict post-operative patient outcomes and measure intra-operative efficiency. OPIs have shown promise in preliminary surgical education and patient outcomes related studies, and further multi-center collaborative research will be imperative to validate the use of OPIs in the field of surgery.

University Rankings in Sport Science: A True Reflection of Excellence?

University rankings often serve as benchmarks for excellence in academic evaluation. For example, ShanghaiRanking data from 2016 to 2023 for the Global Ranking of Sport Science Schools and Departments reveal consistent dominance by Australia (23.1%), Canada (18.0%), and the United States (12.0%), collectively counting over half of the top 50 universities worldwide. Nevertheless, there is uncertainty about how the methodology behind these rankings shapes a reality as much as it reflects one. Our intention is to discuss the complexity of university rankings, using ShanghaiRanking as an example, to highlight how these rankings reflect academic excellence within the field of sport science. Current Evidence: When ranking universities in sport science, several aspects of academic excellence could be considered in addition to research metrics currently considered in ShanghaiRanking (publication, citation, citations per publication, top 25% journal publications, and internationally collaborated publications). These aspects may include (1)teaching quality, (2)practical training, (3)industry links and employability, (4)support services, (5)facilities and equipment, (6)international network, (7)community engagement, (8)sustainability and ethical practices, and (9)interdisciplinary approach. Altogether, they could provide a more comprehensive view of the quality and effectiveness of a sport-science program. The ranking of sport-science institutions poses a complex challenge due to diverse factors influencing academic excellence. Engaging in a broader dialogue and refining internationally relevant evaluation methodologies are crucial. These steps enable comparability between countries and provide a holistic understanding of the multidimensional nature of academic excellence in sport science.

Journal Metrics of the Journal of Craniofacial Surgery: An Analysis Based on the Journal Citation Report 2024.

This study aims the analysis of the journal metrics of Journal of Craniofacial Surgery based on 2024 Journal Citation Reports (JCR) data. In the Journal Citation Reports 2024, shared by Clarivate Analytics on June 20, 2024, the journal metrics of the Journal of Craniofacial Surgery were examined in detail. According to the reports published by Clarivate Analytics in 2024, the journal's impact factor in 2023 was determined as 1.0 and the impact factor excluding self-citations was calculated as 0.8. These values show that the impact factor of the journal has increased in recent years. Also, it is seen that the Journal of Craniofacial Surgery, which has been in the fourth quartile (Q4) among the journals in the "Surgery" category for the last 10 years, has risen to the third quartile (Q3) this year. The increase in the impact factor and ranking of the Journal of Craniofacial Surgery shows that the journal has strengthened its position in the scientific field and is moving toward higher levels. However, it is suggested that other metrics should be taken into consideration in addition to the impact factor.

Diffusion behavior and mechanical properties of the Mg-Er binary system

The Mg-Er binary system, as a new lightweight alloy material system with important research value and prospects for development and application, has attracted much attention from researchers in the field of materials science. Studies have shown that the addition of the rare earth element Er can improve the mechanical strength and heat resistance of magnesium alloys. However, the specific mechanism of the interaction between Mg and Er and its effect on the mechanical properties still need to be studied in depth. A combination of microscopic morphology characterization and nanoindentation experiments were employed to investigate the binary diffusion behavior and mechanical properties of the Mg-Er system between 748 K and 823 K in this research. The results indicate that the growth constant of the diffusion layer increases with temperature, with Mg24Er5 exhibiting a higher growth constant than the sum of Mg2Er and MgEr. Notably, the Mg2Er phase has the lowest growth activation energy (127.81 kJ/mol), which suggests that it is formed first during the initial growth stage due to its relatively low energy barrier. Moreover, the interdiffusion coefficient of Mg24Er5 was found to be the highest. By calculating diffusion activation energy, this sequence of formation (Mg2Er→Mg24Er5→MgEr) is in agreement with the trend of growth activation energy. Additionally, nanoindentation experiments and first-principles calculations were utilized to derive the mechanical properties of the Mg-Er binary system. The investigations unveil that Mg2Er boasts the highest hardness and Young’s modulus. These findings have significant implications for understanding the correlation between structure and properties for the Mg-Er alloy system.

