Field Of Science Research Articles

Biocompatibility, Antimicrobial and Wound Healing Activities of Lavandula dentata Leaves Extract

Background: The human skin is a natural barrier that protects against external stress, but its physiological structure can be compromised when it gets damaged. In the field of medical science, wounds are a significant issue that requires immediate attention. Lavandula species are used in the food, perfume and cosmetics industries due to their antimicrobial, anti-inflammatory and wound-healing properties. Methods: The present study was done to assess the biocompatibility, antimicrobial and wound-healing potential of L. dentata leave methanolic extract (LE) that is cultivated in the Taif region of Saudi Arabia. The biocompatibility of LE was analyzed against the normal human skin fibroblast (HSF) cell line using SRB assay. A quarter of LE IC50 was used for wound healing assay. Finally, Escherichia Coli, Staphylococcus aureus, Pseudomonas aeruginosa and Streptococcus pyogenes were used to determine LE antibacterial potential through the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) measurement. Result: LE appears safe on HSF cell line with IC50 = 97 mg/mL. LE decreased wound width, wound area and the mean cell migratory rate of wound border cells during scratch closure, while increased the wound closure% in a time-dependent manner compared to negative control. LE shows antimicrobial potential against all four strains at MIC and MBC greater than 3.0 mg/mL, except for Streptococcus pyogenes MIC= 3.0 mg/mL. In conclusion, LE offers therapeutic benefits against wound healing with an antimicrobial effect, however, higher concentrations of LE should be used to assess its effect.

Biodynamers: applications of dynamic covalent chemistry in single-chain polymer nanoparticles.

Dynamic Covalent Chemistry (DCC) enables the development of responsive molecular systems through the integration of reversible bonds at the molecular level. These systems are thermodynamically stable and capable of undergoing various molecular assemblies and transformations, allowing them to adapt to changes in environmental conditions like temperature and pH. Introducing DCC into the field of polymer science has led to the design of Single-Chain Nanoparticles (SCNPs), which are formed by self-folding via intramolecular crosslinking mechanisms. Defined by their adaptability, SCNPs mimic biopolymers in size and functionality. Biodynamers, a subclass of SCNPs, are specifically designed for their stimuli-responsive and tunable, dynamic properties. Mimicking complex biological structures, their scope of application includes target-specific and pH-responsive drug delivery, enhanced cellular uptake and endosomal escape. In this manuscript, we discuss the integration of DCC for the design of SCNPs, focusing particularly on the characteristics of biodynamers and their biomedical and pharmaceutical applications. By underlining their potential, we highlight the factors driving the growing interest in SCNPs, providing an overview of recent developments and future perspectives in this research field.

Design considerations for developing measures of policy implementation in quantitative evaluations of public health policy

Typical quantitative evaluations of public policies treat policies as a binary condition, without further attention to how policies are implemented. However, policy implementation plays an important role in how the policy impacts behavioral and health outcomes. The field of policy-focused implementation science is beginning to consider how policy implementation may be conceptualized in quantitative analyses (e.g., as a mediator or moderator), but less work has considered how to measure policy implementation for inclusion in quantitative work. To help address this gap, we discuss four design considerations for researchers interested in developing or identifying measures of policy implementation using three independent NIH-funded research projects studying e-cigarette, food, and mental health policies. Mini case studies of these considerations were developed via group discussions; we used the implementation research logic model to structure our discussions. Design considerations include (1) clearly specifying the implementation logic of the policy under study, (2) developing an interdisciplinary team consisting of policy practitioners and researchers with expertise in quantitative methods, public policy and law, implementation science, and subject matter knowledge, (3) using mixed methods to identify, measure, and analyze relevant policy implementation determinants and processes, and (4) building flexibility into project timelines to manage delays and challenges due to the real-world nature of policy. By applying these considerations in their own work, researchers can better identify or develop measures of policy implementation that fit their needs. The experiences of the three projects highlighted in this paper reinforce the need for high-quality and transferrable measures of policy implementation, an area where collaboration between implementation scientists and policy experts could be particularly fruitful. These measurement practices provide a foundation for the field to build on as attention to incorporating measures of policy implementation into quantitative evaluations grows and will help ensure that researchers are developing a more complete understanding of how policies impact health outcomes.

Unsupervised method for detecting surface defects in steel based on joint optimization of pseudo-labeling and clustering

Abstract Advances in the field of measurement science and technology have improved the detection of defects in industrial production. One of the key challenges in steel plate surface defect detection is the need to quickly detect a small number of defects in an overwhelmingly defect-free sample. Unlike supervised learning, which relies heavily on precise sample labeling, unsupervised learning leverages its inherent learning capabilities for detection. This paper introduces an innovative method for smart steel diagnosis, integrating joint optimization of feature extraction and clustering. The proposed approach merges mini-batch K-Means clustering with a feature extraction network to acquire pseudo-label information for current images. It employs a multi-view transformation strategy, enabling classification through the optimized feedback from pseudo-labels. This method allows the network to self-optimize the distinction of image features through backpropagation. The method exhibits a mere 4% classification failure rate for steel surface images. This significant reduction in additional data processing requirements enhances the inspection system's efficiency and accuracy. Furthermore, the versatility of this method extends beyond steel defect diagnosis. It holds potential for application in various engineering domains, particularly in scenarios characterized by data imbalance.

Applications of deep learning in physical oceanography: a comprehensive review

Deep learning, a data-driven technology, has attracted widespread attention from various disciplines due to the rapid advancements in the Internet of Things (IoT) big data, machine learning algorithms and computational hardware in recent years. It proves to achieve comparable or even more accurate results than traditional methods in a more flexible manner in existing applications in various fields. In the field of physical oceanography, an important scientific field of oceanography, the abundance of ocean surface data and high dynamic complexity pave the way for an extensive application of deep learning. Moreover, researchers have already conducted a great deal of work to innovate traditional approaches in ocean circulation, ocean dynamics, ocean climate, ocean remote sensing and ocean geophysics, leading oceanographic studies into the “AI ocean era”. In our study, we categorize numerous research topics in physical oceanography into four aspects: surface elements, subsurface elements, typical ocean phenomena, and typical weather and climate phenomena. We review the cutting-edge applications of deep learning in physical oceanography over the past three years to provide comprehensive insights into its development. From the perspective of three application scenarios, namely spatial data, temporal data and data generation, three corresponding deep learning model types are introduced, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs) and generative adversarial networks (GANs), and also their principal application tasks. Furthermore, this study discusses the current bottlenecks and future innovative prospects of deep learning in oceanography. Through summarizing and analyzing the existing research, our aim is to delve into the potential and challenges of deep learning in physical oceanography, providing reference and inspiration for researchers in future oceanographic studies.

Women Emancipation in Indonesia: A Psychological desire to choose the field of physics education at university level

The gender bias that places women as incapable people ultimately reduces their psychological drive to engage with the field of science, especially physics. This is in stark contrast to the prevailing attitudes at universities in Indonesia. This then became the basis for the aim of this research to explore whether women's interest in studying physics education was based on psychological factors in themselves by comparing them with men. The research was conducted using quantitative survey methods, and a sampling process using random sampling was carried out on the population of a university in Indonesia. The sample was divided into two groups based on gender: 77 women and 23 men. The data collection instrument was developed using the grounded theory of planned behavior (TPB) and social cognitive career theory (SCCT), which identified four main psychological factors: identity, interest, self-concept, and self-efficacy. The results of the descriptive and inferential data analysis, conducted using one-way MANOVA, indicate that the psychological factors of identity, interest, the self-concept, and self-efficacy for women are significantly higher than those for men. Furthermore, the Pearson analysis demonstrated a significantly strong positive correlation between the psychological factors tested

The Application of Artificial Intelligence (AI) in Library and Information Centre