Development of Mg2TiO4:Mn4+ phosphors for enhanced red LED emission and forensic fingerprint analysis

The advancement in the development of inorganic phosphors marks a significant milestone in the fields of LED technology and forensic science. Herein, Mg2TiO4:Mn4+ (MTO:Mn4+) novel red-emitting phosphors are synthesized through a solvothermal method, which demonstrates a promising approach for enhancing latent fingerprint detection capabilities and improving the performance of red LEDs. The confirmation of the cubic structure post-annealing and the nano-nature as revealed by TEM analysis underpin the MTO:Mn4+ phosphors suitability for these applications. The broad excitation spectra and the sharp red emission at 659 nm, coupled with the optimal doping concentration, showcase the MTO:Mn4+ phosphor's efficient luminescence properties. Moreover, the calculated critical distance between Mn4+ ions (32.906 Å) elucidates the concentration quenching mechanism, which is pivotal for optimizing the performance. The high purity of the emitted red light (97.2 %) and the precise CIE coordinates (0.5969, 0.2926) of the MTO:Mn4+ phosphor suggest its potential for producing high-quality red LEDs. Additionally, the capability of MTO:Mn4+ phosphors to reveal detailed and high-resolution latent fingerprints offers a more reliable and efficient method for processing and analyzing crucial evidence. These findings not only contribute to the scientific understanding of MTO:Mn4+ phosphor materials but also pave the way for their practical application in cutting-edge technologies.

Synthesis, characterization, and biological activity of a fresh class of sonochemically synthesized Cu2+ complexes.

The synthesis and characterization of metal complexes have garnered significant attention due to their versatile applications in scientific and biomedical fields. In this research, two novel copper (Cu) complexes, [Cu(L)(L')(H2O)2] (1) and [Cu(L)(Im)H2O] (2), where L = pyridine-2,6-dicarboxylic acid, L' = 2,4-diamino-6-hydroxypyrimidine, and Im = imidazole, were investigated concerning their sonochemical synthesis, spectroscopic analysis, and biological activity. The complexes' structural characterization was achieved using analytical techniques, including single-crystal X-ray structure determination, FTIR, PXRD, TGA and DTA, SEM, TEM, and EDS. Complex (1) displayed a six-coordinated Cu2+ ion, while complex (2) exhibited a five-coordinated Cu2+ ion. The crystal structures revealed monoclinic (C2/c) and triclinic (P-1) space groups, respectively. Both complexes showcased zero-dimensional (0D) supramolecular networks, primarily driven by hydrogen bonding and π-π stacking interactions, which played pivotal roles in stabilizing the structures and shaping the unique supramolecular architecture. Both complexes demonstrated significant antioxidant activity, suggesting their capability to neutralize free radicals and mitigate oxidative stress-related diseases. Hemolysis percentages were less than 2%, per the ASTM F756-00 standard, indicating non-hemolytic behavior. Low cytotoxicity was observed against fibroblast and MCF-7 cell lines. They do not exhibit antibacterial activity against Escherichia coli and Staphylococcus aureus. These findings suggest that the synthesized Cu2+‒complexes hold considerable promise for applications in drug delivery and cancer treatment. This research contributes to the advancement of supramolecular chemistry and the development of multifunctional materials for diverse scientific and medical applications.

Automated Mixture Analysis via Structural Evaluation.

The determination of chemical mixture components is vital to a multitude of scientific fields. Oftentimes spectroscopic methods are employed to decipher the composition of these mixtures. However, the sheer density of spectral features present in spectroscopic databases can make unambiguous assignment to individual species challenging. Yet, components of a mixture are commonly chemically related due to environmental processes or shared precursor molecules. Therefore, analysis of the chemical relevance of a molecule is important when determining which species are present in a mixture. In this paper, we combine machine-learning molecular embedding methods with a graph-based ranking system to determine the likelihood of a molecule being present in a mixture based on the other known species and/or chemical priors. By incorporating this metric in a rotational spectroscopy mixture analysis algorithm, we demonstrate that the mixture components can be identified with extremely high accuracy (≥97%) in an efficient manner.