Artificial intelligence has taken over many industries and is seen as a continuation of human intelligence. Artificial intelligence applications in libraries have revolutionized the information industry. Additionally, it has given modern libraries' development fresh life. It is believed that integrating artificial intelligence into library operations will open up new internet resources for libraries. Virtual reality, which engages users with libraries and improves information literacy abilities, is one of the valid innovations that librarians are constantly utilizing to engage and expand services for their patrons. It wouldn't be incorrect to argue that the development of the computer accelerated the process of digitization, much as the discovery of the wheel ushered in the mechanical age of human existence. Humans are the only animals with the innate ability to think for themselves. With the power of independent thought, humans have created a great deal of innovative technologies. One example of them is the development of the computer. The most significant development in computer technology that humans have made with the use of their intelligence is artificial intelligence. The goal of the computer science field of artificial intelligence is to build computers with human-like intelligence. Almost everywhere that computers are used, artificial intelligence is now being deployed. The need for this is growing daily, namely in the areas of science, health, automobiles, engineering, climates, business, pharmaceuticals, and academic libraries. AI must be used in libraries for both technical and library services purposes. The application of AI will expedite and improve the quality of work done in libraries, allowing them to offer a greater number of services with fewer staff members. KEYWORDS: Artificial Intelligence, Big Data, Internet of Things, Smart Library.

STEAM: Momentous Approach of Teaching Learning

Globally, learning approaches that mingle various scientific fields are in great need today in education form to improve the product of education. The International Society for Technology in Education (ISTE) requires institutional education to acclimatize to rapid technology and train students to compete internationally by using e-learning medium in the classroom. The purpose of the study is to support every argument about STEAM education. The method used is previous studies based on STEAM. Data is collected through articles based on STEAM, STEAM education, and learning models based on STEAM. Databases used for searching include, Elsevier, Emerald, Springer, Research Gate, Academia Edu, Google Scholar. The result of the study is that STEAM builds human assets who can think critically, logically, and systematically and have universal competitiveness. Implementing STEAM based pedagogical learning in education enables students to comprehend the importance of integrating various disciplines. Moreover, learners can progress their logical thinking through STEAM approach, it will provide inclusive opportunities for students to practice their thinking skill, experiential learning.

The conceptualisation and application of co-occupation in occupational therapy and occupational science across various situations: A scoping review protocol

ABSTRACT Background The concept of co-occupation captures the transactional nature of the occupations people perform together. Although the concept has wide application possibilities, it has mainly been used to describe occupations between parents and young children. Objective To provide an overview of the existing knowledge and gap in knowledge about how the concept of co-occupation is conceptualised and applied in the field of occupational science and in the occupational therapy profession. Method The proposed scoping review will follow the Joanna Briggs Institute – JBI methodology. The key information sources to be searched are CINAHL (EBSCO), MEDLINE (PubMed), Google Scholar, PsycInfo, Open science – OSF, PROSPERO, Opengrey.eu and Greylit.org. Searches will be limited to Danish, Swedish, Norwegian, English, and German languages. All studies will be assessed from the inclusion and exclusion criteria described in the protocol; and will include any study where co-occupation is used in any context of occupational science or occupational therapy across various situations. Conclusion The envisioned impact of the completed review will be a nuanced description of the concept of co-occupation and its application, thereby bringing forth a current comprehension of the concept of co-occupation for research and application.

A global perspective on social stratification in science

To study stratification among scientists, we reconstruct the career-long trajectories of 8.2 million scientists worldwide using 12 bibliometric measures of productivity, geographical mobility, collaboration, and research impact. While most previous studies examined these variables in isolation, we study their relationships using Multiple Correspondence and Cluster Analysis. We group authors according to their bibliometric performance and academic age across six macro fields of science, and analyze co-authorship networks and detect collaboration communities of different sizes. We found a stratified structure in terms of academic age and bibliometric classes, with a small top class and large middle and bottom classes in all collaboration communities. Results are robust to community detection algorithms used and do not depend on authors’ gender. These results imply that increased productivity, impact, and collaboration are driven by a relatively small group that accounts for a large share of academic outputs, i.e., the top class. Mobility indicators are the only exception with bottom classes contributing similar or larger shares. We also show that those at the top succeed by collaborating with various authors from other classes and age groups. Nevertheless, they are benefiting disproportionately from these collaborations which may have implications for persisting stratification in academia.