Unveiling novelty evolution in the field of library and information science in China

Purpose This study aims to analyze the distribution of novelty among scholarly papers in the field of library and information science (LIS) in China. Specifically, this study explores the distribution of novelty of papers in various journals, research topics and different periods. It is possible to understand the characteristics of LIS research in China and what factors have influenced it. Design/methodology/approach This paper collects articles published in Chinese library science journals indexed by the Chinese Social Sciences Citation Index from 2000 to 2022. The BERTopic model is used based on abstracts of the papers and to obtain the topic of each paper. Based on the combination innovation theory of reference pairs cited by focal papers, novelty scores of all papers are calculated. Next, this paper analyzes the novelty of papers under different topics. Finally, this paper analyzes the differences in author collaboration patterns across various topics, aiming to explain how these differences relate to the novelty of papers from a collaborative perspective. Findings This study shows that archival research topics have lower novelty than papers on journal evaluation and patent technology in Chinese LIS. Research papers in this field are gradually becoming more novel over time. Papers on different topics and with varying degrees of novelty exhibit distinct author collaboration patterns, with low-novelty topics more frequently featuring solo authorship, while high-novelty topics tend to involve a higher percentage of inter-institutional collaboration. Originality/value This study investigates the novelty characteristics of research papers on different topics in the field of LIS in China. The authors’ contribution includes visualizing research hotspots and trends in the field and analyzing authors’ collaboration patterns at the level of research topics, thereby providing new perspectives on the factors affecting the novelty of these papers.

Organizational and legal principles of policy formation in the field of science and scientific and technical activity in the system of the Ministry of Defense of Ukraine

Organizational and legal principles of policy formation in the field of science and scientific and technical activity in the system of the Ministry of Defense of Ukraine

An investigation into the unveiling of information-seeking anxiety among agriculture university library users

Purpose This study aims to explore information-seeking anxiety (ISA) among undergraduate, postgraduate and PhD students at Dr Babasaheb Sawant Konkan Krishi Vidyapeeth, located in Dapoli, Distt. Ratnagiri, Maharashtra. The research focuses on evaluating the intensity of ISA, understanding its underlying causes and identifying potential strategies to reduce its effects, particularly within the context of an agricultural university. Design/methodology/approach This research intends to examine Information seeking anxiety (ISA) among undergraduate, postgraduate and PhD students at Dr Babasaheb Sawant Konkan Krishi Vidyapeeth located in Dapoli District, Ratnagiri, Maharashtra. This research intends to examine and investigate the intensity of ISA among students at an agriculture university, its root causes and possible methods to reduce its effects. The research adopts quantitative surveys with qualitative secondary sources to achieve a comprehensive understanding of ISA among students. A standardized scale was used as the primary data collection tool to assess participants’ levels of ISA. A total of 122 students participated in the study, completing the survey and providing valuable data for analysis. Findings The findings reveal a range of ISA levels among students, indicating the presence of varied information skills and abilities. This underscores the need for well-structured information literacy programs tailored to address these disparities. A significant gender-based difference in ISA was observed, with female students exhibiting lower anxiety levels in relation to “integrated elements 3” at a significance level of .05 (t = 1.327). Interestingly, the study found that the students' rural or urban backgrounds did not significantly impact their ISA levels. Additionally, various integrated elements within the information seeking anxiety scale were found to be interconnected, contributing to the overall anxiety experienced by students. Research limitations/implications The study highlights the importance of designing and implementing targeted interventions to address ISA in academic settings. These findings suggest that tailored information literacy programs could be effective in reducing ISA among students. Future research could build on these results by examining ISA across different disciplines and institutions, thereby contributing to the development of broader, more generalized strategies for mitigating ISA in diverse educational contexts. Individuals may not accurately recall or report their anxiety levels, potentially affecting the study’s findings. This study’s findings may not be generalizable to all agriculture university users due to factors such as the specific university setting, student demographics and agricultural disciplines represented. Practical implications This study provides actionable insights for academic institutions, particularly agricultural universities, in developing targeted information literacy programs. These programs can be designed to address varying levels of ISA among students, enhancing their ability to effectively seek and use information. Additionally, understanding the gender differences in ISA can guide the creation of more inclusive resources and support systems, ultimately improving the overall academic experience and reducing anxiety-related barriers to learning. Social implications The research highlights the broader societal impact of ISA, particularly how it can affect students’ academic success and overall well-being. By addressing ISA through tailored interventions, institutions can contribute to reducing educational inequalities, especially in rural and underrepresented communities. This can foster a more equitable learning environment where all students, regardless of their background, have the opportunity to thrive academically and socially. Originality/value This study contributes to the relatively underexplored area of ISA within the context of agricultural universities. By combining quantitative and qualitative approaches, the research offers a comprehensive understanding of ISA, its causes and potential solutions. The findings provide new insights into how gender and integrated elements of ISA influence anxiety levels, offering valuable contributions to the field of library and information science, particularly in specialized educational settings like agricultural universities.

Mechanism of Fe-rich phase supercooling induced by CDS process to regulating microstructure and strengthen properties of Al-Si-Fe alloy

Excessive iron in aluminum alloys forms compounds that weaken their strength and workability. Therefore, controlling the morphology of the Fe-rich phase is vital for developing strong, high-quality aluminum alloys. This research explores the controlled diffusion solidification (CDS) process, an innovative approach designed to optimize the morphology and distribution of the Fe-rich phase, thereby enhancing the alloy properties beyond what conventional methods can achieve. Specifically, the CDS process uniquely modifies the solidification behavior of the alloy, suppressing the growth of the primary Fe-rich phase and enhancing the overall properties. This demonstrating the ability of process to refine microstructural characteristics effectively. The experimental results confirm the presence of primary Si and Al3Fe in the CDS process samples. Consequently, the nucleation of Al3Fe in the precursor alloy leads to the concentration of Fe solutes reduce, impeding the nucleation and growth of the primary Fe-rich phase during the subsequent solidification process. The key factors are temperature difference between two mixed alloy and mass ratio of precursor alloys. The mass ratio influences the precipitation sequence of the precursor alloys, while the difference of temperature and concentration induces the undercooling necessary for nucleation. The study reveals that an optimal mass ratio of 4:1 in the CDS process achieves the best mechanical properties with a 73.78 % increase in ultimate tensile strength (UTS) and a 107.77 % increase in elongation (UL) compared to conventional casting. The findings provide a theoretical basis for controlling the Fe-rich phase evolution, significantly augmenting the performance of Al-Si-Fe alloys. The findings not only enhancing the optimizing fundamental reactions in the CDS process for other alloy systems but also enriching the broader field of materials science.

Convective assembly of silica colloidal particles inside photonic integrated chip-based microfluidic systems for gas sensing applications.

Optofluidics is a new field of modern science that stands at the interface of microfluidics and photonics and has good prospects for application in gas sensors. Microfluidics offers a promising platform for tuning and assembling monolayer structures that are used as sensitive layers for gas detection. Herein, we evaluate the concept of monolayer formation on a silicon nitride substrate enabling a surface coverage up to 59% through a microfluidic convective assembly and couple it with a photonic integrated chip to probe gas sensing performance.

Recent progress of cataluminescence sensing based on gas-solid interfaces.

Cataluminescence (CTL) has emerged as a sensing transduction principle of gas-solid interface for constructing gas sensors that present fast response, high sensitivity, and online monitoring. It has thus been widely associated with the field of chemical analysis and catalytic science. Herein, the latest developments in CTL sensors are reviewed, and the status quo of CTL-based gas sensing systems is discussed. In particular, the basic principles and sensing systems of CTL are outlined, including performance enhancement strategies for specific targets and recognition methods for multiple targets. Moreover, the important applications of CTL sensors are listed and classified, including environmental pollutant monitoring, product quality control, clinical diagnosis, and evaluation of catalyst performance. Finally, based on abundant case reports, the current conundrums of CTL sensors are summarized and their future development trends are also put forward.