The development of mathematics expectancy-value profiles during the secondary–tertiary transition into STEM fields

BackgroundTo master the secondary–tertiary transition into fields of science, technology, engineering, and mathematics (STEM), academic self-beliefs play a pivotal role, especially those related to learning mathematics. The framework of expectancy-value theory has been used widely in primary and secondary education and partly in tertiary education to assess the self-beliefs of students in terms of expectancy of success and perceived value of mathematics. Based on this framework, we measured how the intrinsic value, the attainment value, the utility value, and the cost of learning mathematics as well as the expectancy of success when learning mathematics developed during the secondary–tertiary transition of students into STEM fields. Data were collected in a quantitative repeated-measures questionnaire study with two measurement points (measurement point 1: n = 710, measurement point 2: n = 487, listwise: n = 409). We conducted a latent profile analysis to identify the prevalent patterns of mathematics self-beliefs, called profiles, at each of the two measurement points. We studied the relation of these profiles to prior education, achievement at school, and achievement at university. By performing a latent transition analysis, we determined the probabilities of transitioning from the initial profiles to the posterior profiles.ResultsOur analysis revealed four distinct prevalent profiles at each measurement point, ranging from highly favorable (i.e., high expectancy, high value, low cost) to highly unfavorable with respect to learning mathematics. The profiles with favorable manifestations remained stable over time, while those with undesirable manifestations deteriorated further. We observed a sharp increase in cost across all profiles. Prior achievement correlated strongly with profile membership.ConclusionsThe expenditure of time and energy increased sharply during the secondary–tertiary transition, independently of the students’ initial motivational patterns. The perceived utility of mathematics for potential future careers was shown to be a significant source of motivation. The role of mathematics in future careers should thus be made visible in university teaching. Keeping the detrimental development of initially undesirable motivational profiles in mind, university teachers should create ample opportunities for students to gain a sense of accomplishment.

Dueling cartographies: science, positivism, art, and critical praxis

ABSTRACT Since the first uses of the term in the 19th century, cartography has grown into a sprawling, contested discipline and practice. Today, cartography has been described both as dying and as one of the best jobs to pursue in the 21st century. In this article, we deconstruct this dichotomy by describing three common axes along which cartography has been bifurcated -art and science, critical and post-positivist approaches, and research and practice. These dualisms serve as a frame to better understand what cartography has become and how it is no longer necessarily what self-described cartographers do. In response, we argue that, with the rise of prominent humanistic and affective approaches to geovisualization and the burgeoning field of data science and analytics emerging predominantly outside of traditional cartographic praxis and scholarship, what it means to take cartography seriously across these divides provides an inclusive, unique path forward for both the discipline and practitioners. Using examples from recent work, we lay out potential work that prioritizes ideas from nonrepresentational theory as a framework through which cartographers might productively resituate the field.

Cross-corpus speech emotion recognition with transformers: Leveraging handcrafted features and data augmentation

Speech emotion recognition (SER) stands as a prominent and dynamic research field in data science due to its extensive application in various domains such as psychological assessment, mobile services, and computer games, mobile services. In previous research, numerous studies utilized manually engineered features for emotion classification, resulting in commendable accuracy. However, these features tend to underperform in complex scenarios, leading to reduced classification accuracy. These scenarios include: 1. Datasets that contain diverse speech patterns, dialects, accents, or variations in emotional expressions. 2. Data with background noise. 3. Scenarios where the distribution of emotions varies significantly across datasets can be challenging. 4. Combining datasets from different sources introduce complexities due to variations in recording conditions, data quality, and emotional expressions. Consequently, there is a need to improve the classification performance of SER techniques. To address this, a novel SER framework was introduced in this study. Prior to feature extraction, signal preprocessing and data augmentation methods were applied to augment the available data, resulting in the derivation of 18 informative features from each signal. The discriminative feature set was obtained using feature selection techniques which was then utilized as input for emotion recognition using the SAVEE, RAVDESS, and EMO-DB datasets. Furthermore, this research also implemented a cross-corpus model that incorporated all speech files related to common emotions from three datasets. The experimental outcomes demonstrated the superior performance of SER framework compared to existing frameworks in the field. Notably, the framework presented in this study achieved remarkable accuracy rates across various datasets. Specifically, the proposed model obtained an accuracy of 95%, 94%,97%, and 97% on SAVEE, RAVDESS, EMO-DB and cross-corpus datasets respectively. These results underscore the significant contribution of our proposed framework to the field of SER.

Assessing an integrated team-teaching lecture in medicine and surgery program- Galala University

IntroductionAchieving integration in medical curricula without redundancy in basic medical sciences disciplines is a substantial challenge. Introducing co-teaching in such curricula with active inter-disciplinary participation is believed to best utilize the teaching and learning time for instructors and students, to motivate the students, and to provide a more robust base for bridging the gap between basic and clinical medical sciences in medical schools. Additionally, including more than one student-centered activity in one session is expected to increase the students’ involvement and improve the retention of knowledge. Our study aims at minimizing redundancy and improving the students’ motivation in learning the topic “insulin-glucose regulation” during the Endocrine and Metabolism module taught to year three students at Galala University, Faculty of Medicine in Egypt.MethodsThe authors designed a 3-hr co-teaching integrated session with 3 basic medical sciences aimed to explain the clinical terms including online accessed pre/post-tests, small student groups-created pre/post-session MCQ, with co-sharing of students in the introduction of scientific materials.ResultsThe students’ scores in the post-test showed that they gained more knowledge compared to before. Interestingly, there was only an improvement in the students’ performance in generating questions before and after the session, as well as in the integrated question in the end-of-semester exam, we suggest implementing this approach in other topics and modules in medical schools. It would also be favorable to follow up with the students taught using this approach and those taught differently to assess the effectiveness of this approach in a controlled manner.ConclusionIntegrated sessions effectively increase student awareness of medical concepts and reduce redundancy in basic medical sciences. This approach exposes students to a more comprehensive understanding of the subject matter, improving their comprehension and retention. It is a valuable method for educators and instructors seeking to enhance their students’ learning experience in the field of medical sciences.

Retractions in academic publishing: insights from highly ranked global universities

Purpose The study aims to profile the scientific retractions in the top five global universities and provide descriptive statistics on specific subjects. Design/methodology/approach The data for reasons behind retractions is manually extracted from the Retraction Watch Database. The top five global universities according to the Times Higher Education global ranking of 2024 are selected for this study. Findings The study found that Stanford University emerged with the highest number of retractions in the assessment across institutions in the field of basic life sciences and health sciences. Notably, the predominant reasons for these retractions were identified, with “unreliable results” being the most prevalent, accounting for 53 retractions. Following closely was the category of “errors in results and/or conclusions”, contributing to 51 retractions. MIT has the longest time between publication and retraction of any subject group, with an average of 1,701 days. Research limitations/implications This study has some limitations, as it only analysed the retractions of the top five global universities. Originality/value The study provides a comprehensive analysis of retractions in academic publishing, focusing on reasons, time gaps, article types and accessibility categories across prestigious universities. The paper underscores the critical role of retractions in maintaining the integrity of scientific literature, emphasizing the importance of transparent correction and responsible peer review to ensure the reliability and trustworthiness of published research. Results show that common reasons for retractions include duplication, fake peer review and plagiarism, underlining the need for ethical research standards.