Quaternion-Aware Low-Rank Prior for Blind Color Image Deblurring

Blind image deblurring is a critical and challenging task in the field of imaging science due to its severe ill-posedness. Appropriate prior information and regularizations are normally introduced to alleviate this problem. Inspired by the fact that the matrix representing a natural image is intrinsically low-rank or approximately low-rank, we employ the low-rank matrix approximation (LRMA) approach for tackling blind image deblurring problems with unknown kernels. When applied to color image restoration tasks, making use of the quaternion representation in the hypercomplex domain enables us to better illustrate the inner relationships among color channels and thus more accurately characterize color image structure. Following this idea, we develop a novel model for color image blind deblurring by implementing the quaternion representation to the LRMA method. This proposed model facilitates better results for blur kernel estimation through preserving the sharper color intermediate latent image, which is first implemented for addressing the blind color image deblurring problem. Extensive numerical experiments demonstrate that our proposed quaternion-aware low-rank prior model greatly improves the performance when compared with the conventional low-rank based scheme and outperforms some of the state-of-the-art methods in terms of some criteria and visual quality.

When we talk about Big Data, What do we really mean? Toward a more precise definition of Big Data

Despite the lack of consensus on an official definition of Big Data, research and studies have continued to progress based on this “no consensus” stance over the years. However, the lack of a clear definition and scope for Big Data results in scientific research and communication lacking a common ground. Even with the popular “V” characteristics, Big Data remains elusive. The term is broad and is used differently in research, often referring to entirely different concepts, which is rarely stated explicitly in papers. While many studies and reviews attempt to draw a comprehensive understanding of Big Data, there has been little systematic research on the position and practical implications of the term Big Data in research environments. To address this gap, this paper presents a Systematic Literature Review (SLR) on secondary studies to provide a comprehensive overview of how Big Data is used and understood across different scientific domains. Our objective was to monitor the application of the Big Data concept in science, identify which technologies are prevalent in which fields, and investigate the discrepancies between the theoretical understanding and practical usage of the term. Our study found that various Big Data technologies are being used in different scientific fields, including machine learning algorithms, distributed computing frameworks, and other tools. These manifestations of Big Data can be classified into four major categories: abstract concepts, large datasets, machine learning techniques, and the Big Data ecosystem. This study revealed that despite the general agreement on the “V” characteristics, researchers in different scientific fields have varied implicit understandings of Big Data. These implicit understandings significantly influence the content and discussions of studies involving Big Data, although they are often not explicitly stated. We call for a clearer articulation of the meaning of Big Data in research to facilitate smoother scientific communication.

Culture of care: the question of animal agency in laboratory animal science

BackgroundA majority of the current debates in experimental animal science research focus to a large extent on the significance and implementation of the 3Rs principle according to Russell and Burch. In this context, not least due to the EU Directive 2010/63/EU, the concept of a culture of care has become more prevalent. Although animals are essential actors in the field of laboratory science, the discussion around animal agency, as well as the resulting consequences for laboratory animal science, is currently unconsidered.MethodsThe purpose of this qualitative survey was to identify the perception and understanding of professional workers in laboratory animal science regarding the culture of care in general and aspects of animal agency in particular. Using a non-standardized qualitative survey method (topic-oriented, guideline-based expert interviews), persons involved in animal experimentation in different groups (management, science, regulation, and care) were interviewed.ResultsOverall, the results of the qualitative survey showed that animal agency plays a subordinate role in the question of a culture of care in animal research. Although not all groups explicitly applied the construct of animal agency or comparable terminology for this, there were links to the theoretical construct. Overall, the interviews showed a recognized network between humans and animals and that animals can interact dialogically in research. This is justified, for example, by the transfer of emotions from carers or scientists to animals. Nevertheless, a differentiated reflection of an animal’s agency remains disregarded.ConclusionThe present qualitative survey approached the understanding of a culture of care among experts in the field of animal research. Animal agency does occur in the theoretical reception of the culture of care model. However, it is not conclusively established in everyday practice. Rather, the results lead to the assumption that strategies are being implemented to largely fade out animal agency.

Prompt Engineering Paradigms for Medical Applications: Scoping Review.

Prompt engineering, focusing on crafting effective prompts to large language models (LLMs), has garnered attention for its capabilities at harnessing the potential of LLMs. This is even more crucial in the medical domain due to its specialized terminology and language technicity. Clinical natural language processing applications must navigate complex language and ensure privacy compliance. Prompt engineering offers a novel approach by designing tailored prompts to guide models in exploiting clinically relevant information from complex medical texts. Despite its promise, the efficacy of prompt engineering in the medical domain remains to be fully explored. The aim of the study is to review research efforts and technical approaches in prompt engineering for medical applications as well as provide an overview of opportunities and challenges for clinical practice. Databases indexing the fields of medicine, computer science, and medical informatics were queried in order to identify relevant published papers. Since prompt engineering is an emerging field, preprint databases were also considered. Multiple data were extracted, such as the prompt paradigm, the involved LLMs, the languages of the study, the domain of the topic, the baselines, and several learning, design, and architecture strategies specific to prompt engineering. We include studies that apply prompt engineering-based methods to the medical domain, published between 2022 and 2024, and covering multiple prompt paradigms such as prompt learning (PL), prompt tuning (PT), and prompt design (PD). We included 114 recent prompt engineering studies. Among the 3 prompt paradigms, we have observed that PD is the most prevalent (78 papers). In 12 papers, PD, PL, and PT terms were used interchangeably. While ChatGPT is the most commonly used LLM, we have identified 7 studies using this LLM on a sensitive clinical data set. Chain-of-thought, present in 17 studies, emerges as the most frequent PD technique. While PL and PT papers typically provide a baseline for evaluating prompt-based approaches, 61% (48/78) of the PD studies do not report any nonprompt-related baseline. Finally, we individually examine each of the key prompt engineering-specific information reported across papers and find that many studies neglect to explicitly mention them, posing a challenge for advancing prompt engineering research. In addition to reporting on trends and the scientific landscape of prompt engineering, we provide reporting guidelines for future studies to help advance research in the medical field. We also disclose tables and figures summarizing medical prompt engineering papers available and hope that future contributions will leverage these existing works to better advance the field.

Immuno-Rolling Circle Amplification (Immuno-RCA): Biosensing Strategies, Practical Applications, and Future Perspectives.

In the rapidly evolving field of life sciences and biomedicine, detecting low-abundance biomolecules, and ultraweak biosignals presents significant challenges. This has spurred a rapid development of analytical techniques aiming for increased sensitivity and specificity. These advancements, including signal amplification strategies and the integration of biorecognition events, mark a transformative era in bioanalytical precision and accuracy. A prominent method among these innovations is immuno-rolling circle amplification (immuno-RCA) technology, which effectively combines immunoassays with signal amplification via RCA. This process starts when a targeted biomolecule, such as a protein or cell, binds to an immobilized antibody or probe on a substrate. The introduction of a circular DNA template triggers RCA, leading to exponential amplification and significantly enhanced signal intensity, thus the target molecule is detectable and quantifiable even at the single-molecule level. This review provides an overview of the biosensing strategy and extensive practical applications of immuno-RCA in detecting biomarkers. Furthermore, it scrutinizes the limitations inherent to these sensors and sets forth expectations for their future trajectory. This review serves as a valuable reference for advancing immuno-RCA in various domains, such as diagnostics, biomarker discovery, and molecular imaging.

Digitizing DNA Sequences Using Multiset-Based Nucleotide Frequencies for Machine Learning-Based Mutation Detection

Investigating algebraic structures in a non-conventional framework supplements mathematics for hard-nosed practical applications to the fields of theoretical biology and computer science. One such algebraic structure is multigroup whose underlying set is a multiset. The genome is the entire set of DNA instructions found within a cell which contains all the information needed for an individual to develop and function. DNA and RNA are the hereditary materials that play a vital role in the metabolism process of living things, especially protein synthesis. In genomic database DNA sequences are stored in the form of string or text data types. The only data that works with machine learning algorithms is numerical. Thus, it is necessary to transform DNA sequence strings to numerical values. This article is organized in the following manner. Firstly, we prove that standard genetic code is a multigroup and genome architecture of the whole population can be interpreted as the sum of multisets. Next, it is described how a numerical representation of DNA bases relates to its algebraic representation. We further employed Gated Recurrent Unit, Long Short-Term Memory, and Bidirectional Long Short-Term Memory to identify changes between the DNA sequences. Experimental results show that GRU with multiset-based numerical values for DNA bases offers 98% accuracy on testing data. This novel technique will aid in the detection of mutations in complex diseases.