An Alternative Micro-Milling Fabrication Process for Rapid and Low-Cost Microfluidics

Microfluidics is an important technology for the biomedical industry and is often utilised in our daily lives. Recent advances in micro-milling technology have allowed for rapid fabrication of smaller and more complex structures, at lower costs, making it a viable alternative to other fabrication methods. The microfluidic chip fabrication developed in this research is a step-by-step process with a self-contained wet milling chamber. Additionally, ethanol solvent bonding is used to allow microfluidic chips to be fully fabricated within approximately an hour. The effect of using this process is tested with quantitative contact profileometery data to determine the expected surface roughness in the microchannels. The effect of surface roughness on the controllability of microparticles is tested in functional microfluidic chips using image processing to calculate particle velocity. This process can produce high-quality channels when compared with similar studies in the literature and surface roughness affects the control of microparticles. Lastly, we discuss how the outcomes of this research can produce rapid and higher-quality microfluidic devices, leading to improvement in the research and development process within the fields of science that utilise microfluidic technology. Such as medicine, biology, chemistry, ecology, and aerospace.

A method of identifying domain-specific academic user information needs based on academic Q&A communities

Purpose To address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs. Design/methodology/approach This study’s method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science. Findings Experimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories “methods,” “experimental phenomena” and “experimental materials” are relatively high in the materials science field. Originality/value This study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users’ information needs, which in turn facilitates the platform’s academic resource organization and services.

Atomistic structures and stabilities of MgAl2O4 surfaces under processing conditions: A first-principles thermodynamics study

Surfaces, in contacting ambient environments, are of particular importance for controlling sintering, thin-film growth, and catalytic activity of oxides. Magnesium aluminate spinel (MgAl2O4), an essential oxide for both functional and structural ceramics, has attracted considerable attentions across diverse scientific fields. Nonetheless, a central challenge in rationally manipulating the properties of MgAl2O4 lies in the atomistic understanding of its surface structures under processing conditions. This study presents comprehensive first-principles calculations of MgAl2O4 (110) and (111) polar surfaces with/without off-stoichiometries. We predict from a thermodynamic model the dependence of surface stabilities on critical processing parameters, namely the oxygen partial pressure (PO2) and temperature (T). The findings indicate that at typical sintering temperatures, the non-stoichiometric (110) surface transits from Al/Mg-rich to O-rich with increasing PO2. In contrast, the non-stoichiometric (111) surface stably terminates in a Mg-rich configuration at low PO2, gradually shifting to Al-rich and then to Mg/O-rich as PO2 increases. The correlation between surface off-stoichiometry and stability is elucidated through electronic structure analysis. The investigation offers fundamental insights into surface structures and physicochemical behavior of MgAl2O4, thereby facilitating the rational processing control of complex oxides for applications in catalysis and electronics.

Advanced Method Optimization for Sampling and Analysis Instrumentation.

This work presents a generalized approach for analytical method optimization that branches the gap between techniques historically employed and accurate modern optimization techniques suitable for various applications. The novelty of the described strategy is the utilization of multivariate, multiobjective optimization with Karush-Kuhn-Tucker conditions to bound the optimization space to solutions within the physical limitations of instrumentation. Briefly, the basic steps outlined in this paper are to (1) determine the objective(s) that should be maximized or minimized based on the goals of the analytical application, (2) conduct a screening experiment, (3) perform ANOVA to determine the parameters which have a statistically significant effect on the objective, (4) conduct an experiment (e.g., Box-Behnken design) to collect data for fitting the objective equation, and (5) determine the physical constraints of the parameters and solve the Lagrangian to determine the optimal method parameters. A broad approach to optimization target selection allows for robust method tuning to develop improved data sets amenable for chemometrics and machine learning algorithm development. Gas chromatography-mass spectrometry was selected as a use case due to its broad use across scientific fields and time-consuming method development involving numerous parameters. This strategy can reduce the cost of research, improve data quality, and enable the rapid development of new analytical techniques.

Coupling Hyperbolic GCN with Graph Generation for Spatial Community Detection and Dynamic Evolution Analysis

Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning.